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stringdate 2007-09-13 00:00:00
2025-05-15 00:00:00
|
|---|---|---|---|---|---|---|---|---|
Tilman Ki{\ss}linger, Andreas Raabgrund, Begmuhammet Geldiyev,
Maximilian Ammon, Janek Rieger, Jonas Hauner, Lutz Hammer, Thomas Fauster,
and M. Alexander Schneider
|
CuTe chains on Cu(111) by deposition of 1/3 ML Te: atomic and electronic
structure
|
Phys. Rev. B 102, 155422 (2020)
|
10.1103/PhysRevB.102.155422
| null |
cond-mat.mtrl-sci
|
The surface atomic and electronic structure after deposition of 1/3 monolayer
(ML) Te on Cu(111) was determined using a combination of low-energy electron
diffraction (LEED), scanning tunneling microscopy and spectroscopy (STM/STS),
angle-resolved single and two-photon photoelectron spectroscopy (ARPES
/AR-2PPE) and density functional theory (DFT) calculations. Contrary to the
current state in literature Te does not create a two-dimensional surface alloy
but forms Cu$_2$Te$_2$ adsorbate chains in a $\left(2\sqrt{3} \times
\sqrt{3}\right)\textrm{R30}^\circ$ superstructure. We establish this by a
high-precision LEED-IV structural analysis with Pendry $R$ factor of $R =
0.099$ and corroborating DFT and STM results. The electronic structure of the
surface phase is dominated by an anisotropic downward dispersing state at the
Fermi energy $E_F$ and a more isotropic upward dispersing unoccupied state at
$E-E_F = + 1.43\,\textrm{eV}$. Both states coexist with bulk states of the
projected band structure and are therefore surface resonances.
|
[{'version': 'v1', 'created': 'Wed, 30 Sep 2020 12:42:24 GMT'}]
|
2020-11-02
|
Hironori Yoshioka and Tomonori Honda
|
Determination of the Interface between Amorphous Insulator and
Crystalline 4H-SiC in Transmission Electron Microscope Image by using
Convolutional Neural Network
|
AIP Advances 11, 015101 (2021)
|
10.1063/5.0036982
| null |
cond-mat.mtrl-sci cond-mat.dis-nn cs.LG
|
A rough interface seems to be one of the possible reasons for low channel
mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface
roughness, we drew a boundary line between amorphous insulator and crystalline
4H-SiC in a cross-sectional image obtained by a transmission electron
microscