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2025-05-15 00:00:00
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|---|---|---|---|---|---|---|---|---|
Christopher Sims
|
Edge Detection and Image Filter algorithms for Spectroscopic Analysis
with Deep Learning Applications
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Edge detection and image filters are commonly used in computer vision.
However, they have never been applied to the data analysis of angle-resolved
photoemission spectroscopy (ARPES) data before in a systematic fashion. In this
paper we will use the Sobel, Laplacian of a gaussian (LoG), Canny, Prewitt,
Roberts, and fuzzy logic methods for edge detection in the ARPES results of
HfP2, ZrSiS, and Hf2Te2P2. We find that the Canny filter is the best method for
edge detection of noisy data that is typical of ARPES measurements, while the
other edge detection techniques are not able to correctly detect ARPES bands.
|
[{'version': 'v1', 'created': 'Mon, 14 Mar 2022 02:31:06 GMT'}]
|
2022-03-15
|
Wei Wang, Loai Danial, Yang Li, Eric Herbelin, Evgeny Pikhay, Yakov
Roizin, Barak Hoffer, Zhongrui Wang, Shahar Kvatinsky
|
A memristive deep belief neural network based on silicon synapses
|
Nature Electronics, 5, 870 (2022)
|
10.1038/s41928-022-00878-9
| null |
physics.app-ph cond-mat.dis-nn cond-mat.mtrl-sci cs.ET
|
Memristor-based neuromorphic computing could overcome the limitations of
traditional von Neumann computing architectures -- in which data are shuffled
between separate memory and processing units -- and improve the performance of
deep neural networks. However, this will require accurate synaptic-like device
performance, and memristors typically suffer from poor yield and a limited
number of reliable conductance states. Here we report floating gate memristive
synaptic devices that are fabricated in a commercial complementary
metal-oxide-semiconductor (CMOS) process. These silicon synapses offer analogue
tunability, high endurance, long retention times, predictable cycling
degradation, moderate device-to-device variations, and high yield. They also
provide two orders of magnitude higher energy efficiency for
multiply-accumulate operations than graphics processing units. We use two
12-by-8 arrays of the memristive devices for in-situ training of a 19-by-8
memristive restricted Boltzmann machine for pattern recognition via a gradient
descent algorithm based on contrastive divergence. We then create a memristive
deep belief neural network consisting of three memristive restricted Boltzmann
machines. We test this on the modified National Institute of Standards and
Technology (MNIST) dataset, demonstrating recognition accuracy up to 97.05%.
|
[{'version': 'v1', 'created': 'Thu, 17 Mar 2022 02:54:55 GMT'}, {'version': 'v2', 'created': 'Fri, 20 May 2022 01:26:46 GMT'}, {'version': 'v3', 'created': 'Fri, 21 Jul 2023 01:26:34 GMT'}]
|
2023-07-24
|
James Chapman, Tim Hsu, Xiao Chen, Tae Wook Heo, Brandon C. Wood
|
Quantifying Disorder One Atom at a Time Using an Interpretable Graph
Neural Network Paradigm
| null |
10.1038/s41467-023-39755-0
| null |
cond-mat.dis-nn cond-mat.mes-hall cond-mat.mtrl-sci
|
Quantifying the level of atomic disorder within materials is critical to
understanding how evolving local structural environments dictate performance
and durability. Here, we leverage graph neural networks to define a physically
interpretable metric for local disorder. This metric encodes the diversity of
the local atomic configurations as a continuous spectrum between the solid and
liquid phases, quantified against a distribution of thermal perturbations. We
apply this novel methodology to three prototypical examples with varying levels
of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures,
and (3) grain boundaries. Using elemental aluminum as a case study, we show how
our paradigm can track the spatio-temporal evolution of interfaces,
incorporating a mathematically defined description of the spatial boundary
between order and disorder. We further show how to extract physics-preserved
gradients from our continuous disorder fields, which may be used to understand
and predict materials performance and failure. Overall, our framework provides
an intuitive and generalizable pathway to quantify the relationship between
complex local atomic structure and coarse-grained materials phenomena.
|
[{'version': 'v1', 'created': 'Fri, 18 Mar 2022 22:12:42 GMT'}, {'version': 'v2', 'created': 'Thu, 16 Jun 2022 19:09:47 GMT'}]
|
2023-08-02
|
Marcin Abram, Keith Burghardt, Greg Ver Steeg, Aram Galstyan, Remi
Dingreville
|
Inferring topological transitions in pattern-forming processes with
self-supervised learning
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cs.CV cs.LG
|
The identification and classification of transitions in topological and
microstructural regimes in pattern-forming processes are critical for
understanding and fabricating microstructurally precise novel materials in many
application domains. Unfortunately, relevant microstructure transitions may
depend on process parameters in subtle and complex ways that are not captured
by the classic theory of phase transition. While supervised machine learning
methods may be useful for identifying transition regimes, they need labels
which require prior knowledge of order parameters or relevant structures
describing these transitions. Motivated by the universality principle for
dynamical systems, we instead use a self-supervised approach to solve the
inverse problem of predicting process parameters from observed microstructures
using neural networks. This approach does not require predefined, labeled data
about the different classes of microstructural patterns or about the target
task of predicting microstructure transitions. We show that the difficulty of
performing the inverse-problem prediction task is related to the goal of
discovering microstructure regimes, because qualitative changes in
microstructural patterns correspond to changes in uncertainty predictions for
our self-supervised problem. We demonstrate the value of our approach by
automatically discovering transitions in microstructural regimes in two
distinct pattern-forming processes: the spinodal decomposition of a two-phase
mixture and the formation of concentration modulations of binary alloys during
physical vapor deposition of thin films. This approach opens a promising path
forward for discovering and understanding unseen or hard-to-discern transition
regimes, and ultimately for controlling complex pattern-forming processes.
|
[{'version': 'v1', 'created': 'Sat, 19 Mar 2022 00:47:50 GMT'}, {'version': 'v2', 'created': 'Wed, 10 Aug 2022 20:11:40 GMT'}]
|
2022-08-12
|
Rongzhi Dong, Yong Zhao, Yuqi Song, Nihang Fu, Sadman Sadeed Omee,
Sourin Dey, Qinyang Li, Lai Wei, Jianjun Hu
|
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from
Materials Composition
| null | null | null |
cond-mat.mtrl-sci
|
One of the long-standing problems in materials science is how to predict a
material's structure and then its properties given only its composition.
Experimental characterization of crystal structures has been widely used for
structure determination, which is however too expensive for high-throughput
screening. At the same time, directly predicting crystal structures from
compositions remains a challenging unsolved problem. Herein we propose a deep
learning algorithm for predicting the XRD spectrum given only the composition
of a material, which can then be used to infer key structural features for
downstream structural analysis such as crystal system or space group
classification or crystal lattice parameter determination or materials property
predictions. Benchmark studies on two datasets show that our DeepXRD algorithm
can achieve good performance for XRD prediction as evaluated over our test
sets. It can thus be used in high-throughput screening in the huge materials
composition space for new materials discovery.
|
[{'version': 'v1', 'created': 'Sun, 27 Mar 2022 15:20:11 GMT'}]
|
2022-03-29
|
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu,
Mohammed Al-Fahdi, Ming Hu, and Jianjun Hu
|
Physics Guided Deep Learning for Generative Design of Crystal Materials
with Symmetry Constraints
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Discovering new materials is a challenging task in materials science crucial
to the progress of human society. Conventional approaches based on experiments
and simulations are labor-intensive or costly with success heavily depending on
experts' heuristic knowledge. Here, we propose a deep learning based Physics
Guided Crystal Generative Model (PGCGM) for efficient crystal material design
with high structural diversity and symmetry. Our model increases the generation
validity by more than 700\% compared to FTCP, one of the latest structure
generators and by more than 45\% compared to our previous CubicGAN model.
Density Functional Theory (DFT) calculations are used to validate the generated
structures with 1,869 materials out of 2,000 are successfully optimized and
deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of
which 39.6\% have negative formation energy and 5.3\% have energy-above-hull
less than 0.25 eV/atom, indicating their thermodynamic stability and potential
synthesizability.
|
[{'version': 'v1', 'created': 'Sun, 27 Mar 2022 17:21:36 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Jul 2022 15:57:26 GMT'}, {'version': 'v3', 'created': 'Tue, 13 Dec 2022 17:01:31 GMT'}]
|
2022-12-14
|
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher,
Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Masao Yano,
Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Thomas
Schrefl
|
Exploring the hysteresis properties of nanocrystalline permanent magnets
using deep learning
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
We demonstrate the use of model order reduction and neural networks for
estimating the hysteresis properties of nanocrystalline permanent magnets from
microstructure. With a data-driven approach, we learn the demagnetization curve
from data-sets created by grain growth and micromagnetic simulations. We show
that the granular structure of a magnet can be encoded within a low-dimensional
latent space. Latent codes are constructed using a variational autoencoder. The
mapping of structure code to hysteresis properties is a multi-target regression
problem. We apply deep neural network and use parameter sharing, in order to
predict anchor points along the demagnetization curves from the magnet's
structure code. The method is applied to study the magnetic properties of
nanocrystalline permanent magnets. We show how new grain structures can be
generated by interpolation between two points in the latent space and how the
magnetic properties of the resulting magnets can be predicted.
|
[{'version': 'v1', 'created': 'Wed, 30 Mar 2022 21:04:09 GMT'}]
|
2022-04-01
|
Koji Shimizu, Ying Dou, Elvis F. Arguelles, Takumi Moriya, Emi
Minamitani, and Satoshi Watanabe
|
Using neural network potential to study point defect properties in
multiple charge states of GaN with nitrogen vacancy
| null | null | null |
cond-mat.mtrl-sci
|
Investigation of charged defects is necessary to understand the properties of
semiconductors. While density functional theory calculations can accurately
describe the relevant physical quantities, these calculations increase the
computational loads substantially, which often limits the application of this
method to large-scale systems. In this study, we propose a new scheme of neural
network potential (NNP) to analyze the point defect behavior in multiple charge
states. The proposed scheme necessitates only minimal modifications to the
conventional scheme. We demonstrated the prediction performance of the proposed
NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0, 1+, 2+,
and 3+. The proposed scheme accurately trained the total energies and atomic
forces for all the charge states. Furthermore, it fairly reproduced the phonon
band structures and thermodynamics properties of the defective structures.
Based on the results of this study, we expect that the proposed scheme can
enable us to study more complicated defective systems and lead to breakthroughs
in novel semiconductor applications.
|
[{'version': 'v1', 'created': 'Thu, 31 Mar 2022 04:33:36 GMT'}]
|
2022-04-01
|
H. Pahlavani, M. Amani, M. Cruz Sald\'ivar, J. Zhou, M. J. Mirzaali,
A. A. Zadpoor
|
Deep learning for the rare-event rational design of 3D printed
multi-material mechanical metamaterials
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Emerging multi-material 3D printing techniques have paved the way for the
rational design of metamaterials with not only complex geometries but also
arbitrary distributions of multiple materials within those geometries. Varying
the spatial distribution of multiple materials gives rise to many interesting
and potentially unique combinations of anisotropic elastic properties. While
the availability of a design approach to cover a large portion of all possible
combinations of elastic properties is interesting in itself, it is even more
important to find the extremely rare designs that lead to highly unusual
combinations of material properties (e.g., double-auxeticity and high elastic
moduli). Here, we used a random distribution of a hard phase and a soft phase
within a regular lattice to study the resulting anisotropic mechanical
properties of the network in general and the abovementioned rare designs in
particular. The primary challenge to take up concerns the huge number of design
parameters and the extreme rarity of such designs. We, therefore, used
computational models and deep learning algorithms to create a mapping from the
space of design parameters to the space of mechanical properties, thereby (i)
reducing the computational time required for evaluating each designand (ii)
making the process of evaluating the different designs highly parallelizable.
Furthermore, we selected ten designs to be fabricated using polyjet
multi-material 3D printing techniques, mechanically tested them, and
characterized their behavior using digital image correlation (DIC, 3 designs)
to validate the accuracy of our computational models. The results of our
simulations show that deep learning-based algorithms can accurately predict the
mechanical properties of the different designs, which match the various
deformation mechanisms observed in the experiments.
|
[{'version': 'v1', 'created': 'Mon, 4 Apr 2022 18:04:23 GMT'}]
|
2022-04-06
|
Qi-Jun Hong
|
Melting temperature prediction via first principles and deep learning
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Melting is a high temperature process that requires extensive sampling of
configuration space, thus making melting temperature prediction computationally
very expensive and challenging. Over the past few years, I have built two
methods to address this challenge, one via direct density functional theory
(DFT) molecular dynamics (MD) simulations and the other via deep learning graph
neural networks. The DFT approach is based on statistical analysis of
small-size solid-liquid coexistence MD simulations. It eliminates the risk of
metastable superheated solid in the fast-heating method, while also
significantly reducing the computer cost relative to the traditional
large-scale coexistence method. Being both accurate and efficient (at the speed
of several days per material), it is considered as one of the best methods for
direct DFT melting temperature calculation. The deep learning method is based
on graph neural networks that effectively handles permutation invariance in
chemical formula, which drastically improves efficiency and reduces cost. At
the speed of milliseconds per material, the model is extremely fast, while
being moderately accurate, especially within the composition space expanded by
the dataset. I have implemented both methods into automated computer code
packages, making them publicly available and free to download. The DFT and deep
learning methods are highly complementary to each other, and hence they can be
potentially well integrated into a framework for melting temperature
prediction. I demonstrated examples of applying the methods to materials design
and discovery of high-melting-point materials.
|
[{'version': 'v1', 'created': 'Sun, 10 Apr 2022 18:17:32 GMT'}]
|
2022-04-12
|
Dillan J. Chang, Colum M. O'Leary, Cong Su, Salman Kahn, Alex Zettl,
Jim Ciston, Peter Ercius and Jianwei Miao
|
Deep Learning Coherent Diffractive Imaging
| null | null | null |
cond-mat.mtrl-sci
|
We report the development of deep learning coherent electron diffractive
imaging at sub-angstrom resolution using convolutional neural networks (CNNs)
trained with only simulated data. We experimentally demonstrate this method by
applying the trained CNNs to directly recover the phase images from electron
diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and
a Au nanoparticle with comparable quality to those reconstructed by a
conventional ptychographic method. Fourier ring correlation between the CNN and
ptychographic images indicates the achievement of a spatial resolution in the
range of 0.70 and 0.55 angstrom (depending on different resolution criteria).
The ability to replace iterative algorithms with CNNs and perform real-time
imaging from coherent diffraction patterns is expected to find broad
applications in the physical and biological sciences.
|
[{'version': 'v1', 'created': 'Mon, 18 Apr 2022 04:05:41 GMT'}]
|
2022-04-19
|
Giovanni Bertoni, Enzo Rotunno, Daan Marsmans, Peter Tiemeijer, Amir
H. Tavabi, Rafal E. Dunin-Borkowski and Vincenzo Grillo
|
Near-real-time diagnosis of electron optical phase aberrations in
scanning transmission electron microscopy using an artificial neural network
|
Ultramicroscopy 2023
|
10.1016/j.ultramic.2022.113663
| null |
physics.ins-det cond-mat.mtrl-sci physics.optics
|
The key to optimizing spatial resolution in a state-of-the-art scanning
transmission electron microscope is the ability to precisely measure and
correct for electron optical aberrations of the probe-forming lenses. Several
diagnostic methods for aberration measurement and correction with maximum
precision and accuracy have been proposed, albeit often at the cost of
relatively long acquisition times. Here, we illustrate how artificial
intelligence can be used to provide near-real-time diagnosis of aberrations
from individual Ronchigrams. The demonstrated speed of aberration measurement
is important as microscope conditions can change rapidly, as well as for the
operation of MEMS-based hardware correction elements that have less intrinsic
stability than conventional electromagnetic lenses.
|
[{'version': 'v1', 'created': 'Sat, 23 Apr 2022 19:07:43 GMT'}]
|
2023-01-04
|
Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya M. D.
Siriwardane, Fanglin Chen, Jianjun Hu
|
Crystal Transformer: Self-learning neural language model for Generative
and Tinkering Design of Materials
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Self-supervised neural language models have recently achieved unprecedented
success, from natural language processing to learning the languages of
biological sequences and organic molecules. These models have demonstrated
superior performance in the generation, structure classification, and
functional predictions for proteins and molecules with learned representations.
However, most of the masking-based pre-trained language models are not designed
for generative design, and their black-box nature makes it difficult to
interpret their design logic. Here we propose BLMM Crystal Transformer, a
neural network based probabilistic generative model for generative and
tinkering design of inorganic materials. Our model is built on the blank
filling language model for text generation and has demonstrated unique
advantages in learning the "materials grammars" together with high-quality
generation, interpretability, and data efficiency. It can generate chemically
valid materials compositions with as high as 89.7\% charge neutrality and
84.8\% balanced electronegativity, which are more than 4 and 8 times higher
compared to a pseudo random sampling baseline. The probabilistic generation
process of BLMM allows it to recommend tinkering operations based on learned
materials chemistry and makes it useful for materials doping. Combined with the
TCSP crysal structure prediction algorithm, We have applied our model to
discover a set of new materials as validated using DFT calculations. Our work
thus brings the unsupervised transformer language models based generative
artificial intelligence to inorganic materials. A user-friendly web app has
been developed for computational materials doping and can be accessed freely at
\url{www.materialsatlas.org/blmtinker}.
|
[{'version': 'v1', 'created': 'Mon, 25 Apr 2022 20:20:26 GMT'}]
|
2022-04-27
|
Jim James, Nathan Pruyne, Tiberiu Stan, Marcus Schwarting, Jiwon Yeom,
Seungbum Hong, Peter Voorhees, Ben Blaiszik, Ian Foster
|
3D Convolutional Neural Networks for Dendrite Segmentation Using
Fine-Tuning and Hyperparameter Optimization
| null | null | null |
cs.CV cond-mat.mtrl-sci eess.IV
|
Dendritic microstructures are ubiquitous in nature and are the primary
solidification morphologies in metallic materials. Techniques such as x-ray
computed tomography (XCT) have provided new insights into dendritic phase
transformation phenomena. However, manual identification of dendritic
morphologies in microscopy data can be both labor intensive and potentially
ambiguous. The analysis of 3D datasets is particularly challenging due to their
large sizes (terabytes) and the presence of artifacts scattered within the
imaged volumes. In this study, we trained 3D convolutional neural networks
(CNNs) to segment 3D datasets. Three CNN architectures were investigated,
including a new 3D version of FCDense. We show that using hyperparameter
optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures
can be trained to outperform the previous state of the art. The 3D U-Net
architecture trained in this study produced the best segmentations according to
quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement
error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and
best segmentations according to visual inspection. The trained 3D CNNs are able
to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus
hastening the progress towards a deeper understanding of phase transformation
phenomena such as dendritic solidification.
|
[{'version': 'v1', 'created': 'Mon, 2 May 2022 19:20:05 GMT'}]
|
2022-05-04
|
Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, and Zachary Ulissi
|
FINETUNA: Fine-tuning Accelerated Molecular Simulations
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci cs.LG
|
Machine learning approaches have the potential to approximate Density
Functional Theory (DFT) for atomistic simulations in a computationally
efficient manner, which could dramatically increase the impact of computational
simulations on real-world problems. However, they are limited by their accuracy
and the cost of generating labeled data. Here, we present an online active
learning framework for accelerating the simulation of atomic systems
efficiently and accurately by incorporating prior physical information learned
by large-scale pre-trained graph neural network models from the Open Catalyst
Project. Accelerating these simulations enables useful data to be generated
more cheaply, allowing better models to be trained and more atomistic systems
to be screened. We also present a method of comparing local optimization
techniques on the basis of both their speed and accuracy. Experiments on 30
benchmark adsorbate-catalyst systems show that our method of transfer learning
to incorporate prior information from pre-trained models accelerates
simulations by reducing the number of DFT calculations by 91%, while meeting an
accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a
technique for leveraging the interactive functionality built in to VASP to
efficiently compute single point calculations within our online active learning
framework without the significant startup costs. This allows VASP to work in
tandem with our framework while requiring 75% fewer self-consistent cycles than
conventional single point calculations. The online active learning
implementation, and examples using the VASP interactive code, are available in
the open source FINETUNA package on Github.
|
[{'version': 'v1', 'created': 'Mon, 2 May 2022 21:36:01 GMT'}, {'version': 'v2', 'created': 'Fri, 1 Jul 2022 18:59:42 GMT'}]
|
2022-07-05
|
Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani
|
Crystal Twins: Self-supervised Learning for Crystalline Material
Property Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training
accurate ML models are elusive and computationally expensive to generate.
Recent advances in Self-Supervised Learning (SSL) frameworks capable of
training ML models on unlabeled data have mitigated this problem and
demonstrated superior performance in computer vision and natural language
processing tasks. Drawing inspiration from the developments in SSL, we
introduce Crystal Twins (CT): an SSL method for crystalline materials property
prediction. Using a large unlabeled dataset, we pre-train a Graph Neural
Network (GNN) by applying the redundancy reduction principle to the graph
latent embeddings of augmented instances obtained from the same crystalline
system. By sharing the pre-trained weights when fine-tuning the GNN for
regression tasks, we significantly improve the performance for 7 challenging
material property prediction benchmarks
|
[{'version': 'v1', 'created': 'Wed, 4 May 2022 05:08:46 GMT'}]
|
2022-05-05
|
B. Burton, W.T. Nash, N. Birbilis
|
RustSEG -- Automated segmentation of corrosion using deep learning
| null | null | null |
cs.CV cond-mat.mtrl-sci
|
The inspection of infrastructure for corrosion remains a task that is
typically performed manually by qualified engineers or inspectors. This task of
inspection is laborious, slow, and often requires complex access. Recently,
deep learning based algorithms have revealed promise and performance in the
automatic detection of corrosion. However, to date, research regarding the
segmentation of images for automated corrosion detection has been limited, due
to the lack of availability of per-pixel labelled data sets which are required
for model training. Herein, a novel deep learning approach (termed RustSEG) is
presented, that can accurately segment images for automated corrosion
detection, without the requirement of per-pixel labelled data sets for
training. The RustSEG method will first, using deep learning techniques,
determine if corrosion is present in an image (i.e. a classification task), and
then if corrosion is present, the model will examine what pixels in the
original image contributed to that classification decision. Finally, the method
can refine its predictions into a pixel-level segmentation mask. In ideal
cases, the method is able to generate precise masks of corrosion in images,
demonstrating that the automated segmentation of corrosion without per-pixel
training data is possible, addressing a significant hurdle in automated
infrastructure inspection.
|
[{'version': 'v1', 'created': 'Wed, 11 May 2022 11:48:02 GMT'}]
|
2022-05-12
|
Mao Su, Ji-Hui Yang, Hong-Jun Xiang and Xin-Gao Gong
|
Efficient determination of the Hamiltonian and electronic properties
using graph neural network with complete local coordinates
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph
|
Despite the successes of machine learning methods in physical sciences,
prediction of the Hamiltonian, and thus electronic properties, is still
unsatisfactory. Here, based on graph neural network architecture, we present an
extendable neural network model to determine the Hamiltonian from ab initio
data, with only local atomic structures as inputs. Rotational equivariance of
the Hamiltonian is achieved by our complete local coordinates. The local
coordinates information, encoded using the convolutional neural network and
designed to preserve Hermitian symmetry, is used to map hopping parameters onto
local structures. We demonstrate the performance of our model using graphene
and SiGe random alloys as examples. We show that our neural network model,
although trained using small-size systems, can predict the Hamiltonian, as well
as electronic properties such as band structures and densities of states (DOS)
for large-size systems within the ab initio accuracy, justifying its
extensibility. In combination with the high efficiency of our model, which
takes only seconds to get the Hamiltonian of a 1728-atom system, present work
provides a general framework to predict electronic properties efficiently and
accurately, which provides new insights into computational physics and will
accelerate the research for large-scale materials.
|
[{'version': 'v1', 'created': 'Wed, 11 May 2022 13:20:20 GMT'}, {'version': 'v2', 'created': 'Wed, 11 Jan 2023 17:56:34 GMT'}]
|
2023-01-12
|
Darren C. Pagan, Calvin R. Pash, Austin R. Benson, Matthew P. Kasemer
|
Graph Neural Network Modeling of Grain-scale Anisotropic Elastic
Behavior using Simulated and Measured Microscale Data
| null | null | null |
cond-mat.mtrl-sci
|
Here we assess the applicability of graph neural networks (GNNs) for
predicting the grain-scale elastic response of polycrystalline metallic alloys.
Using GNN surrogate models, grain-averaged stresses during uniaxial elastic
tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al
(Ti-7Al), as example face centered cubic and hexagonal closed packed alloys,
are predicted. A transfer learning approach is taken in which GNN surrogate
models are trained using crystal elasticity finite element method (CEFEM)
simulations and then the trained surrogate models are used to predict the
mechanical response of microstructures measured using high-energy X-ray
diffraction microscopy (HEDM). The performance of using various microstructural
and micromechanical descriptors for input nodal features to the GNNs is
explored through comparisons to traditional mean-field theory predictions,
reserved full-field CEFEM data, and measured far-field HEDM data. The effects
of elastic anisotropy on GNN model performance and outlooks for extension of
the framework are discussed.
|
[{'version': 'v1', 'created': 'Thu, 12 May 2022 19:25:50 GMT'}, {'version': 'v2', 'created': 'Tue, 30 Aug 2022 18:24:36 GMT'}]
|
2022-09-01
|
Emanuele Costa, Giuseppe Scriva, Rosario Fazio, Sebastiano Pilati
|
Deep learning density functionals for gradient descent optimization
|
Phys. Rev. E 106, 045309 (2022)
|
10.1103/PhysRevE.106.045309
| null |
physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.other cond-mat.quant-gas
|
Machine-learned regression models represent a promising tool to implement
accurate and computationally affordable energy-density functionals to solve
quantum many-body problems via density functional theory. However, while they
can easily be trained to accurately map ground-state density profiles to the
corresponding energies, their functional derivatives often turn out to be too
noisy, leading to instabilities in self-consistent iterations and in
gradient-based searches of the ground-state density profile. We investigate how
these instabilities occur when standard deep neural networks are adopted as
regression models, and we show how to avoid it using an ad-hoc convolutional
architecture featuring an inter-channel averaging layer. The testbed we
consider is a realistic model for noninteracting atoms in optical speckle
disorder. With the inter-channel average, accurate and systematically
improvable ground-state energies and density profiles are obtained via
gradient-descent optimization, without instabilities nor violations of the
variational principle.
|
[{'version': 'v1', 'created': 'Tue, 17 May 2022 13:57:08 GMT'}, {'version': 'v2', 'created': 'Mon, 7 Nov 2022 12:14:42 GMT'}]
|
2022-11-08
|
Md Esharuzzaman Emu
|
Predictions of Electromotive Force of Magnetic Shape Memory Alloy (MSMA)
Using Constitutive Model and Generalized Regression Neural Network
| null |
10.1088/1361-665X/acb2a1
| null |
cond-mat.mtrl-sci stat.ML
|
Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals,
can exhibit the shape memory effect due to an applied magnetic field at room
temperature. Under a variable magnetic field and a constant bias stress
loading, MSMAs have been used for actuation applications. This work introduced
a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga
single crystal. This model includes the fact that the magnetic easy axis in the
two variants is not exactly perpendicular as observed by D silva et al. This
offset helps explain some of the power harvesting capabilities of MSMAs. Model
predictions are compared to experimental data collected on a Ni-Mn-Ga single
crystal. The experiments include both stress-controlled loading with constant
bias magnetic field load (which mimics power harvesting or sensing) and
fieldcontrolled loading with constant bias compressive stress (which mimics
actuation). Each type of test was performed at several different load levels,
and the applied field was measured without the MSMA specimen present so that
demagnetization does not affect the experimentally measured field as suggested
by Eberle et al. Results show decent agreement between model predictions and
experimental data. Although the model predicts experimental results decently,
it does not capture all the features of the experimental data. In order to
capture all the experimental features, finally, a generalized regression neural
network (GRNN) was used to train the experimental data (stress, strain,
magnetic field, and emf) so that it can make a reasonably better prediction.
|
[{'version': 'v1', 'created': 'Wed, 8 Jun 2022 06:38:33 GMT'}, {'version': 'v2', 'created': 'Wed, 19 Oct 2022 03:50:36 GMT'}, {'version': 'v3', 'created': 'Fri, 11 Nov 2022 21:37:38 GMT'}]
|
2023-01-20
|
Kihyun Lee, Jinsub Park, Soyeon Choi, Yangjin Lee, Sol Lee, Joowon
Jung, Jong-Young Lee, Farman Ullah, Zeeshan Tahir, Yong Soo Kim, Gwan-Hyoung
Lee, and Kwanpyo Kim
|
STEM image analysis based on deep learning: identification of vacancy
defects and polymorphs of ${MoS_2}$
|
Nano Letters, 2022
|
10.1021/acs.nanolett.2c00550
| null |
cond-mat.mes-hall cond-mat.mtrl-sci cs.CV
|
Scanning transmission electron microscopy (STEM) is an indispensable tool for
atomic-resolution structural analysis for a wide range of materials. The
conventional analysis of STEM images is an extensive hands-on process, which
limits efficient handling of high-throughput data. Here we apply a fully
convolutional network (FCN) for identification of important structural features
of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying
sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM
images. Efficient models are achieved based on training with simulated images
in the presence of different levels of noise, aberrations, and carbon
contamination. The accuracy of the FCN models toward extensive experimental
STEM images is comparable to that of careful hands-on analysis. Our work
provides a guideline on best practices to train a deep learning model for STEM
image analysis and demonstrates FCN's application for efficient processing of a
large volume of STEM data.
|
[{'version': 'v1', 'created': 'Thu, 9 Jun 2022 04:43:56 GMT'}]
|
2022-06-10
|
Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto, Toshiyuki Hama,
Rodrigo F. Neumann, Binquan Luan, Seiji Takeda, Lisa Hamada and Mathias B.
Steiner
|
AI powered, automated discovery of polymer membranes for carbon capture
| null | null | null |
cond-mat.mtrl-sci
|
The generation of molecules with Artificial Intelligence (AI) is poised to
revolutionize materials discovery. Potential applications range from
development of potent drugs to efficient carbon capture and separation
technologies. However, existing computational frameworks lack automated
training data creation and physical performance validation at meso-scale where
complex properties of amorphous materials emerge. The methodological gaps have
so far limited AI design to small-molecule applications. Here, we report the
first automated discovery of complex materials through inverse molecular design
which is informed by meso-scale target features and process figures-of-merit.
We have entered the new discovery regime by computationally generating and
validating hundreds of polymer candidates designed for application in
post-combustion carbon dioxide filtration. Specifically, we have validated each
discovery step, from training dataset creation, via graph-based generative
design of optimized monomer units, to molecular dynamics simulation of gas
permeation through the polymer membranes. For the latter, we have devised a
Representative Elementary Volume (REV) enabling permeability simulations at
about 1,000x the volume of an individual, AI-generated monomer, obtaining
quantitative agreement. The discovery-to-validation time per polymer candidate
is on the order of 100 hours in a standard computing environment, offering a
computational screening alternative prior to lab validation.
|
[{'version': 'v1', 'created': 'Wed, 29 Jun 2022 13:31:24 GMT'}, {'version': 'v2', 'created': 'Thu, 28 Jul 2022 17:14:06 GMT'}]
|
2022-07-29
|
Claus O.W. Trost (1), Stanislav Zak (1), Sebastian Schaffer (2 and 3),
Christian Saringer (4), Lukas Exl (2 and 3) and Megan J. Cordill (1 and 4)
((1) Erich Schmid Institute of Materials Science, Austrian Academy of
Sciences, Leoben, Austria., (2) Wolfgang Pauli Institute c/o Faculty of
Mathematics, University of Vienna, Vienna, Austria., (3) University of Vienna
Research Platform MMM Mathematics - Magnetism - Materials, University of
Vienna, Vienna, Austria., (4.) Christian Doppler Laboratory for Advanced
Coated Cutting Tools at the Department of Materials Science, Leoben,
Austria., (5) Department of Materials Science, Leoben, Austria.)
|
Bridging Fidelities to Predict Nanoindentation Tip Radii Using
Interpretable Deep Learning Models
| null |
10.1007/s11837-022-05233-z
| null |
cond-mat.mtrl-sci
|
As the need for miniaturized structural and functional materials has
increased,the need for precise materials characterizaton has also expanded.
Nanoindentation is a popular method that can be used to measure material
mechanical behavior which enables high-throughput experiments and, in some
cases, can also provide images of the indented area through scanning. Both
indenting and scanning can cause tip wear that can influence the measurements.
Therefore, precise characterization of tip radii is needed to improve data
evaluation. A data fusion method is introduced which uses finite element
simulations and experimental data to estimate the tip radius in situ in a
meaningful way using an interpretable multi-fidelity deep learning approach. By
interpreting the machine learning models, it is shown that the approaches are
able to accurately capture physical indentation phenomena.
|
[{'version': 'v1', 'created': 'Fri, 1 Jul 2022 07:26:54 GMT'}]
|
2022-07-04
|
Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan
Chard, Aristana Scourtas, K.J. Schmidt, Kyle Chard, Ben Blaiszik and Ian
Foster
|
FAIR principles for AI models with a practical application for
accelerated high energy diffraction microscopy
|
Scientific Data 9, 657 (2022)
|
10.1038/s41597-022-01712-9
| null |
cs.AI cond-mat.mtrl-sci cs.LG
|
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and
Reusable) principles for scientific data is transforming the state-of-practice
for data management and stewardship, supporting and enabling discovery and
innovation. Learning from this initiative, and acknowledging the impact of
artificial intelligence (AI) in the practice of science and engineering, we
introduce a set of practical, concise, and measurable FAIR principles for AI
models. We showcase how to create and share FAIR data and AI models within a
unified computational framework combining the following elements: the Advanced
Photon Source at Argonne National Laboratory, the Materials Data Facility, the
Data and Learning Hub for Science, and funcX, and the Argonne Leadership
Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the
SambaNova DataScale system at the ALCF AI Testbed. We describe how this
domain-agnostic computational framework may be harnessed to enable autonomous
AI-driven discovery.
|
[{'version': 'v1', 'created': 'Fri, 1 Jul 2022 18:11:12 GMT'}, {'version': 'v2', 'created': 'Thu, 14 Jul 2022 18:11:27 GMT'}, {'version': 'v3', 'created': 'Wed, 21 Dec 2022 17:37:27 GMT'}]
|
2023-08-21
|
Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang
Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun
Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P.
Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
|
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness
Characterization from Optical Spectra of Perovskite Thin Films
| null | null | null |
cs.LG cond-mat.mtrl-sci eess.IV physics.optics
|
Transfer learning increasingly becomes an important tool in handling data
scarcity often encountered in machine learning. In the application of
high-throughput thickness as a downstream process of the high-throughput
optimization of optoelectronic thin films with autonomous workflows, data
scarcity occurs especially for new materials. To achieve high-throughput
thickness characterization, we propose a machine learning model called
thicknessML that predicts thickness from UV-Vis spectrophotometry input and an
overarching transfer learning workflow. We demonstrate the transfer learning
workflow from generic source domain of generic band-gapped materials to
specific target domain of perovskite materials, where the target domain data
only come from limited number (18) of refractive indices from literature. The
target domain can be easily extended to other material classes with a few
literature data. Defining thickness prediction accuracy to be within-10%
deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with
transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without
(lower mean and larger standard deviation). Experimental validation on six
deposited perovskite films also corroborates the efficacy of the proposed
workflow by yielding a 10.5% mean absolute percentage error (MAPE).
|
[{'version': 'v1', 'created': 'Tue, 14 Jun 2022 16:26:15 GMT'}, {'version': 'v2', 'created': 'Tue, 20 Dec 2022 08:51:48 GMT'}]
|
2022-12-21
|
Alexey N. Korovin, Innokentiy S. Humonen, Artem I. Samtsevich, Roman
A. Eremin, Artem I. Vasilyev, Vladimir D. Lazarev, Semen A. Budennyy
|
Boosting Heterogeneous Catalyst Discovery by Structurally Constrained
Deep Learning Models
|
Materials Today Chemistry 2023, 30, 101541
|
10.1016/j.mtchem.2023.101541
| null |
cond-mat.mtrl-sci cs.LG
|
The discovery of new catalysts is one of the significant topics of
computational chemistry as it has the potential to accelerate the adoption of
renewable energy sources. Recently developed deep learning approaches such as
graph neural networks (GNNs) open new opportunity to significantly extend scope
for modelling novel high-performance catalysts. Nevertheless, the graph
representation of particular crystal structure is not a straightforward task
due to the ambiguous connectivity schemes and numerous embeddings of nodes and
edges. Here we present embedding improvement for GNN that has been modified by
Voronoi tesselation and is able to predict the energy of catalytic systems
within Open Catalyst Project dataset. Enrichment of the graph was calculated
via Voronoi tessellation and the corresponding contact solid angles and types
(direct or indirect) were considered as features of edges and Voronoi volumes
were used as node characteristics. The auxiliary approach was enriching node
representation by intrinsic atomic properties (electronegativity, period and
group position). Proposed modifications allowed us to improve the mean absolute
error of the original model and the final error equals to 651 meV per atom on
the Open Catalyst Project dataset and 6 meV per atom on the intermetallics
dataset. Also, by consideration of additional dataset, we show that a sensible
choice of data can decrease the error to values above physically-based 20 meV
per atom threshold.
|
[{'version': 'v1', 'created': 'Mon, 11 Jul 2022 17:01:28 GMT'}, {'version': 'v2', 'created': 'Mon, 5 Sep 2022 13:49:45 GMT'}, {'version': 'v3', 'created': 'Sun, 2 Oct 2022 15:30:48 GMT'}]
|
2023-04-24
|
Sadhana Singh, Avinash G. Khanderao, Mukul Gupta, Ilya Sergeev, H. C.
Wille, Kai Schlage, Marcus Herlitschke, Dileep Kumar
|
Origin of exchange bias in [Co/Pt]ML/Fe multilayer with orthogonal
magnetic anisotropies
|
Phys. Rev. B 108, 075414 (2023)
|
10.1103/PhysRevB.108.075414
| null |
cond-mat.mtrl-sci
|
Magnetization reversal of soft ferromagnetic Fe layer, coupled to [Co/Pt]ML
multilayer [ML] with perpendicular magnetic anisotropy (PMA), has been studied
in-situ with an aim to understand the origin of exchange bias (EB) in
orthogonal magnetic anisotropic systems. The interface remanant state of the ML
is modified by magnetic field annealing, and the effect of the same on the soft
Fe layer is monitored using the in-situ magneto-optical Kerr effect (MOKE). A
considerable shift in the Fe layer hysteresis loop from the centre and an
unusual increase in the coercivity, similar to exchange bias phenomena, is
attributed to the exchange coupling at the [Co/Pt]ML and Fe interface. The
effect of the coupling on spin orientation at the interface is further explored
precisely by performing an isotope selective grazing incident nuclear resonance
scattering (GINRS) technique. Here, the interface selectivity is achieved by
introducing a 2 nm thick Fe57 marker between [Co/Pt]ML and Fe layers. Interface
sensitivity is further enhanced by performing measurements under the x-ray
standing wave conditions. The combined MOKE and GINRS analysis revealed the
unidirectional pinning of the Fe layer due to the net in-plane magnetic spin at
the interface caused by magnetic field annealing. Unidirectional exchange
coupling or pinning at the interface, which may be due to the formation of
asymmetrical closure domains, is found responsible for the origin of EB with an
unusual increase in coercivity.
|
[{'version': 'v1', 'created': 'Fri, 15 Jul 2022 09:48:17 GMT'}, {'version': 'v2', 'created': 'Tue, 19 Jul 2022 14:23:44 GMT'}]
|
2023-09-06
|
Sachin Gautham and Tarak Patra
|
Deep Learning Potential of Mean Force between Polymer Grafted
Nanoparticles
| null | null | null |
cond-mat.mtrl-sci cond-mat.soft
|
Grafting polymer chains on nanoparticles surfaces is a well-known route to
control their self assembly and distribution in a polymer matrix. A wide
variety of self assembled structures are achieved by changing the grafting
patterns on an individual nanoparticle surface. However, accurate estimation of
the effective potential of mean force between a pair of grafted nanoparticles
that determines their assembly and distribution in a polymer matrix is an
outstanding challenge in nanoscience. Here, we propose a new deep learning
method that learns the interaction between a pair of grafted nanoparticles from
the molecular dynamics trajectory of a cluster of polymer-grafted
nanoparticles. Subsequently, we carry out the deep learning potential of mean
force-based molecular simulation that predicts the self-assembly of a large
number of polymer grafted nanoparticles into various anisotropic
superstructures, including percolating networks and bilayers depending on
nanoparticles concentration in 3D. The deep learning potential of mean
force-predicted self-assembled superstructures are consistent with the actual
superstructures of polymer grafted nanoparticles. This deep learning framework
is very generic and can accelerate the characterization and prediction of the
self-assembly and phase behaviour of polymer-grafted and unfunctionalized
nanoparticles in free space or a polymer matrix.
|
[{'version': 'v1', 'created': 'Mon, 18 Jul 2022 15:26:53 GMT'}]
|
2022-07-19
|
Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna, Mathew
Cherukara, Valeria Molinero, Subramanian KRS Sankaranarayanan
|
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale
classification of materials environment
| null | null | null |
cond-mat.mtrl-sci
|
Machine learning models and applications in materials design and discovery
typically involve the use of feature representations or "descriptors" followed
by a learning algorithm that maps them to a user-desired property of interest.
Most popular mathematical formulation-based descriptors are not unique across
atomic environments or suffer from transferability issues across different
application domains and/or material classes. In this work, we introduce the
Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph
attention-based architecture to learn unique feature representations and
perform classification of materials across multiple scales (from atomic to
mesoscale) and diverse classes ranging from metals, oxides, non-metals and even
hierarchical materials such as zeolites and semi ordered materials such as
mesophases. We first demonstrate a case study where the classification is based
on a global, structure-level representation such as space group and structural
dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials
such as polycrystals and zeolites, we next demonstrate the transferability of
our network in successfully performing local atom-level classification tasks,
such as grain boundary identification and other heterointerfaces. We also
demonstrate classification in (thermal) noisy dynamical environments using a
representative example of crystal nucleation and growth of a zeolite polymorph
from an amorphous synthesis mixture. Finally, we characterize the formation of
a binary mesophase and its phase transitions and the growth of ice,
demonstrating the performance of CEGANN in systems with thermal noise and
compositional diversity. Overall, our approach is agnostic to the material type
and allows for multiscale classification of features ranging from atomic-scale
crystal structures to heterointerfaces to microscale grain boundaries.
|
[{'version': 'v1', 'created': 'Wed, 20 Jul 2022 19:42:46 GMT'}, {'version': 'v2', 'created': 'Tue, 15 Nov 2022 05:10:45 GMT'}]
|
2022-11-16
|
Jingrui Wei, Ben Blaiszik, Aristana Scourtas, Dane Morgan, and Paul M.
Voyles
|
Benchmark tests of atom segmentation deep learning models with a
consistent dataset
|
Microsc. Microanal. (2022)
|
10.1093/micmic/ozac043
| null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
The information content of atomic resolution scanning transmission electron
microscopy (STEM) images can often be reduced to a handful of parameters
describing each atomic column, chief amongst which is the column position.
Neural networks (NNs) are a high performance, computationally efficient method
to automatically locate atomic columns in images, which has led to a profusion
of NN models and associated training datasets. We have developed a benchmark
dataset of simulated and experimental STEM images and used it to evaluate the
performance of two sets of recent NN models for atom location in STEM images.
Both models exhibit high performance for images of varying quality from several
different crystal lattices. However, there are important differences in
performance as a function of image quality, and both models perform poorly for
images outside the training data, such as interfaces with large difference in
background intensity. Both the benchmark dataset and the models are available
using the Foundry service for dissemination, discovery, and reuse of machine
learning models.
|
[{'version': 'v1', 'created': 'Wed, 20 Jul 2022 19:51:39 GMT'}]
|
2023-02-22
|
Ramya Gurunathan and Kamal Choudhary and Francesca Tavazza
|
Rapid Prediction of Phonon Structure and Properties using an Atomistic
Line Graph Neural Network (ALIGNN)
| null | null | null |
cond-mat.mtrl-sci
|
The phonon density-of-states (DOS) summarizes the lattice vibrational modes
supported by a structure, and gives access to rich information about the
material's stability, thermodynamic constants, and thermal transport
coefficients. Here, we present an atomistic line graph neural network (ALIGNN)
model for the prediction of the phonon density of states and the derived
thermal and thermodynamic properties. The model is trained on a database of
over 14,000 phonon spectra included in the JARVIS-DFT (Joint Automated
Repository for Various Integrated Simulations: Density Functional Theory)
database. The model predictions are shown to capture the spectral features of
the phonon density-of-states, effectively categorize dynamical stability, and
lead to accurate predictions of DOS-derived thermal and thermodynamic
properties, including heat capacity $C_{\mathrm{V}}$, vibrational entropy
$S_{\mathrm{vib}}$, and the isotopic phonon scattering rate
$\tau^{-1}_{\mathrm{i}}$. The DOS-mediated ALIGNN model provides superior
predictions when compared to a direct deep-learning prediction of these
material properties as well as predictions based on analytic simplifications of
the phonon DOS, including the Debye or Born-von Karman models. Finally, the
ALIGNN model is used to predict the phonon spectra and properties for about
40,000 additional materials listed in the JARVIS-DFT database, which are
validated as far as possible against other open-sourced high-throughput DFT
phonon databases.
|
[{'version': 'v1', 'created': 'Mon, 25 Jul 2022 20:13:49 GMT'}]
|
2022-07-27
|
Andy Bridger, William I. F. David, Thomas J. Wood, Mohsen Danaie,
Keith T. Butler
|
Versatile Domain Mapping Of Scanning Electron Nanobeam Diffraction
Datasets Utilising Variational AutoEncoders and Decoder-Assisted Latent-Space
Clustering
| null | null | null |
cond-mat.mtrl-sci
|
Advancements in fast electron detectors have enabled the statistically
significant sampling of crystal structures on the nanometre scale by means of
Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural
similarity across this length scale is key to bridging the gap between local
atomic structure (using atomic resolution techniques such as High Resolution
Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using
bulk techniques such as powder X-ray and neutron diffraction). The use of SEND
technique allows for structural investigation of a broad range of samples, due
to the techniques ability to operate with low electron dosage and its tolerance
for sample thickness, relative to HR-STEM. This, coupled with the capacity for
data collection over a wide areas and the automation of this collection, allows
for statistically representative sampling of the microstructure. Also due to
these factors, SEND generates large datasets and as a result automated/
semi-automated data processing workflows are required to aid in maximal
extraction of useful information. As such, this paper outlines a versatile,
data-driven approach for producing domain maps, as well as a statistical
approach for assessing their applicability. The production of such domain maps
for a dataset can help highlight nuance in the microstructure, as well as
improve the manageability of that dataset for further investigation. The
workflow outlined utilises a Variational AutoEncoder to identify and learn the
sources of variance in the diffraction signal and this, in combination with
clustering techniques, is used to produce domain maps for a set of varied
example cases. This approach: is agnostic to domain crystallinity; requires no
prior knowledge of crystal structure; and does not require the, potentially
prohibitive, simulation of a library of appropriate diffraction patterns.
|
[{'version': 'v1', 'created': 'Wed, 27 Jul 2022 09:16:01 GMT'}]
|
2022-07-28
|
\c{S}ener \"Oz\"onder and H. K\"ubra K\"u\c{c}\"ukkartal
|
Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian
Optimization with Neural Network Kernel
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph stat.ML
|
Density functional theory (DFT) is a powerful computational method used to
obtain physical and chemical properties of materials. In the materials
discovery framework, it is often necessary to virtually screen a large and
high-dimensional chemical space to find materials with desired properties.
However, grid searching a large chemical space with DFT is inefficient due to
its high computational cost. We propose an approach utilizing Bayesian
optimization (BO) with an artificial neural network kernel to enable smart
search. This method leverages the BO algorithm, where the neural network,
trained on a limited number of DFT results, determines the most promising
regions of the chemical space to explore in subsequent iterations. This
approach aims to discover materials with target properties while minimizing the
number of DFT calculations required. To demonstrate the effectiveness of this
method, we investigated 63 doped graphene quantum dots (GQDs) with sizes
ranging from 1 to 2 nm to find the structure with the highest light absorbance.
Using time-dependent DFT (TDDFT) only 12 times, we achieved a significant
reduction in computational cost, approximately 20% of what would be required
for a full grid search, by employing the BO algorithm with a neural network
kernel. Considering that TDDFT calculations for a single GQD require about half
a day of wall time on high-performance computing nodes, this reduction is
substantial. Our approach can be generalized to the discovery of new drugs,
chemicals, crystals, and alloys with high-dimensional and large chemical
spaces, offering a scalable solution for various applications in materials
science.
|
[{'version': 'v1', 'created': 'Tue, 16 Aug 2022 09:02:16 GMT'}, {'version': 'v2', 'created': 'Sat, 3 Aug 2024 20:39:34 GMT'}]
|
2024-08-06
|
Kyeongpung Lee, Yutack Park, and Seungwu Han
|
$\textit{Ab initio}$ construction of full phase diagram of MgO-CaO
eutectic system using neural network interatomic potentials
| null | null | null |
physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci
|
While several studies confirmed that machine-learned potentials (MLPs) can
provide accurate free energies for determining phase stabilities, the abilities
of MLPs for efficiently constructing a full phase diagram of multi-component
systems are yet to be established. In this work, by employing neural network
interatomic potentials (NNPs), we demonstrate construction of the MgO-CaO
eutectic phase diagram with temperatures up to 3400 K, which includes liquid
phases. The NNP is trained over trajectories of various solid and liquid phases
at several compositions that are calculated within the density functional
theory (DFT). For the exchange-correlation energy among electrons, we compare
the PBE and SCAN functionals. The phase boundaries such as solidus, solvus, and
liquidus are determined by free-energy calculations based on the thermodynamic
integration or semigrand ensemble methods, and salient features in the phase
diagram such as solubility limit and eutectic points are well reproduced. In
particular, the phase diagram produced by the SCAN-NNP closely follows the
experimental data, exhibiting both eutectic composition and temperature within
the measurements. On a rough estimate, the whole procedure is more than 1,000
times faster than pure-DFT based approaches. We believe that this work paves
the way to fully $\textit{ab initio}$ calculation of phase diagrams.
|
[{'version': 'v1', 'created': 'Thu, 25 Aug 2022 04:25:46 GMT'}]
|
2022-08-26
|
Zeyu Liu, Meng Jiang, Tengfei Luo
|
Leveraging Low-Fidelity Data to Improve Machine Learning of Sparse
High-Fidelity Thermal Conductivity Data via Transfer Learning
| null | null | null |
cond-mat.mtrl-sci physics.app-ph
|
Lattice thermal conductivity (TC) of semiconductors is crucial for various
applications, ranging from microelectronics to thermoelectrics. Data-driven
approach can potentially establish the critical composition-property
relationship needed for fast screening of candidates with desirable TC, but the
small number of available data remains the main challenge. TC can be
efficiently calculated using empirical models, but they have inferior accuracy
compared to the more resource-demanding first-principles calculations. Here, we
demonstrate the use of transfer learning (TL) to improve the machine learning
models trained on small but high-fidelity TC data from experiments and
first-principles calculations, by leveraging a large but low-fidelity data
generated from empirical TC models, where the trainings on high- and
low-fidelity TC data are treated as different but related tasks. TL improves
the model accuracy by as much as 23% in R2 and reduces the average factor
difference by as much as 30%. Using the TL model, a large semiconductor
database is screened, and several candidates with room temperature TC > 350
W/mK are identified and further verified using first-principles simulations.
This study demonstrates that TL can leverage big low-fidelity data as a proxy
task to improve models for the target task with high-fidelity but small data.
Such a capability of TL may have important implications to materials
informatics in general.
|
[{'version': 'v1', 'created': 'Sun, 28 Aug 2022 21:45:54 GMT'}]
|
2022-08-30
|
Mohammad S. Khorrami, Jaber R. Mianroodi, Nima H. Siboni, Pawan Goyal,
Bob Svendsen, Peter Benner, Dierk Raabe
|
An artificial neural network for surrogate modeling of stress fields in
viscoplastic polycrystalline materials
| null | null | null |
cond-mat.mtrl-sci
|
The purpose of this work is the development of an artificial neural network
(ANN) for surrogate modeling of the mechanical response of viscoplastic grain
microstructures. To this end, a U-Net-based convolutional neural network (CNN)
is trained to account for the history dependence of the material behavior. The
training data take the form of numerical simulation results for the von Mises
stress field under quasi-static tensile loading. The trained CNN (tCNN) can
accurately reproduce both the average response as well as the local von Mises
stress field. The tCNN calculates the von Mises stress field of grain
microstructures not included in the training dataset about 500 times faster
than its calculation based on the numerical solution with a spectral solver of
the corresponding initial-boundary-value problem. The tCNN is also successfully
applied to other types of microstructure morphologies (e.g., matrix-inclusion
type topologies) and loading levels not contained in the training dataset.
|
[{'version': 'v1', 'created': 'Mon, 29 Aug 2022 10:47:55 GMT'}]
|
2022-08-30
|
Roberto Perera and Vinamra Agrawal
|
Dynamic and adaptive mesh-based graph neural network framework for
simulating displacement and crack fields in phase field models
| null | null | null |
cond-mat.mtrl-sci
|
Fracture is one of the main causes of failure in engineering structures.
Phase field methods coupled with adaptive mesh refinement (AMR) techniques have
been widely used to model crack propagation due to their ease of implementation
and scalability. However, phase field methods can still be computationally
demanding making them unfeasible for high-throughput design applications.
Machine learning (ML) models such as Graph Neural Networks (GNNs) have shown
their ability to emulate complex dynamic problems with speed-ups orders of
magnitude faster compared to high-fidelity simulators. In this work, we present
a dynamic mesh-based GNN framework for emulating phase field simulations of
crack propagation with AMR for different crack configurations. The developed
framework - ADAPTive mesh-based graph neural network (ADAPT-GNN) - exploits the
benefits of both ML methods and AMR by describing the graph representation at
each time-step as the refined mesh itself. Using ADAPT-GNN, we predict the
evolution of displacement fields and scalar damage field (or phase field) with
high accuracy compared to conventional phase field fracture model. We also
compute crack stress fields with high accuracy using the predicted
displacements and phase field parameter. Finally, we observe speed up of 15-36x
compared to serial execution of the phase field model.
|
[{'version': 'v1', 'created': 'Tue, 30 Aug 2022 16:12:16 GMT'}, {'version': 'v2', 'created': 'Thu, 1 Sep 2022 14:00:43 GMT'}, {'version': 'v3', 'created': 'Tue, 11 Jul 2023 16:54:12 GMT'}]
|
2023-07-12
|
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan
Thiyagalingam, Francesca Tavazza
|
Unified Graph Neural Network Force-field for the Periodic Table
| null |
10.1039/D2DD00096B
| null |
cond-mat.mtrl-sci
|
Classical force fields (FF) based on machine learning (ML) methods show great
potential for large scale simulations of materials. MLFFs have hitherto largely
been designed and fitted for specific systems and are not usually transferable
to chemistries beyond the specific training set. We develop a unified
atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both
structurally and chemically diverse materials with any combination of 89
elements from the periodic table. To train the ALIGNN-FF model, we use the
JARVIS-DFT dataset which contains around 75000 materials and 4 million
energy-force entries, out of which 307113 are used in the training. We
demonstrate the applicability of this method for fast optimization of atomic
structures in the crystallography open database and by predicting accurate
crystal structures using genetic algorithm for alloys.
|
[{'version': 'v1', 'created': 'Mon, 12 Sep 2022 19:13:27 GMT'}, {'version': 'v2', 'created': 'Fri, 16 Sep 2022 11:21:11 GMT'}]
|
2023-03-06
|
Minyi Dai, Mehmet F. Demirel, Xuanhan Liu, Yingyu Liang, Jia-Mian Hu
|
Graph Neural Network for Predicting the Effective Properties of
Polycrystalline Materials: A Comprehensive Analysis
| null | null | null |
cond-mat.mtrl-sci
|
We develop a polycrystal graph neural network (PGNN) model for predicting the
effective properties of polycrystalline materials, using the Li7La3Zr2O12
ceramic as an example. A large-scale dataset with >5000 different
three-dimensional polycrystalline microstructures of finite-width grain
boundary is generated by Voronoi tessellation and processing of the electron
backscatter diffraction images. The effective ion conductivities and elastic
stiffness coefficients of these microstructures are calculated by
high-throughput physics-based simulations. The optimized PGNN model achieves a
low error of <1.4% in predicting all three diagonal components of the effective
Li-ion conductivity matrix, outperforming a linear regression model and two
baseline convolutional neural network models. Sequential forward selection
method is used to quantify the relative importance of selecting individual
grain (boundary) features to improving the property prediction accuracy,
through which both the critical and unwanted node (edge) feature can be
determined. The extrapolation performance of the trained PGNN model is also
investigated. The transfer learning performance is evaluated by using the PGNN
model pretrained for predicting conductivities to predict the elastic
properties of the same set of microstructures.
|
[{'version': 'v1', 'created': 'Mon, 12 Sep 2022 20:12:19 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2023 05:39:03 GMT'}]
|
2023-06-09
|
Paul Laiu, Ying Yang, Massimiliano Lupo Pasini, Jong Youl Choi,
Dongwon Shin
|
A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid
Solutions
| null | null | null |
cond-mat.mtrl-sci
|
We present a data-centric deep learning (DL) approach using neural networks
(NNs) to predict the thermodynamics of ternary solid solutions. We explore how
NNs can be trained with a dataset of Gibbs free energies computed from a
CALPHAD database to predict ternary systems as a function of composition and
temperature. We have chosen the energetics of the FCC solid solution phase in
226 binaries consisting of 23 elements at 11 different temperatures to
demonstrate the feasibility. The number of binary data points included in the
present study is 102,000. We select six ternaries to augment the binary dataset
to investigate their influence on the NN prediction accuracy. We examine the
sensitivity of data sampling on the prediction accuracy of NNs over selected
ternary systems. It is anticipated that the current DL workflow can be further
elevated by integrating advanced descriptors beyond the elemental composition
and more curated training datasets to improve prediction accuracy and
applicability.
|
[{'version': 'v1', 'created': 'Mon, 12 Sep 2022 20:59:10 GMT'}]
|
2022-09-14
|
Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu
|
Probabilistic Generative Transformer Language models for Generative
Design of Molecules
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Self-supervised neural language models have recently found wide applications
in generative design of organic molecules and protein sequences as well as
representation learning for downstream structure classification and functional
prediction. However, most of the existing deep learning models for molecule
design usually require a big dataset and have a black-box architecture, which
makes it difficult to interpret their design logic. Here we propose Generative
Molecular Transformer (GMTransformer), a probabilistic neural network model for
generative design of molecules. Our model is built on the blank filling
language model originally developed for text processing, which has demonstrated
unique advantages in learning the "molecules grammars" with high-quality
generation, interpretability, and data efficiency. Benchmarked on the MOSES
datasets, our models achieve high novelty and Scaf compared to other baselines.
The probabilistic generation steps have the potential in tinkering molecule
design due to their capability of recommending how to modify existing molecules
with explanation, guided by the learned implicit molecule chemistry. The source
code and datasets can be accessed freely at
https://github.com/usccolumbia/GMTransformer
|
[{'version': 'v1', 'created': 'Tue, 20 Sep 2022 01:51:57 GMT'}]
|
2022-09-21
|
Jaber R. Mianroodi, Nima H. Siboni, Dierk Raabe
|
Computational Discovery of Energy-Efficient Heat Treatment for
Microstructure Design using Deep Reinforcement Learning
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Deep Reinforcement Learning (DRL) is employed to develop autonomously
optimized and custom-designed heat-treatment processes that are both,
microstructure-sensitive and energy efficient. Different from conventional
supervised machine learning, DRL does not rely on static neural network
training from data alone, but a learning agent autonomously develops optimal
solutions, based on reward and penalty elements, with reduced or no
supervision. In our approach, a temperature-dependent Allen-Cahn model for
phase transformation is used as the environment for the DRL agent, serving as
the model world in which it gains experience and takes autonomous decisions.
The agent of the DRL algorithm is controlling the temperature of the system, as
a model furnace for heat-treatment of alloys. Microstructure goals are defined
for the agent based on the desired microstructure of the phases. After
training, the agent can generate temperature-time profiles for a variety of
initial microstructure states to reach the final desired microstructure state.
The agent's performance and the physical meaning of the heat-treatment profiles
generated are investigated in detail. In particular, the agent is capable of
controlling the temperature to reach the desired microstructure starting from a
variety of initial conditions. This capability of the agent in handling a
variety of conditions paves the way for using such an approach also for
recycling-oriented heat treatment process design where the initial composition
can vary from batch to batch, due to impurity intrusion, and also for the
design of energy-efficient heat treatments. For testing this hypothesis, an
agent without penalty on the total consumed energy is compared with one that
considers energy costs. The energy cost penalty is imposed as an additional
criterion on the agent for finding the optimal temperature-time profile.
|
[{'version': 'v1', 'created': 'Thu, 22 Sep 2022 18:07:16 GMT'}]
|
2022-09-26
|
Christopher Kuenneth and Rampi Ramprasad
|
polyBERT: A chemical language model to enable fully machine-driven
ultrafast polymer informatics
| null |
10.1038/s41467-023-39868-6
| null |
cond-mat.mtrl-sci cs.AI cs.LG
|
Polymers are a vital part of everyday life. Their chemical universe is so
large that it presents unprecedented opportunities as well as significant
challenges to identify suitable application-specific candidates. We present a
complete end-to-end machine-driven polymer informatics pipeline that can search
this space for suitable candidates at unprecedented speed and accuracy. This
pipeline includes a polymer chemical fingerprinting capability called polyBERT
(inspired by Natural Language Processing concepts), and a multitask learning
approach that maps the polyBERT fingerprints to a host of properties. polyBERT
is a chemical linguist that treats the chemical structure of polymers as a
chemical language. The present approach outstrips the best presently available
concepts for polymer property prediction based on handcrafted fingerprint
schemes in speed by two orders of magnitude while preserving accuracy, thus
making it a strong candidate for deployment in scalable architectures including
cloud infrastructures.
|
[{'version': 'v1', 'created': 'Thu, 29 Sep 2022 14:09:54 GMT'}]
|
2023-07-20
|
Animesh Ghose, Mikhail Segal, Fanchen Meng, Zhu Liang, Mark S.
Hybertsen, Xiaohui Qu, Eli Stavitski, Shinjae Yoo, Deyu Lu, Matthew R.
Carbone
|
Uncertainty-aware predictions of molecular X-ray absorption spectra
using neural network ensembles
| null | null | null |
cond-mat.mtrl-sci
|
As machine learning (ML) methods continue to be applied to a broad scope of
problems in the physical sciences, uncertainty quantification is becoming
correspondingly more important for their robust application. Uncertainty aware
machine learning methods have been used in select applications, but largely for
scalar properties. In this work, we showcase an exemplary study in which neural
network ensembles are used to predict the X-ray absorption spectra of small
molecules, as well as their point-wise uncertainty, from local atomic
environments. The performance of the resulting surrogate clearly demonstrates
quantitative correlation between errors relative to ground truth and the
predicted uncertainty estimates. Significantly, the model provides an upper
bound on the expected error. Specifically, an important quality of this
uncertainty-aware model is that it can indicate when the model is predicting on
out-of-sample data. This allows for its integration with large scale sampling
of structures together with active learning or other techniques for structure
refinement. Additionally, our models can be generalized to larger molecules
than those used for training, and also successfully track uncertainty due to
random distortions in test molecules. While we demonstrate this workflow on a
specific example, ensemble learning is completely general. We believe it could
have significant impact on ML-enabled forward modeling of a broad array of
molecular and materials properties.
|
[{'version': 'v1', 'created': 'Sat, 1 Oct 2022 18:11:26 GMT'}, {'version': 'v2', 'created': 'Fri, 28 Oct 2022 19:17:51 GMT'}, {'version': 'v3', 'created': 'Sun, 22 Jan 2023 02:54:31 GMT'}]
|
2023-01-24
|
Abdourahman Khaireh-Walieh, Alexandre Arnoult, S\'ebastien Plissard,
Peter R. Wiecha
|
Monitoring MBE substrate deoxidation via RHEED image-sequence analysis
by deep learning
|
Crystal Growth and Design, 23(2) 892-898 (2023)
|
10.1021/acs.cgd.2c01132
| null |
cond-mat.mes-hall cond-mat.mtrl-sci cs.LG
|
Reflection high-energy electron diffraction (RHEED) is a powerful tool in
molecular beam epitaxy (MBE), but RHEED images are often difficult to
interpret, requiring experienced operators. We present an approach for
automated surveillance of GaAs substrate deoxidation in MBE reactors using deep
learning based RHEED image-sequence classification. Our approach consists of an
non-supervised auto-encoder (AE) for feature extraction, combined with a
supervised convolutional classifier network. We demonstrate that our
lightweight network model can accurately identify the exact deoxidation moment.
Furthermore we show that the approach is very robust and allows accurate
deoxidation detection during months without requiring re-training. The main
advantage of the approach is that it can be applied to raw RHEED images without
requiring further information such as the rotation angle, temperature, etc.
|
[{'version': 'v1', 'created': 'Fri, 7 Oct 2022 10:01:06 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Dec 2022 14:14:55 GMT'}]
|
2023-06-09
|
Himanshu and Tarak K Patra
|
When does deep learning fail and how to tackle it? A critical analysis
on polymer sequence-property surrogate models
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Deep learning models are gaining popularity and potency in predicting polymer
properties. These models can be built using pre-existing data and are useful
for the rapid prediction of polymer properties. However, the performance of a
deep learning model is intricately connected to its topology and the volume of
training data. There is no facile protocol available to select a deep learning
architecture, and there is a lack of a large volume of homogeneous
sequence-property data of polymers. These two factors are the primary
bottleneck for the efficient development of deep learning models. Here we
assess the severity of these factors and propose new algorithms to address
them. We show that a linear layer-by-layer expansion of a neural network can
help in identifying the best neural network topology for a given problem.
Moreover, we map the discrete sequence space of a polymer to a continuous
one-dimensional latent space using a machine learning pipeline to identify
minimal data points for building a universal deep learning model. We implement
these approaches for three representative cases of building sequence-property
surrogate models, viz., the single-molecule radius of gyration of a copolymer,
adhesive free energy of a copolymer, and copolymer compatibilizer,
demonstrating the generality of the proposed strategies. This work establishes
efficient methods for building universal deep learning models with minimal data
and hyperparameters for predicting sequence-defined properties of polymers.
|
[{'version': 'v1', 'created': 'Wed, 12 Oct 2022 23:04:10 GMT'}]
|
2022-10-14
|
Shuyan Zhang, Jie Gong, Sharon Chu, Daniel Xiao, B. Reeja Jayan, Alan
J. H. McGaughey
|
Pair distribution function analysis for oxide defect identification
through feature extraction and supervised learning
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Feature extraction and a neural network model are applied to predict the
defect types and concentrations in experimental TiO$_2$ samples. A dataset of
TiO$_2$ structures with vacancies and interstitials of oxygen and titanium is
built and the structures are relaxed using energy minimization. The features of
the calculated pair distribution functions (PDFs) of these defected structures
are extracted using linear methods (principal component analysis, non-negative
matrix factorization) and non-linear methods (autoencoder, convolutional neural
network). The extracted features are used as the inputs to a neural network
that maps the feature weights to the concentration of each defect type. The
performance of this machine learning pipeline is validated by predicting the
defect concentrations based on experimentally-measured TiO$_2$ PDFs and
comparing the results to brute-force predictions. A physics-based
initialization of the autoencoder has the highest accuracy in predicting the
defect concentrations. This model incorporates physical interpretability and
predictability of material properties, enabling a more efficient material
characterization process with scattering data.
|
[{'version': 'v1', 'created': 'Thu, 13 Oct 2022 21:34:56 GMT'}]
|
2022-10-17
|
Ivan Novikov, Olga Kovalyova, Alexander Shapeev and Max Hodapp
|
AI-accelerated Materials Informatics Method for the Discovery of Ductile
Alloys
|
Journal of Materials Research (2022)
|
10.1557/s43578-022-00783-z
| null |
cond-mat.mtrl-sci
|
In computational materials science, a common means for predicting macroscopic
(e.g., mechanical) properties of an alloy is to define a model using
combinations of descriptors that depend on some material properties (elastic
constants, misfit volumes, etc.), representative for the macroscopic behavior.
The material properties are usually computed using special quasi-random
structures (SQSs), in tandem with density functional theory (DFT). However, DFT
scales cubically with the number of atoms and is thus impractical for a
screening over many alloy compositions.
Here, we present a novel methodology which combines modeling approaches and
machine-learning interatomic potentials. Machine-learning interatomic
potentials are orders of magnitude faster than DFT, while achieving similar
accuracy, allowing for a predictive and tractable high-throughput screening
over the whole alloy space. The proposed methodology is illustrated by
predicting the room temperature ductility of the medium-entropy alloy Mo-Nb-Ta.
|
[{'version': 'v1', 'created': 'Fri, 14 Oct 2022 10:12:24 GMT'}]
|
2022-10-17
|
Dongchen Huang, Junde Liu, Tian Qian, and Yi-feng Yang
|
Spectroscopic data de-noising via training-set-free deep learning method
|
Sci. China-Phys. Mech. Astron. 66, 267011 (2023)
|
10.1007/s11433-022-2075-x
| null |
cond-mat.mtrl-sci cs.LG physics.data-an
|
De-noising plays a crucial role in the post-processing of spectra. Machine
learning-based methods show good performance in extracting intrinsic
information from noisy data, but often require a high-quality training set that
is typically inaccessible in real experimental measurements. Here, using
spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we
develop a de-noising method for extracting intrinsic spectral information
without the need for a training set. This is possible as our method leverages
the self-correlation information of the spectra themselves. It preserves the
intrinsic energy band features and thus facilitates further analysis and
processing. Moreover, since our method is not limited by specific properties of
the training set compared to previous ones, it may well be extended to other
fields and application scenarios where obtaining high-quality multidimensional
training data is challenging.
|
[{'version': 'v1', 'created': 'Wed, 19 Oct 2022 12:04:35 GMT'}, {'version': 'v2', 'created': 'Mon, 15 May 2023 12:07:55 GMT'}]
|
2023-05-16
|
Suresh Bishnoi, Skyler Badge, Jayadeva and N. M. Anoop Krishnan
|
Predicting Oxide Glass Properties with Low Complexity Neural Network and
Physical and Chemical Descriptors
| null |
10.1016/j.jnoncrysol.2023.122488
| null |
cond-mat.mtrl-sci cs.LG
|
Due to their disordered structure, glasses present a unique challenge in
predicting the composition-property relationships. Recently, several attempts
have been made to predict the glass properties using machine learning
techniques. However, these techniques have the limitations, namely, (i)
predictions are limited to the components that are present in the original
dataset, and (ii) predictions towards the extreme values of the properties,
important regions for new materials discovery, are not very reliable due to the
sparse datapoints in this region. To address these challenges, here we present
a low complexity neural network (LCNN) that provides improved performance in
predicting the properties of oxide glasses. In addition, we combine the LCNN
with physical and chemical descriptors that allow the development of universal
models that can provide predictions for components beyond the training set. By
training on a large dataset (~50000) of glass components, we show the LCNN
outperforms state-of-the-art algorithms such as XGBoost. In addition, we
interpret the LCNN models using Shapely additive explanations to gain insights
into the role played by the descriptors in governing the property. Finally, we
demonstrate the universality of the LCNN models by predicting the properties
for glasses with new components that were not present in the original training
set. Altogether, the present approach provides a promising direction towards
accelerated discovery of novel glass compositions.
|
[{'version': 'v1', 'created': 'Wed, 19 Oct 2022 12:23:30 GMT'}]
|
2023-08-09
|
Elton Pan, Christopher Karpovich and Elsa Olivetti
|
Deep Reinforcement Learning for Inverse Inorganic Materials Design
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
A major obstacle to the realization of novel inorganic materials with
desirable properties is the inability to perform efficient optimization across
both materials properties and synthesis of those materials. In this work, we
propose a reinforcement learning (RL) approach to inverse inorganic materials
design, which can identify promising compounds with specified properties and
synthesizability constraints. Our model learns chemical guidelines such as
charge and electronegativity neutrality while maintaining chemical diversity
and uniqueness. We demonstrate a multi-objective RL approach, which can
generate novel compounds with targeted materials properties including formation
energy and bulk/shear modulus alongside a lower sintering temperature synthesis
objectives. Using this approach, the model can predict promising compounds of
interest, while suggesting an optimized chemical design space for inorganic
materials discovery.
|
[{'version': 'v1', 'created': 'Fri, 21 Oct 2022 13:06:19 GMT'}]
|
2022-10-24
|
Xiaoxun Gong, He Li, Nianlong Zou, Runzhang Xu, Wenhui Duan, Yong Xu
|
General framework for E(3)-equivariant neural network representation of
density functional theory Hamiltonian
|
Nat. Commun. 14, 2848 (2023)
|
10.1038/s41467-023-38468-8
| null |
physics.comp-ph cond-mat.mtrl-sci
|
Combination of deep learning and ab initio calculation has shown great
promise in revolutionizing future scientific research, but how to design neural
network models incorporating a priori knowledge and symmetry requirements is a
key challenging subject. Here we propose an E(3)-equivariant deep-learning
framework to represent density functional theory (DFT) Hamiltonian as a
function of material structure, which can naturally preserve the Euclidean
symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method
enables very efficient electronic-structure calculation at ab initio accuracy
by learning from DFT data of small-sized structures, making routine study of
large-scale supercells ($> 10^4$ atoms) feasible. Remarkably, the method can
reach sub-meV prediction accuracy at high training efficiency, showing
state-of-the-art performance in our experiments. The work is not only of
general significance to deep-learning method development, but also creates new
opportunities for materials research, such as building Moir\'e-twisted material
database.
|
[{'version': 'v1', 'created': 'Tue, 25 Oct 2022 12:16:26 GMT'}]
|
2023-06-12
|
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar,
Matthew Spellings
|
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in
Materials Science
|
Transactions on Machine Learning Research (2023)
| null |
2835-8856
|
cs.LG cond-mat.mtrl-sci cs.AI
|
We present the Open MatSci ML Toolkit: a flexible, self-contained, and
scalable Python-based framework to apply deep learning models and methods on
scientific data with a specific focus on materials science and the OpenCatalyst
Dataset. Our toolkit provides: 1. A scalable machine learning workflow for
materials science leveraging PyTorch Lightning, which enables seamless scaling
across different computation capabilities (laptop, server, cluster) and
hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for
rapid graph neural network prototyping and development. By publishing and
sharing this toolkit with the research community via open-source release, we
hope to: 1. Lower the entry barrier for new machine learning researchers and
practitioners that want to get started with the OpenCatalyst dataset, which
presently comprises the largest computational materials science dataset. 2.
Enable the scientific community to apply advanced machine learning tools to
high-impact scientific challenges, such as modeling of materials behavior for
clean energy applications. We demonstrate the capabilities of our framework by
enabling three new equivariant neural network models for multiple OpenCatalyst
tasks and arrive at promising results for compute scaling and model
performance.
|
[{'version': 'v1', 'created': 'Mon, 31 Oct 2022 17:11:36 GMT'}]
|
2023-09-01
|
Biswadev Roy, A. Karoui, B. Vlahovic, and M.H. Wu
|
Supervised learning applied to high-dimensional millimeter wave
transient absorption data for age prediction of perovskite thin-film
| null | null | null |
cond-mat.mtrl-sci physics.data-an
|
We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved
millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick
air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited
using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We
correlated 12 parameters derived directly from acquired mmPCD kinetic-trace
data and its step-response, each with the sample-age based on the date of the
experiment. Five parameters with a high negative correlation with sample age
were finally selected as predictors in the Gaussian Process Regression (GPR)
machine learning model for prediction of the age of the sample. The effects of
aging (between 0 and 40,000 hours after film production) are quantified mainly
in terms of a shift in peak voltage, the response ratio (conductance
parameter), loss-compensated transmission coefficient, and the radiofrequency
(RF) area of the transient itself (flux). Changes in the other step-response
parameters and the decay length of the aging transients are also shown. The GPR
model is found to work well for a forward prediction of the age of the sample
using this method. It is noted that the Matern-5 over 2 GPR kernel for
supervised learning provides the best realistic solution for age prediction
with R squared around 0.97.
|
[{'version': 'v1', 'created': 'Fri, 4 Nov 2022 13:15:55 GMT'}]
|
2022-11-07
|
Phong C.H. Nguyen, Yen-Thi Nguyen, Pradeep K. Seshadri, Joseph B.
Choi, H.S. Udaykumar, and Stephen Baek
|
A physics-aware deep learning model for energy localization in
multiscale shock-to-detonation simulations of heterogeneous energetic
materials
|
Pyrotech. 2023, e202200268
|
10.1002/prep.202200268
| null |
cond-mat.mtrl-sci cs.LG
|
Predictive simulations of the shock-to-detonation transition (SDT) in
heterogeneous energetic materials (EM) are vital to the design and control of
their energy release and sensitivity. Due to the complexity of the
thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid
mesoscale energy localization must be captured accurately. This work proposes
an efficient and accurate multiscale framework for SDT simulations of EM. We
introduce a new approach for SDT simulation by using deep learning to model the
mesoscale energy localization of shock-initiated EM microstructures. The
proposed multiscale modeling framework is divided into two stages. First, a
physics-aware recurrent convolutional neural network (PARC) is used to model
the mesoscale energy localization of shock-initiated heterogeneous EM
microstructures. PARC is trained using direct numerical simulations (DNS) of
hotspot ignition and growth within microstructures of pressed HMX material
subjected to different input shock strengths. After training, PARC is employed
to supply hotspot ignition and growth rates for macroscale SDT simulations. We
show that PARC can play the role of a surrogate model in a multiscale
simulation framework, while drastically reducing the computation cost and
providing improved representations of the sub-grid physics. The proposed
multiscale modeling approach will provide a new tool for material scientists in
designing high-performance and safer energetic materials.
|
[{'version': 'v1', 'created': 'Tue, 8 Nov 2022 21:16:00 GMT'}, {'version': 'v2', 'created': 'Wed, 22 Mar 2023 01:53:41 GMT'}]
|
2023-05-12
|
Marios Mattheakis, Gabriel R. Schleder, Daniel T. Larson, Efthimios
Kaxiras
|
First principles physics-informed neural network for quantum
wavefunctions and eigenvalue surfaces
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.comp-ph
|
Physics-informed neural networks have been widely applied to learn general
parametric solutions of differential equations. Here, we propose a neural
network to discover parametric eigenvalue and eigenfunction surfaces of quantum
systems. We apply our method to solve the hydrogen molecular ion. This is an
ab-initio deep learning method that solves the Schrodinger equation with the
Coulomb potential yielding realistic wavefunctions that include a cusp at the
ion positions. The neural solutions are continuous and differentiable functions
of the interatomic distance and their derivatives are analytically calculated
by applying automatic differentiation. Such a parametric and analytical form of
the solutions is useful for further calculations such as the determination of
force fields.
|
[{'version': 'v1', 'created': 'Tue, 8 Nov 2022 23:22:42 GMT'}, {'version': 'v2', 'created': 'Sun, 13 Nov 2022 19:42:06 GMT'}, {'version': 'v3', 'created': 'Sun, 20 Nov 2022 01:41:11 GMT'}]
|
2022-11-22
|
Yu-Chuan Hsu, Markus J. Buehler
|
DyFraNet: Forecasting and Backcasting Dynamic Fracture Mechanics in
Space and Time Using a 2D-to-3D Deep Neural Network
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall
|
The dynamics of materials failure is one of the most critical phenomena in a
range of scientific and engineering fields, from healthcare to structural
materials to transportation. In this paper we propose a specially designed deep
neural network, DyFraNet, which can predict dynamic fracture behaviors by
identifying a complete history of fracture propagation - from cracking onset,
as a crack grows through the material, modeled as a series of frames evolving
over time and dependent on each other. Furthermore, this model can not only
forecast future fracture processes but also backcast to elucidate the past
fracture history. In this scenario, once provided with the outcome of a
fracture event, the model will elucidate past events that led to this state and
will predict the future evolution of the failure process. By comparing the
predicted results with atomistic-level simulations and theory, we show that
DyFraNet can capture dynamic fracture mechanics by accurately predicting how
cracks develop over time, including measures such as the crack speed, as well
as when cracks become unstable. We use GradCAM to interpret how DyFraNet
perceives the relationship between geometric conditions and fracture dynamics
and we find DyFraNet pays special attention to the areas around crack tips,
which have a critical influence in the early stage of fracture propagation. In
later stages, the model pays increased attention to the existing or newly
formed damage distribution in the material. The proposed approach offers
significant potential to accelerate the exploration of the dynamics in material
design against fracture failures and can be beneficially adapted for all kinds
of dynamical engineering problems.
|
[{'version': 'v1', 'created': 'Tue, 15 Nov 2022 20:19:32 GMT'}]
|
2022-11-17
|
Albert Zhu, Simon Batzner, Albert Musaelian, Boris Kozinsky
|
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
| null |
10.1063/5.0136574
| null |
physics.comp-ph cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Deep learning has emerged as a promising paradigm to give access to highly
accurate predictions of molecular and materials properties. A common
short-coming shared by current approaches, however, is that neural networks
only give point estimates of their predictions and do not come with predictive
uncertainties associated with these estimates. Existing uncertainty
quantification efforts have primarily leveraged the standard deviation of
predictions across an ensemble of independently trained neural networks. This
incurs a large computational overhead in both training and prediction that
often results in order-of-magnitude more expensive predictions. Here, we
propose a method to estimate the predictive uncertainty based on a single
neural network without the need for an ensemble. This allows us to obtain
uncertainty estimates with virtually no additional computational overhead over
standard training and inference. We demonstrate that the quality of the
uncertainty estimates matches those obtained from deep ensembles. We further
examine the uncertainty estimates of our methods and deep ensembles across the
configuration space of our test system and compare the uncertainties to the
potential energy surface. Finally, we study the efficacy of the method in an
active learning setting and find the results to match an ensemble-based
strategy at order-of-magnitude reduced computational cost.
|
[{'version': 'v1', 'created': 'Thu, 17 Nov 2022 20:13:39 GMT'}]
|
2023-05-10
|
Hongyu Yu, Boyu Liu, Yang Zhong, Liangliang Hong, Junyi Ji, Changsong
Xu, Xingao Gong, Hongjun Xiang
|
General time-reversal equivariant neural network potential for magnetic
materials
|
Physical Review B 2024
|
10.1103/PhysRevB.110.104427
|
Phys. Rev. B 110,104427
|
cond-mat.mtrl-sci cs.LG physics.comp-ph
|
This study introduces time-reversal E(3)-equivariant neural network and
SpinGNN++ framework for constructing a comprehensive interatomic potential for
magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic
moments. SpinGNN++ integrates multitask spin equivariant neural network with
explicit spin-lattice terms, including Heisenberg, Dzyaloshinskii-Moriya,
Kitaev, single-ion anisotropy, and biquadratic interactions, and employs
time-reversal equivariant neural network to learn high-order spin-lattice
interactions using time-reversal E(3)-equivariant convolutions. To validate
SpinGNN++, a complex magnetic model dataset is introduced as a benchmark and
employed to demonstrate its capabilities. SpinGNN++ provides accurate
descriptions of the complex spin-lattice coupling in monolayer CrI$_3$ and
CrTe$_2$, achieving sub-meV errors. Importantly, it facilitates large-scale
parallel spin-lattice dynamics, thereby enabling the exploration of associated
properties, including the magnetic ground state and phase transition.
Remarkably, SpinGNN++ identifies a new ferrimagnetic state as the ground
magnetic state for monolayer CrTe2, thereby enriching its phase diagram and
providing deeper insights into the distinct magnetic signals observed in
various experiments.
|
[{'version': 'v1', 'created': 'Mon, 21 Nov 2022 12:25:58 GMT'}, {'version': 'v2', 'created': 'Mon, 19 Dec 2022 07:20:51 GMT'}, {'version': 'v3', 'created': 'Mon, 8 Jan 2024 12:45:12 GMT'}]
|
2025-03-14
|
Xiangrui Yang
|
Leveraging Orbital Information and Atomic Feature in Deep Learning Model
| null | null | null |
cond-mat.mtrl-sci cs.AI cs.LG
|
Predicting material properties base on micro structure of materials has long
been a challenging problem. Recently many deep learning methods have been
developed for material property prediction. In this study, we propose a crystal
representation learning framework, Orbital CrystalNet, OCrystalNet, which
consists of two parts: atomic descriptor generation and graph representation
learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and
atomic features to construct OFM-feature atomic descriptor, and then the atomic
descriptor is used as atom embedding in the atom-bond message passing module
which takes advantage of the topological structure of crystal graphs to learn
crystal representation. To demonstrate the capabilities of OCrystalNet we
performed a number of prediction tasks on Material Project dataset and JARVIS
dataset and compared our model with other baselines and state of art methods.
To further present the effectiveness of OCrystalNet, we conducted ablation
study and case study of our model. The results show that our model have various
advantages over other state of art models.
|
[{'version': 'v1', 'created': 'Sat, 29 Oct 2022 06:22:29 GMT'}]
|
2022-11-22
|
Roberto Perera, Vinamra Agrawal
|
A generalized machine learning framework for brittle crack problems
using transfer learning and graph neural networks
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Despite their recent success, machine learning (ML) models such as graph
neural networks (GNNs), suffer from drawbacks such as the need for large
training datasets and poor performance for unseen cases. In this work, we use
transfer learning (TL) approaches to circumvent the need for retraining with
large datasets. We apply TL to an existing ML framework, trained to predict
multiple crack propagation and stress evolution in brittle materials under
Mode-I loading. The new framework, ACCelerated Universal fRAcTure Emulator
(ACCURATE), is generalized to a variety of crack problems by using a sequence
of TL update steps including (i) arbitrary crack lengths, (ii) arbitrary crack
orientations, (iii) square domains, (iv) horizontal domains, and (v) shear
loadings. We show that using small training datasets of 20 simulations for each
TL update step, ACCURATE achieved high prediction accuracy in Mode-I and
Mode-II stress intensity factors, and crack paths for these problems. %case
studies (i) - (iv). We demonstrate ACCURATE's ability to predict crack growth
and stress evolution with high accuracy for unseen cases involving the
combination of new boundary dimensions with arbitrary crack lengths and crack
orientations in both tensile and shear loading. We also demonstrate
significantly accelerated simulation times of up to 2 orders of magnitude
faster (200x) compared to an XFEM-based fracture model. The ACCURATE framework
provides a universal computational fracture mechanics model that can be easily
modified or extended in future work.
|
[{'version': 'v1', 'created': 'Tue, 22 Nov 2022 18:16:16 GMT'}]
|
2022-11-23
|
Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, Hongjun Xiang
|
Capturing long-range interaction with reciprocal space neural network
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.chem-ph physics.comp-ph
|
Machine Learning (ML) interatomic models and potentials have been widely
employed in simulations of materials. Long-range interactions often dominate in
some ionic systems whose dynamics behavior is significantly influenced.
However, the long-range effect such as Coulomb and Van der Wales potential is
not considered in most ML interatomic potentials. To address this issue, we put
forward a method that can take long-range effects into account for most ML
local interatomic models with the reciprocal space neural network. The
structure information in real space is firstly transformed into reciprocal
space and then encoded into a reciprocal space potential or a global descriptor
with full atomic interactions. The reciprocal space potential and descriptor
keep full invariance of Euclidean symmetry and choice of the cell. Benefiting
from the reciprocal-space information, ML interatomic models can be extended to
describe the long-range potential including not only Coulomb but any other
long-range interaction. A model NaCl system considering Coulomb interaction and
the GaxNy system with defects are applied to illustrate the advantage of our
approach. At the same time, our approach helps to improve the prediction
accuracy of some global properties such as the band gap where the full atomic
interaction beyond local atomic environments plays a very important role. In
summary, our work has expanded the ability of current ML interatomic models and
potentials when dealing with the long-range effect, hence paving a new way for
accurate prediction of global properties and large-scale dynamic simulations of
systems with defects.
|
[{'version': 'v1', 'created': 'Wed, 30 Nov 2022 02:10:48 GMT'}]
|
2022-12-01
|
Andrew J. Lew, Kai Jin, Markus J. Buehler
|
Architected Materials for Mechanical Compression: Design via Simulation,
Deep Learning, and Experimentation
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall
|
Architected materials can achieve enhanced properties compared to their plain
counterparts. Specific architecting serves as a powerful design lever to
achieve targeted behavior without changing the base material. Thus, the
connection between architected structure and resultant properties remains an
open field of great interest to many fields, from aerospace to civil to
automotive applications. Here, we focus on properties related to mechanical
compression, and design hierarchical honeycomb structures to meet specific
values of stiffness and compressive stress. To do so, we employ a combination
of techniques in a singular workflow, starting with molecular dynamics
simulation of the forward design problem, augmenting with data-driven
artificial intelligence models to address the inverse design problem, and
verifying the behavior of de novo structures with experimentation of additively
manufactured samples. We thereby demonstrate an approach for architected design
that is generalizable to multiple material properties and agnostic to the
identity of the base material.
|
[{'version': 'v1', 'created': 'Mon, 5 Dec 2022 22:58:01 GMT'}, {'version': 'v2', 'created': 'Mon, 13 Feb 2023 13:07:25 GMT'}]
|
2023-02-14
|
James P. Horwath, Xiao-Min Lin, Hongrui He, Qingteng Zhang, Eric M.
Dufresne, Miaoqi Chu, Subramanian K. R. S. Sankaranarayanan, Wei Chen, Suresh
Narayanan, Mathew J. Cherukara
|
Elucidation of Relaxation Dynamics Beyond Equilibrium Through
AI-informed X-ray Photon Correlation Spectroscopy
| null | null | null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
Understanding and interpreting dynamics of functional materials \textit{in
situ} is a grand challenge in physics and materials science due to the
difficulty of experimentally probing materials at varied length and time
scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited
for characterizing materials dynamics over wide-ranging time scales, however
spatial and temporal heterogeneity in material behavior can make interpretation
of experimental XPCS data difficult. In this work we have developed an
unsupervised deep learning (DL) framework for automated classification and
interpretation of relaxation dynamics from experimental data without requiring
any prior physical knowledge of the system behavior. We demonstrate how this
method can be used to rapidly explore large datasets to identify samples of
interest, and we apply this approach to directly correlate bulk properties of a
model system to microscopic dynamics. Importantly, this DL framework is
material and process agnostic, marking a concrete step towards autonomous
materials discovery.
|
[{'version': 'v1', 'created': 'Wed, 7 Dec 2022 22:36:53 GMT'}]
|
2022-12-14
|
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo
|
Predicting Thermoelectric Transport Properties from Composition with
Attention-based Deep Learning
| null | null | null |
cond-mat.mtrl-sci
|
Thermoelectric materials can be used to construct devices which recycle waste
heat into electricity. However, the best known thermoelectrics are based on
rare, expensive or even toxic elements, which limits their widespread adoption.
To enable deployment on global scales, new classes of effective thermoelectrics
are thus required. $\textit{Ab initio}$ models of transport properties can help
in the design of new thermoelectrics, but they are still too computationally
expensive to be solely relied upon for high-throughput screening in the vast
chemical space of all possible candidates. Here, we use models constructed with
modern machine learning techniques to scan very large areas of inorganic
materials space for novel thermoelectrics, using composition as an input. We
employ an attention-based deep learning model, trained on data derived from
$\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient,
electrical conductivity, and power factor over a range of temperatures and
$\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good
performance given the simplicity of the input, and with significantly lower
computational cost. The results of applying the model to a space of known and
hypothetical binary and ternary selenides reveal several materials that may
represent promising thermoelectrics. Our study establishes a protocol for
composition-based prediction of thermoelectric behaviour that can be easily
enhanced as more accurate theoretical or experimental databases become
available.
|
[{'version': 'v1', 'created': 'Tue, 13 Dec 2022 09:26:24 GMT'}]
|
2022-12-14
|
Hyun Park, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Emad
Tajkhorshid, Donny Cooper
|
End-to-end AI framework for interpretable prediction of molecular and
crystal properties
|
Mach. Learn.: Sci. Technol. 4 (2023) 025036
|
10.1088/2632-2153/acd434
| null |
cond-mat.mtrl-sci cs.AI cs.LG
|
We introduce an end-to-end computational framework that allows for
hyperparameter optimization using the DeepHyper library, accelerated model
training, and interpretable AI inference. The framework is based on
state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN,
MPNN-transformer, and TorchMD-NET. We employ these AI models along with the
benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict
user-specified material properties within modern computing environments. We
demonstrate transferable applications in the modeling of small molecules,
inorganic crystals and nanoporous metal organic frameworks with a unified,
standalone framework. We have deployed and tested this framework in the
ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the
Delta supercomputer at the National Center for Supercomputing Applications to
provide researchers with modern tools to conduct accelerated AI-driven
discovery in leadership-class computing environments. We release these digital
assets as open source scientific software in GitLab, and ready-to-use Jupyter
notebooks in Google Colab.
|
[{'version': 'v1', 'created': 'Wed, 21 Dec 2022 19:27:51 GMT'}, {'version': 'v2', 'created': 'Mon, 14 Aug 2023 22:45:57 GMT'}]
|
2023-08-16
|
Ashwini Gupta, Anindya Bhaduri, Lori Graham-Brady
|
Accelerated multiscale mechanics modeling in a deep learning framework
| null |
10.1016/j.mechmat.2023.104709
| null |
cond-mat.mtrl-sci
|
Microstructural heterogeneity affects the macro-scale behavior of materials.
Conversely, load distribution at the macro-scale changes the microstructural
response. These up-scaling and down-scaling relations are often modeled using
multiscale finite element (FE) approaches such as FE-squared ($FE^2$). However,
$FE^2$ requires numerous calculations at the micro-scale, which often renders
this approach intractable. This paper reports an enormously faster machine
learning (ML) based approach for multiscale mechanics modeling. The proposed
ML-driven multiscale analysis approach uses an ML-model that predicts the local
stress tensor fields in a linear elastic fiber-reinforced composite
microstructure. This ML-model, specifically a U-Net deep convolutional neural
network (CNN), is trained separately to perform the mapping between the spatial
arrangement of fibers and the corresponding 2D stress tensor fields. This
ML-model provides effective elastic material properties for up-scaling and
local stress tensor fields for subsequent down-scaling in a multiscale analysis
framework. Several numerical examples demonstrate a substantial reduction in
computational cost using the proposed ML-driven approach when compared with the
traditional multiscale modeling approaches such as full-scale FE analysis, and
homogenization based $FE^2$ analysis. This approach has tremendous potential in
efficient multiscale analysis of complex heterogeneous materials, with
applications in uncertainty quantification, design, and optimization.
|
[{'version': 'v1', 'created': 'Fri, 30 Dec 2022 08:57:43 GMT'}]
|
2023-06-13
|
Pan Zhang, Cheng Shang, Zhipan Liu, Ji-Hui Yang, Xin-Gao Gong
|
Origin of performance degradation in high-delithiation Li$_x$CoO$_2$:
insights from direct atomic simulations using global neural network
potentials
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
Li$_x$CoO$_2$ based batteries have serious capacity degradation and safety
issues when cycling at high-delithiation states but full and consistent
mechanisms are still poorly understood. Herein, a global neural network
potential (GNNP) is developed to provide direct theoretical understandings by
performing long-time and large-size atomic simulations. We propose a
self-consistent picture as follows: (i) CoO$_2$ layers are easier to glide with
longer distances at more highly delithiated states, resulting in structural
transitions and structural inhomogeneity; (ii) at regions between different
phases with different Li distributions due to gliding, local strains are
induced and accumulate during cycling processes; (3) accumulated strains cause
the rupture of Li diffusion channels and result in formation of oxygen dimers
during cycling especially when Li has inhomogeneous distributions, leading to
capacity degradations and safety issues. We find that large tensile strains
combined with inhomogeneous distributions of Li ions play critical roles in the
formation processes of blocked Li diffusion channels and the oxygen dimers at
high-delithiation states, which could be the fundamental origins of capacity
degradations and safety issues. Correspondingly, suppressing accumulations of
strains by controlling charge and discharge conditions as well as suppressing
the gliding will be helpful for improving the performance of lithium-ion
batteries (LIBs).
|
[{'version': 'v1', 'created': 'Fri, 30 Dec 2022 12:59:02 GMT'}]
|
2023-01-02
|
Armand Barbot, Riccardo Gatti
|
Unsupervised learning for structure detection in plastically deformed
crystals
|
Computational Materials Science, 2023, 230, pp.112459
|
10.1016/j.commatsci.2023.112459
| null |
cond-mat.mtrl-sci cs.LG
|
Detecting structures at the particle scale within plastically deformed
crystalline materials allows a better understanding of the occurring phenomena.
While previous approaches mostly relied on applying hand-chosen criteria on
different local parameters, these approaches could only detect already known
structures.We introduce an unsupervised learning algorithm to automatically
detect structures within a crystal under plastic deformation. This approach is
based on a study developed for structural detection on colloidal materials.
This algorithm has the advantage of being computationally fast and easy to
implement. We show that by using local parameters based on bond-angle
distributions, we are able to detect more structures and with a higher degree
of precision than traditional hand-made criteria.
|
[{'version': 'v1', 'created': 'Thu, 22 Dec 2022 14:17:32 GMT'}, {'version': 'v2', 'created': 'Tue, 14 May 2024 07:12:35 GMT'}]
|
2024-05-15
|
M. Pietrow, A. Miaskowski
|
Artificial neural network as an effective tool to calculate parameters
of positron annihilation lifetime spectra
|
J. Appl. Phys. 134, 114902 (2023)
|
10.1063/5.0155987
| null |
physics.atom-ph cond-mat.mtrl-sci physics.chem-ph physics.data-an
|
The paper presents the application of the multi-layer perceptron regressor
model for predicting the parameters of positron annihilation lifetime spectra
using the example of alkanes in the solid phase. A good agreement of
calculation results was found when comparing with the commonly used methods.
The presented method can be used as an alternative quick and accurate tool for
decomposition of PALS spectra in general. The advantages and disadvantages of
the new method are discussed.
|
[{'version': 'v1', 'created': 'Wed, 4 Jan 2023 10:18:58 GMT'}]
|
2023-09-20
|
Fatemeh Hafezianzade, Morad Biagooi, SeyedEhsan Nedaaee Oskoee
|
Physics informed neural network for charged particles surrounded by
conductive boundaries
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci math-ph math.MP
|
In this paper, we developed a new PINN-based model to predict the potential
of point-charged particles surrounded by conductive walls. As a result of the
proposed physics-informed neural network model, the mean square error and R2
score are less than 7% and more than 90% for the corresponding example
simulation, respectively. Results have been compared with typical neural
networks and random forest as a standard machine learning algorithm. The R2
score of the random forest model was 70%, and a standard neural network could
not be trained well. Besides, computing time is significantly reduced compared
to the finite element solver.
|
[{'version': 'v1', 'created': 'Thu, 5 Jan 2023 17:52:36 GMT'}]
|
2023-01-06
|
Saugat Kandel, Tao Zhou, Anakha V Babu, Zichao Di, Xinxin Li, Xuedan
Ma, Martin Holt, Antonino Miceli, Charudatta Phatak, and Mathew Cherukara
|
Demonstration of an AI-driven workflow for autonomous high-resolution
scanning microscopy
| null | null | null |
physics.app-ph cond-mat.mtrl-sci physics.ins-det
|
With the continuing advances in scientific instrumentation, scanning
microscopes are now able to image physical systems with up to sub-atomic-level
spatial resolutions and sub-picosecond time resolutions. Commensurately, they
are generating ever-increasing volumes of data, storing and analysis of which
is becoming an increasingly difficult prospect. One approach to address this
challenge is through self-driving experimentation techniques that can actively
analyze the data being collected and use this information to make on-the-fly
measurement choices, such that the data collected is sparse but representative
of the sample and sufficiently informative. Here, we report the Fast Autonomous
Scanning Toolkit (FAST) that combines a trained neural network, a route
optimization technique, and efficient hardware control methods to enable a
self-driving scanning microscopy experiment. The key features of our method are
that: it does not require any prior information about the sample, it has a very
low computational cost, and that it uses generic hardware controls with minimal
experiment-specific wrapping. We test this toolkit in numerical experiments and
a scanning dark-field x-ray microscopy experiment of a $WSe_2$ thin film, where
our experiments show that a FAST scan of <25% of the sample is sufficient to
produce both a high-fidelity image and a quantitative analysis of the surface
distortions in the sample. We show that FAST can autonomously identify all
features of interest in the sample while significantly reducing the scan time,
the volume of data acquired, and dose on the sample. The FAST toolkit is easy
to apply for any scanning microscopy modalities and we anticipate adoption of
this technique will empower broader multi-level studies of the evolution of
physical phenomena with respect to time, temperature, or other experimental
parameters.
|
[{'version': 'v1', 'created': 'Thu, 12 Jan 2023 20:40:43 GMT'}]
|
2023-01-16
|
Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane, Jianjun Hu
|
Discovery of 2D materials using Transformer Network based Generative
Design
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Two-dimensional (2D) materials have wide applications in superconductors,
quantum, and topological materials. However, their rational design is not well
established, and currently less than 6,000 experimentally synthesized 2D
materials have been reported. Recently, deep learning, data-mining, and density
functional theory (DFT)-based high-throughput calculations are widely performed
to discover potential new materials for diverse applications. Here we propose a
generative material design pipeline, namely material transformer
generator(MTG), for large-scale discovery of hypothetical 2D materials. We
train two 2D materials composition generators using self-learning neural
language models based on Transformers with and without transfer learning. The
models are then used to generate a large number of candidate 2D compositions,
which are fed to known 2D materials templates for crystal structure prediction.
Next, we performed DFT computations to study their thermodynamic stability
based on energy-above-hull and formation energy. We report four new
DFT-verified stable 2D materials with zero e-above-hull energies, including
NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential
of our MTG generative materials design pipeline in the discovery of novel 2D
materials and other functional materials.
|
[{'version': 'v1', 'created': 'Sat, 14 Jan 2023 05:59:38 GMT'}]
|
2023-01-18
|
Xiongzhi Zeng, Yi Fan, Jie Liu, Zhenyu Li, Jinlong Yang
|
Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient
Quantum Simulation of Chemical Systems
| null | null | null |
quant-ph cond-mat.mtrl-sci physics.chem-ph
|
The variational quantum eigensolver is a promising way to solve the
Schr\"odinger equation on a noisy intermediate-scale quantum (NISQ) computer,
while its success relies on a well-designed wavefunction ansatz. Compared to
physically motivated ansatzes, hardware heuristic ansatzes usually lead to a
shallower circuit, but it may still be too deep for an NISQ device. Inspired by
the quantum neural network, we propose a new hardware heuristic ansatz where
the circuit depth can be significantly reduced by introducing ancilla qubits,
which makes a practical simulation of a chemical reaction with more than 20
atoms feasible on a currently available quantum computer. More importantly, the
expressibility of this new ansatz can be improved by increasing either the
depth or the width of the circuit, which makes it adaptable to different
hardware environments. These results open a new avenue to develop practical
applications of quantum computation in the NISQ era.
|
[{'version': 'v1', 'created': 'Wed, 18 Jan 2023 14:00:26 GMT'}, {'version': 'v2', 'created': 'Thu, 19 Jan 2023 11:42:14 GMT'}]
|
2023-01-20
|
Francesco Guidarelli Mattioli, Francesco Sciortino, John Russo
|
A neural network potential with self-trained atomic fingerprints: a test
with the mW water potential
| null |
10.1063/5.0139245
| null |
cond-mat.soft cond-mat.dis-nn cond-mat.mtrl-sci
|
We present a neural network (NN) potential based on a new set of atomic
fingerprints built upon two- and three-body contributions that probe distances
and local orientational order respectively. Compared to existing NN potentials,
the atomic fingerprints depend on a small set of tuneable parameters which are
trained together with the neural network weights. To tackle the simultaneous
training of the atomic fingerprint parameters and neural network weights we
adopt an annealing protocol that progressively cycles the learning rate,
significantly improving the accuracy of the NN potential. We test the
performance of the network potential against the mW model of water, which is a
classical three-body potential that well captures the anomalies of the liquid
phase. Trained on just three state points, the NN potential is able to
reproduce the mW model in a very wide range of densities and temperatures, from
negative pressures to several GPa, capturing the transition from an open random
tetrahedral network to a dense interpenetrated network. The NN potential also
reproduces very well properties for which it was not explicitly trained, such
as dynamical properties and the structure of the stable crystalline phases of
mW.
|
[{'version': 'v1', 'created': 'Fri, 27 Jan 2023 09:28:08 GMT'}]
|
2023-03-22
|
Soumya Sanyal, Arun Kumar Sagotra, Narendra Kumar, Sharad Rathi,
Mohana Krishna, Nagesh Somayajula, Duraivelan Palanisamy, Ram R. Ratnakar,
Suchismita Sanyal, Partha Talukdar, Umesh Waghmare and Janakiraman
Balachandran
|
Potential energy surface prediction of Alumina polymorphs using graph
neural network
| null | null | null |
cond-mat.mtrl-sci
|
The process of design and discovery of new materials can be significantly
expedited and simplified if we can learn effectively from available data. Deep
learning (DL) approaches have recently received a lot of interest for their
ability to speed up the design of novel materials by predicting material
properties with precision close to experiments and ab-initio calculations. The
application of deep learning to predict materials properties measured by
experiments are valuable yet challenging due to the limited amount of
experimental data. Most of the existing approaches to predict properties from
computational data have also been directed towards specific material
properties. In this work, we extend this approach, by proposing Landscape
Crystal Graph Convolution Network(LCGCN), an accurate and transferable deep
learning framework based on graph convolutional networks. LCGCN directly learns
the potential energy surface (PES) from atomic configurations. This approach
can enable transferable models that can predict different material properties.
We apply this framework to bulk crystals (i.e. Al2O3), and test it by
calculating potential energy surfaces at different temperatures and across
different phases of crystal.
|
[{'version': 'v1', 'created': 'Sat, 28 Jan 2023 02:27:47 GMT'}]
|
2023-01-31
|
Bulat N. Galimzyanov, Maria A. Doronina, Anatolii V. Mokshin
|
Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an
Artificial Neural Network
|
Materials 2023, 16(3), 1127
|
10.3390/ma16031127
| null |
cond-mat.mtrl-sci
|
The Arrhenius crossover temperature, $T_{A}$, corresponds to a thermodynamic
state wherein the atomistic dynamics of a liquid becomes heterogeneous and
cooperative; and the activation barrier of diffusion dynamics becomes
temperature-dependent at temperatures below $T_{A}$. The theoretical estimation
of this temperature is difficult for some types of materials, especially
silicates and borates. In these materials, self-diffusion as a function of the
temperature $T$ is reproduced by the Arrhenius law, where the activation
barrier practically independent on the temperature $T$. The purpose of the
present work was to establish the relationship between the Arrhenius crossover
temperature $T_{A}$ and the physical properties of liquids directly related to
their glass-forming ability. Using a machine learning model, the crossover
temperature $T_{A}$ was calculated for silicates, borates, organic compounds
and metal melts of various compositions. The empirical values of the glass
transition temperature $T_{g}$, the melting temperature $T_{m}$, the ratio of
these temperatures $T_{g}/T_{m}$ and the fragility index $m$ were applied as
input parameters. It has been established that the temperatures $T_{g}$ and
$T_{m}$ are significant parameters, whereas their ratio $T_{g}/T_{m}$ and the
fragility index $m$ do not correlate much with the temperature $T_{A}$. An
important result of the present work is the analytical equation relating the
temperatures $T_{g}$, $T_{m}$ and $T_{A}$, and that, from the algebraic point
of view, is the equation for a second-order curved surface. It was shown that
this equation allows one to correctly estimate the temperature $T_{A}$ for a
large class of materials, regardless of their compositions and glass-forming
abilities.
|
[{'version': 'v1', 'created': 'Sat, 28 Jan 2023 18:11:55 GMT'}]
|
2023-01-31
|
Fabrice Roncoroni, Ana Sanz-Matias, Siddharth Sundararaman, David
Prendergast
|
Unsupervised learning of representative local atomic arrangements in
molecular dynamics data
| null |
10.1039/D3CP00525A
| null |
cond-mat.mtrl-sci physics.chem-ph physics.comp-ph
|
Molecular dynamics (MD) simulations present a data-mining challenge, given
that they can generate a considerable amount of data but often rely on limited
or biased human interpretation to examine their information content. By not
asking the right questions of MD data we may miss critical information hidden
within it. We combine dimensionality reduction (UMAP) and unsupervised
hierarchical clustering (HDBSCAN) to quantitatively characterize the
coordination environment of chemical species within MD data. By focusing on
local coordination, we significantly reduce the amount of data to be analyzed
by extracting all distinct molecular formulas within a given coordination
sphere. We then efficiently combine UMAP and HDBSCAN with alignment or
shape-matching algorithms to classify these formulas into distinct structural
isomer families. The outcome is a quantitative mapping of the multiple
coordination environments present in the MD data. The method was employed to
reveal details of cation coordination in electrolytes based on molecular
liquids.
|
[{'version': 'v1', 'created': 'Thu, 2 Feb 2023 23:37:19 GMT'}]
|
2023-05-09
|
Chuannan Li, Hanpu Liang, Xie Zhang, Zijing Lin, Su-Huai Wei
|
Graph deep learning accelerated efficient crystal structure search and
feature extraction
| null | null | null |
cond-mat.mtrl-sci
|
Structural search and feature extraction are a central subject in modern
materials design, the efficiency of which is currently limited, but can be
potentially boosted by machine learning (ML). Here, we develop an ML-based
prediction-analysis framework, which includes a symmetry-based combinatorial
crystal optimization program (SCCOP) and a feature additive attribution model,
to significantly reduce computational costs and to extract property-related
structural features. Our method is highly accurate and predictive, and extracts
structural features from desired structures to guide materials design. As a
case study, we apply our new approach to a two-dimensional B-C-N system, which
identifies 28 previously undiscovered stable structures out of 82 compositions;
our analysis further establishes the structural features that contribute most
to energy and bandgap. Compared to conventional approaches, SCCOP is about 10
times faster while maintaining a comparable accuracy. Our new framework is
generally applicable to all types of systems for precise and efficient
structural search, providing new insights into the relationship between
ML-extracted structural features and physical properties.
|
[{'version': 'v1', 'created': 'Tue, 7 Feb 2023 09:14:52 GMT'}]
|
2023-02-08
|
Brenden W. Hamilton, Pilsun Yoo, Michael N. Sakano, Md Mahbubul Islam,
Alejandro Strachan
|
High Pressure and Temperature Neural Network Reactive Force Field for
Energetic Materials
| null |
10.1063/5.0146055
| null |
cond-mat.mtrl-sci physics.chem-ph
|
Reactive force fields for molecular dynamics have enabled a wide range of
studies in numerous material classes. These force fields are computationally
inexpensive as compared to electronic structure calculations and allow for
simulations of millions of atoms. However, the accuracy of traditional force
fields is limited by their functional forms, preventing continual refinement
and improvement. Therefore, we develop a neural network based reactive
interatomic potential for the prediction of the mechanical, thermal, and
chemical response of energetic materials at extreme conditions for energetic
materials. The training set is expanded in an automatic iterative approach and
consists of various CHNO materials and their reactions under ambient and under
shock loading conditions. This new potential shows improved accuracy over the
current state of the art force fields for a wide range of properties such as
detonation performance, decomposition product formation, and vibrational
spectra under ambient and shock loading conditions.
|
[{'version': 'v1', 'created': 'Thu, 9 Feb 2023 19:26:33 GMT'}]
|
2023-04-26
|
Zechen Tang, He Li, Peize Lin, Xiaoxun Gong, Gan Jin, Lixin He, Hong
Jiang, Xinguo Ren, Wenhui Duan, Yong Xu
|
Efficient hybrid density functional calculation by deep learning
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Hybrid density functional calculation is indispensable to accurate
description of electronic structure, whereas the formidable computational cost
restricts its broad application. Here we develop a deep equivariant neural
network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian
from self-consistent field calculations of small structures, and apply the
trained neural networks for efficient electronic-structure calculation by
passing the self-consistent iterations. The method is systematically checked to
show high efficiency and accuracy, making the study of large-scale materials
with hybrid-functional accuracy feasible. As an important application, the
DeepH-hybrid method is applied to study large-supercell Moir\'{e} twisted
materials, offering the first case study on how the inclusion of exact exchange
affects flat bands in the magic-angle twisted bilayer graphene.
|
[{'version': 'v1', 'created': 'Thu, 16 Feb 2023 11:08:35 GMT'}]
|
2023-02-17
|
Sayani Majumdar and Ioannis Zeimpekis
|
Back-end and Flexible Substrate Compatible Analog Ferroelectric Field
Effect Transistors for Accurate Online Training in Deep Neural Network
Accelerators
| null | null | null |
cond-mat.mtrl-sci physics.app-ph
|
Online training of deep neural networks (DNN) can be significantly
accelerated by performing in-situ vector matrix multiplication in a crossbar
array of analog memories. However, training accuracies often suffer due to
device non-idealities such as nonlinearity, asymmetry, limited bit precision
and dynamic weight update range within constrained power budget. Here, we
report a three-terminal Ferroelectric-Field-Effect-Transistor based on low
thermal budget processes that can work efficiently as an analog synaptic
transistor. Ferroelectric polymer P(VDF-TrFE) as the gate insulator and 2D
semiconductor MoS2 as the n-type semiconducting channel material makes them
suitable for flexible and wearable substrate integration. The analog
conductance of the FeFETs can be precisely manipulated by employing a
ferroelectric-dielectric layer as the gate stack. The ferroelectric-only
devices show excellent performance as digital non-volatile memory operating at
+-5V while the hybrid ferroelectric-dielectric devices show quasi-continuous
resistive switching resulting from gradual ferroelectric domain rotation,
important for their multibit operation. Analog conductance states of the hybrid
devices allow linearity and symmetry of weight updates and produce a dynamic
conductance range of 104 with >16 reproducible conducting states. Network
training experiments of these FeFETs show >96% classification accuracy with
MNIST handwritten datasets highlighting their potential for implementation in
scaled DNN architectures.
|
[{'version': 'v1', 'created': 'Thu, 23 Feb 2023 13:45:14 GMT'}]
|
2023-02-24
|
Matthew Helmi Leth Larsen (1), William Bang Lomholdt (2), Cuauhtemoc
Nu\~nez Valencia (1), Thomas W. Hansen (2), Jakob Schi{\o}tz (1) ((1)
Department of Physics Technical University of Denmark, (2) National Center
for Nano Fabrication and Characterization Technical University of Denmark)
|
Quantifying Noise Limitations of Neural Network Segmentations in
High-Resolution Transmission Electron Microscopy
|
Ultramicroscopy 253, (2023) 113803
|
10.1016/j.ultramic.2023.113803
| null |
cond-mat.mtrl-sci
|
Motivated by the need for low electron dose transmission electron microscopy
imaging, we report the optimal frame dose (i.e. $e^-/A^{2}$) range for object
detection and segmentation tasks with neural networks. The MSD-net architecture
shows promising abilities over the industry standard U-net architecture in
generalising to frame doses below the range included in the training set, for
both simulated and experimental images. It also presents a heightened ability
to learn from lower dose images. The MSD-net displays mild visibility of a Au
nanoparticle at 20-30 $e^-/A^{2}$, and converges at 200 $e^-/A^{2}$ where a
full segmentation of the nanoparticle is achieved. Between 30 and 200
$e^-/A^{2}$ object detection applications are still possible. This work also
highlights the importance of modelling the modulation transfer function when
training with simulated images for applications on images acquired with
scintillator based detectors such as the Gatan Oneview camera. A parametric
form of the modulation transfer function is applied with varying ranges of
parameters, and the effects on low electron dose segmentation is presented.
|
[{'version': 'v1', 'created': 'Fri, 24 Feb 2023 13:46:05 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2023 14:32:24 GMT'}]
|
2023-09-26
|
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han,
Christopher J. Bartel, Gerbrand Ceder
|
CHGNet: Pretrained universal neural network potential for
charge-informed atomistic modeling
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
The simulation of large-scale systems with complex electron interactions
remains one of the greatest challenges for the atomistic modeling of materials.
Although classical force fields often fail to describe the coupling between
electronic states and ionic rearrangements, the more accurate
\textit{ab-initio} molecular dynamics suffers from computational complexity
that prevents long-time and large-scale simulations, which are essential to
study many technologically relevant phenomena, such as reactions, ion
migrations, phase transformations, and degradation.
In this work, we present the Crystal Hamiltonian Graph neural Network
(CHGNet) as a novel machine-learning interatomic potential (MLIP), using a
graph-neural-network-based force field to model a universal potential energy
surface. CHGNet is pretrained on the energies, forces, stresses, and magnetic
moments from the Materials Project Trajectory Dataset, which consists of over
10 years of density functional theory static and relaxation trajectories of
$\sim 1.5$ million inorganic structures. The explicit inclusion of magnetic
moments enables CHGNet to learn and accurately represent the orbital occupancy
of electrons, enhancing its capability to describe both atomic and electronic
degrees of freedom. We demonstrate several applications of CHGNet in
solid-state materials, including charge-informed molecular dynamics in
Li$_x$MnO$_2$, the finite temperature phase diagram for Li$_x$FePO$_4$ and Li
diffusion in garnet conductors. We critically analyze the significance of
including charge information for capturing appropriate chemistry, and we
provide new insights into ionic systems with additional electronic degrees of
freedom that can not be observed by previous MLIPs.
|
[{'version': 'v1', 'created': 'Tue, 28 Feb 2023 01:30:06 GMT'}, {'version': 'v2', 'created': 'Tue, 20 Jun 2023 21:27:36 GMT'}]
|
2023-06-22
|
Qichen Xu, I. P. Miranda, Manuel Pereiro, Filipp N. Rybakov, Danny
Thonig, Erik Sj\"oqvist, Pavel Bessarab, Anders Bergman, Olle Eriksson, Pawel
Herman, Anna Delin
|
Metaheuristic conditional neural network for harvesting skyrmionic
metastable states
| null |
10.1103/PhysRevResearch.5.043199
| null |
physics.comp-ph cond-mat.mtrl-sci cs.AI cs.LG
|
We present a metaheuristic conditional neural-network-based method aimed at
identifying physically interesting metastable states in a potential energy
surface of high rugosity. To demonstrate how this method works, we identify and
analyze spin textures with topological charge $Q$ ranging from 1 to $-13$
(where antiskyrmions have $Q<0$) in the Pd/Fe/Ir(111) system, which we model
using a classical atomistic spin Hamiltonian based on parameters computed from
density functional theory. To facilitate the harvest of relevant spin textures,
we make use of the newly developed Segment Anything Model (SAM). Spin textures
with $Q$ ranging from $-3$ to $-6$ are further analyzed using
finite-temperature spin-dynamics simulations. We observe that for temperatures
up to around 20\,K, lifetimes longer than 200\,ps are predicted, and that when
these textures decay, new topological spin textures are formed. We also find
that the relative stability of the spin textures depend linearly on the
topological charge, but only when comparing the most stable antiskyrmions for
each topological charge. In general, the number of holes (i.e.,
non-self-intersecting curves that define closed domain walls in the structure)
in the spin texture is an important predictor of stability -- the more holes,
the less stable is the texture. Methods for systematic identification and
characterization of complex metastable skyrmionic textures -- such as the one
demonstrated here -- are highly relevant for advancements in the field of
topological spintronics.
|
[{'version': 'v1', 'created': 'Mon, 6 Mar 2023 04:04:19 GMT'}, {'version': 'v2', 'created': 'Mon, 29 May 2023 14:13:15 GMT'}]
|
2023-12-05
|
Noah Hoffmann (1), Jonathan Schmidt (2,1), Silvana Botti (2), Miguel
A. L. Marques (1) ((1) Institut f\"ur Physik, Martin-Luther-Universit\"at
Halle-Wittenberg, D-06099 Halle, Germany, (2) Institut f\"ur
Festk\"orpertheorie und -optik, Friedrich-Schiller-Universit\"at Jena,
Max-Wien-Platz 1, 07743 Jena, Germany)
|
Transfer learning on large datasets for the accurate prediction of
material properties
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Graph neural networks trained on large crystal structure databases are
extremely effective in replacing ab initio calculations in the discovery and
characterization of materials. However, crystal structure datasets comprising
millions of materials exist only for the Perdew-Burke-Ernzerhof (PBE)
functional. In this work, we investigate the effectiveness of transfer learning
to extend these models to other density functionals. We show that pre-training
significantly reduces the size of the dataset required to achieve chemical
accuracy and beyond. We also analyze in detail the relationship between the
transfer-learning performance and the size of the datasets used for the initial
training of the model and transfer learning. We confirm a linear dependence of
the error on the size of the datasets on a log-log scale, with a similar slope
for both training and the pre-training datasets. This shows that further
increasing the size of the pre-training dataset, i.e. performing additional
calculations with a low-cost functional, is also effective, through transfer
learning, in improving machine-learning predictions with the quality of a more
accurate, and possibly computationally more involved functional. Lastly, we
compare the efficacy of interproperty and intraproperty transfer learning.
|
[{'version': 'v1', 'created': 'Mon, 6 Mar 2023 09:56:06 GMT'}]
|
2023-03-07
|
Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na,
Chanyoung Park
|
Predicting Density of States via Multi-modal Transformer
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.comp-ph
|
The density of states (DOS) is a spectral property of materials, which
provides fundamental insights on various characteristics of materials. In this
paper, we propose a model to predict the DOS by reflecting the nature of DOS:
DOS determines the general distribution of states as a function of energy.
Specifically, we integrate the heterogeneous information obtained from the
crystal structure and the energies via multi-modal transformer, thereby
modeling the complex relationships between the atoms in the crystal structure,
and various energy levels. Extensive experiments on two types of DOS, i.e.,
Phonon DOS and Electron DOS, with various real-world scenarios demonstrate the
superiority of DOSTransformer. The source code for DOSTransformer is available
at https://github.com/HeewoongNoh/DOSTransformer.
|
[{'version': 'v1', 'created': 'Mon, 13 Mar 2023 10:57:35 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Apr 2023 04:16:58 GMT'}]
|
2023-04-11
|
Michael Kilgour, Jutta Rogal, Mark Tuckerman
|
Geometric Deep Learning for Molecular Crystal Structure Prediction
| null |
10.1021/acs.jctc.3c00031
| null |
cond-mat.mtrl-sci cs.LG physics.chem-ph physics.comp-ph
|
We develop and test new machine learning strategies for accelerating
molecular crystal structure ranking and crystal property prediction using tools
from geometric deep learning on molecular graphs. Leveraging developments in
graph-based learning and the availability of large molecular crystal datasets,
we train models for density prediction and stability ranking which are
accurate, fast to evaluate, and applicable to molecules of widely varying size
and composition. Our density prediction model, MolXtalNet-D, achieves state of
the art performance, with lower than 2% mean absolute error on a large and
diverse test dataset. Our crystal ranking tool, MolXtalNet-S, correctly
discriminates experimental samples from synthetically generated fakes and is
further validated through analysis of the submissions to the Cambridge
Structural Database Blind Tests 5 and 6. Our new tools are computationally
cheap and flexible enough to be deployed within an existing crystal structure
prediction pipeline both to reduce the search space and score/filter crystal
candidates.
|
[{'version': 'v1', 'created': 'Fri, 17 Mar 2023 17:27:47 GMT'}]
|
2024-07-29
|
Vadim Korolev and Pavel Protsenko
|
Toward Accurate Interpretable Predictions of Materials Properties within
Transformer Language Models
| null |
10.1016/j.patter.2023.100803
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Property prediction accuracy has long been a key parameter of machine
learning in materials informatics. Accordingly, advanced models showing
state-of-the-art performance turn into highly parameterized black boxes missing
interpretability. Here, we present an elegant way to make their reasoning
transparent. Human-readable text-based descriptions automatically generated
within a suite of open-source tools are proposed as materials representation.
Transformer language models pretrained on 2 million peer-reviewed articles take
as input well-known terms, e.g., chemical composition, crystal symmetry, and
site geometry. Our approach outperforms crystal graph networks by classifying
four out of five analyzed properties if one considers all available reference
data. Moreover, fine-tuned text-based models show high accuracy in the
ultra-small data limit. Explanations of their internal machinery are produced
using local interpretability techniques and are faithful and consistent with
domain expert rationales. This language-centric framework makes accurate
property predictions accessible to people without artificial-intelligence
expertise.
|
[{'version': 'v1', 'created': 'Tue, 21 Mar 2023 20:33:12 GMT'}]
|
2023-08-03
|
Daniel R. Cassar
|
GlassNet: a multitask deep neural network for predicting many glass
properties
|
Ceramics International 49 (2023) 36013-36024
|
10.1016/j.ceramint.2023.08.281
| null |
cond-mat.soft cond-mat.mtrl-sci
|
A multitask deep neural network model was trained on more than 218k different
glass compositions. This model, called GlassNet, can predict 85 different
properties (such as optical, electrical, dielectric, mechanical, and thermal
properties, as well as density, viscosity/relaxation, crystallization, surface
tension, and liquidus temperature) of glasses and glass-forming liquids of
different chemistries (such as oxides, chalcogenides, halides, and others). The
model and the data used to train it are available in the GlassPy Python module
as free and open source software for the community to use and build upon. As a
proof of concept, GlassNet was used with the MYEGA viscosity equation to
predict the temperature dependence of viscosity and outperformed another
general purpose viscosity model available in the literature (ViscNet) on unseen
data. An explainable AI algorithm (SHAP) was used to extract knowledge
correlating the input (physicochemical information) and output (glass
properties) of the model, providing valuable insights for glass manufacturing
and design. It is hoped that GlassNet, with its free and open source nature,
can be used to enable faster and better computer-aided design of new
technological glasses.
|
[{'version': 'v1', 'created': 'Mon, 27 Mar 2023 18:36:41 GMT'}, {'version': 'v2', 'created': 'Fri, 25 Aug 2023 18:20:44 GMT'}, {'version': 'v3', 'created': 'Mon, 20 Nov 2023 18:55:16 GMT'}]
|
2023-11-21
|
Shun Muroga, Yasuaki Miki, and Kenji Hata
|
A Comprehensive and Versatile Multimodal Deep Learning Approach for
Predicting Diverse Properties of Advanced Materials
|
Adv. Sci.,10, 24, 2302508 (2023)
|
10.1002/advs.202302508
| null |
cond-mat.soft cond-mat.mtrl-sci cs.AI cs.LG
|
We present a multimodal deep learning (MDL) framework for predicting physical
properties of a 10-dimensional acrylic polymer composite material by merging
physical attributes and chemical data. Our MDL model comprises four modules,
including three generative deep learning models for material structure
characterization and a fourth model for property prediction. Our approach
handles an 18-dimensional complexity, with 10 compositional inputs and 8
property outputs, successfully predicting 913,680 property data points across
114,210 composition conditions. This level of complexity is unprecedented in
computational materials science, particularly for materials with undefined
structures. We propose a framework to analyze the high-dimensional information
space for inverse material design, demonstrating flexibility and adaptability
to various materials and scales, provided sufficient data is available. This
study advances future research on different materials and the development of
more sophisticated models, drawing us closer to the ultimate goal of predicting
all properties of all materials.
|
[{'version': 'v1', 'created': 'Wed, 29 Mar 2023 02:42:17 GMT'}]
|
2023-11-28
|
Qing-Jie Li, Mahmut Nedim Cinbiz, Yin Zhang, Qi He, Geoffrey
Beausoleil II, Ju Li
|
Robust Deep Learning Framework for Constitutive-Relation Modeling
| null | null | null |
cond-mat.mtrl-sci
|
Modeling the full-range deformation behaviors of materials under complex
loading and materials conditions is a significant challenge for constitutive
relations (CRs) modeling. We propose a general encoder-decoder deep learning
framework that can model high-dimensional stress-strain data and complex
loading histories with robustness and universal capability. The framework
employs an encoder to project high-dimensional input information (e.g., loading
history, loading conditions, and materials information) to a lower-dimensional
hidden space and a decoder to map the hidden representation to the stress of
interest. We evaluated various encoder architectures, including gated recurrent
unit (GRU), GRU with attention, temporal convolutional network (TCN), and the
Transformer encoder, on two complex stress-strain datasets that were designed
to include a wide range of complex loading histories and loading conditions.
All architectures achieved excellent test results with an RMSE below 1 MPa.
Additionally, we analyzed the capability of the different architectures to make
predictions on out-of-domain applications, with an uncertainty estimation based
on deep ensembles. The proposed approach provides a robust alternative to
empirical/semi-empirical models for CRs modeling, offering the potential for
more accurate and efficient materials design and optimization.
|
[{'version': 'v1', 'created': 'Sun, 2 Apr 2023 20:13:25 GMT'}]
|
2023-04-04
|
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben
Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, A. Al-Najjar, Christina Doty,
Sarah Akers, Nageswara S. Rao, Joshua C. Agar, Steven R. Spurgeon
|
Deep Learning for Automated Experimentation in Scanning Transmission
Electron Microscopy
| null |
10.1038/s41524-023-01142-0
| null |
cond-mat.mtrl-sci cs.LG
|
Machine learning (ML) has become critical for post-acquisition data analysis
in (scanning) transmission electron microscopy, (S)TEM, imaging and
spectroscopy. An emerging trend is the transition to real-time analysis and
closed-loop microscope operation. The effective use of ML in electron
microscopy now requires the development of strategies for microscopy-centered
experiment workflow design and optimization. Here, we discuss the associated
challenges with the transition to active ML, including sequential data analysis
and out-of-distribution drift effects, the requirements for the edge operation,
local and cloud data storage, and theory in the loop operations. Specifically,
we discuss the relative contributions of human scientists and ML agents in the
ideation, orchestration, and execution of experimental workflows and the need
to develop universal hyper languages that can apply across multiple platforms.
These considerations will collectively inform the operationalization of ML in
next-generation experimentation.
|
[{'version': 'v1', 'created': 'Tue, 4 Apr 2023 18:01:56 GMT'}]
|
2023-11-10
|
Bin Xing, Timothy J. Rupert, Xiaoqing Pan, Penghui Cao
|
Neural Network Kinetics for Exploring Diffusion Multiplicity and
Chemical Ordering in Compositionally Complex Materials
|
Nat Commun 15, 3879 (2024)
|
10.1038/s41467-024-47927-9
| null |
cond-mat.dis-nn cond-mat.mtrl-sci
|
Diffusion involving atom transport from one location to another governs many
important processes and behaviors such as precipitation and phase nucleation.
Local chemical complexity in compositionally complex alloys poses challenges
for modeling atomic diffusion and the resulting formation of chemically ordered
structures. Here, we introduce a neural network kinetics (NNK) scheme that
predicts and simulates diffusion-induced chemical and structural evolution in
complex concentrated chemical environments. The framework is grounded on
efficient on-lattice structure and chemistry representation combined with
neural networks, enabling precise prediction of all path-dependent migration
barriers and individual atom jumps. Using this method, we study the
temperature-dependent local chemical ordering in a refractory Nb-Mo-Ta alloy
and reveal a critical temperature at which the B2 order reaches a maximum. Our
atomic jump randomness map exhibits the highest diffusion heterogeneity
(multiplicity) in the vicinity of this characteristic temperature, which is
closely related to chemical ordering and B2 structure formation. The scalable
NNK framework provides a promising new avenue to exploring diffusion-related
properties in the vast compositional space within which extraordinary
properties are hidden.
|
[{'version': 'v1', 'created': 'Thu, 6 Apr 2023 09:35:31 GMT'}, {'version': 'v2', 'created': 'Sat, 6 Apr 2024 22:02:02 GMT'}]
|
2024-05-10
|
Peichen Zhong, Bowen Deng, Tanjin He, Zhengyan Lun, Gerbrand Ceder
|
Deep learning of experimental electrochemistry for battery cathodes
across diverse compositions
| null |
10.1016/j.joule.2024.03.010
| null |
cond-mat.mtrl-sci
|
Artificial intelligence (AI) has emerged as a tool for discovering and
optimizing novel battery materials. However, the adoption of AI in battery
cathode representation and discovery is still limited due to the complexity of
optimizing multiple performance properties and the scarcity of high-fidelity
data. In this study, we present a machine-learning model (DRXNet) for battery
informatics and demonstrate the application in the discovery and optimization
of disordered rocksalt (DRX) cathode materials. We have compiled the
electrochemistry data of DRX cathodes over the past five years, resulting in a
dataset of more than 19,000 discharge voltage profiles on diverse chemistries
spanning 14 different metal species. Learning from this extensive dataset, our
DRXNet model can automatically capture critical features in the cycling curves
of DRX cathodes under various conditions. Illustratively, the model gives
rational predictions of the discharge capacity for diverse compositions in the
Li--Mn--O--F chemical space as well as for high-entropy systems. As a universal
model trained on diverse chemistries, our approach offers a data-driven
solution to facilitate the rapid identification of novel cathode materials,
accelerating the development of next-generation batteries for carbon
neutralization.
|
[{'version': 'v1', 'created': 'Tue, 11 Apr 2023 05:09:48 GMT'}, {'version': 'v2', 'created': 'Thu, 21 Sep 2023 07:01:47 GMT'}, {'version': 'v3', 'created': 'Tue, 2 Apr 2024 21:08:37 GMT'}]
|
2024-05-14
|
Teng Yang, Zefeng Cai, Zhengtao Huang, Wenlong Tang, Ruosong Shi, Andy
Godfrey, Hanxing Liu, Yuanhua Lin, Ce-Wen Nan, Meng Ye, LinFeng Zhang, Han
Wang, Ben Xu
|
Deep Learning Illuminates Spin and Lattice Interaction in Magnetic
Materials
| null | null | null |
cond-mat.mtrl-sci
|
Atomistic simulations hold significant value in clarifying crucial phenomena
such as phase transitions and energy transport in materials science. Their
success stems from the presence of potential energy functions capable of
accurately depicting the relationship between system energy and lattice
changes. In magnetic materials, two atomic scale degrees of freedom come into
play: the lattice and the spin. However, accurately tracing the simultaneous
evolution of both lattice and spin in magnetic materials at an atomic scale is
a substantial challenge. This is largely due to the complexity involved in
depicting the interaction energy precisely, and its influence on lattice and
spin-driving forces, such as atomic force and magnetic torque, which continues
to be a daunting task in computational science. Addressing this deficit, we
present DeepSPIN, a versatile approach that generates high-precision predictive
models of energy, atomic forces, and magnetic torque in magnetic systems. This
is achieved by integrating first-principles calculations of magnetic excited
states with deep learning techniques via active learning. We thoroughly explore
the methodology, accuracy, and scalability of our proposed model in this paper.
Our technique adeptly connects first-principles computations and atomic-scale
simulations of magnetic materials. This synergy presents opportunities to
utilize these calculations in devising and tackling theoretical and practical
obstacles concerning magnetic materials.
|
[{'version': 'v1', 'created': 'Wed, 19 Apr 2023 12:24:56 GMT'}, {'version': 'v2', 'created': 'Wed, 14 Jun 2023 02:38:48 GMT'}, {'version': 'v3', 'created': 'Fri, 18 Aug 2023 05:47:37 GMT'}]
|
2023-08-21
|
Qidong Lin and Bin Jiang
|
First-Principles Modeling of Equilibration Dynamics of Hyperthermal
Products of Surface Reactions Using Scalable Neural Network Potential
| null | null | null |
cond-mat.mtrl-sci cond-mat.other
|
Equilibration dynamics of hot oxygen atoms following O2 dissociation on
Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations
based on a scalable neural network potential enabling first-principles
description of O2 and O interacting with variable Pd supercells. We find that
to accurately describe the equilibration dynamics after dissociation, the
simulation cell length necessarily exceeds twice the maximum distance of
equilibrated oxygen adsorbates. By analyzing hundreds of trajectories with
appropriate initial sampling, the measured distance distribution of
equilibrated atom pairs on Pd(111) is well reproduced. However, our results on
Pd(100) suggest that the ballistic motion of hot atoms predicted previously is
a rare event under ideal conditions, while initial molecular orientation and
surface thermal fluctuation could significantly affect the overall
post-dissociation dynamics. On both surfaces, dissociated oxygen atoms remain
primarily locate their nascent positions and then randomly cross bridge sites
nearby.
|
[{'version': 'v1', 'created': 'Fri, 21 Apr 2023 08:42:34 GMT'}]
|
2023-04-24
|
Rachel K. Luu, Marcin Wysokowski, Markus J. Buehler
|
Generative Discovery of Novel Chemical Designs using Diffusion Modeling
and Transformer Deep Neural Networks with Application to Deep Eutectic
Solvents
| null |
10.1063/5.0155890
| null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech
|
We report a series of deep learning models to solve complex forward and
inverse design problems in molecular modeling and design. Using both diffusion
models inspired by nonequilibrium thermodynamics and attention-based
transformer architectures, we demonstrate a flexible framework to capture
complex chemical structures. First trained on the QM9 dataset and a series of
quantum mechanical properties (e.g. homo, lumo, free energy, heat capacity,
etc.), we then generalize the model to study and design key properties of deep
eutectic solvents. In addition to separate forward and inverse models, we also
report an integrated fully prompt-based multi-task generative pretrained
transformer model that solves multiple forward, inverse design, and prediction
tasks, flexibly and within one model. We show that the multi-task generative
model has the overall best performance and allows for flexible integration of
multiple objectives, within one model, and for distinct chemistries, suggesting
that synergies emerge during training of this large language model. Trained
jointly in tasks related to the QM9 dataset and deep eutectic solvents (DESs),
the model can predict various quantum mechanical properties and critical
properties to achieve deep eutectic solvent behavior. Several novel
combinations of DESs are proposed based on this framework.
|
[{'version': 'v1', 'created': 'Mon, 24 Apr 2023 19:17:38 GMT'}]
|
2023-06-21
|
Junrong Lin, Mahmudul Hasan, Pinar Acar, Jose Blanchet and Vahid
Tarokh
|
Neural Network Accelerated Process Design of Polycrystalline
Microstructures
| null | null | null |
cs.CE cond-mat.mtrl-sci cs.LG
|
Computational experiments are exploited in finding a well-designed processing
path to optimize material structures for desired properties. This requires
understanding the interplay between the processing-(micro)structure-property
linkages using a multi-scale approach that connects the macro-scale (process
parameters) to meso (homogenized properties) and micro (crystallographic
texture) scales. Due to the nature of the problem's multi-scale modeling setup,
possible processing path choices could grow exponentially as the decision tree
becomes deeper, and the traditional simulators' speed reaches a critical
computational threshold. To lessen the computational burden for predicting
microstructural evolution under given loading conditions, we develop a neural
network (NN)-based method with physics-infused constraints. The NN aims to
learn the evolution of microstructures under each elementary process. Our
method is effective and robust in finding optimal processing paths. In this
study, our NN-based method is applied to maximize the homogenized stiffness of
a Copper microstructure, and it is found to be 686 times faster while achieving
0.053% error in the resulting homogenized stiffness compared to the traditional
finite element simulator on a 10-process experiment.
|
[{'version': 'v1', 'created': 'Tue, 11 Apr 2023 20:35:29 GMT'}, {'version': 'v2', 'created': 'Wed, 3 May 2023 04:07:49 GMT'}]
|
2023-05-04
|
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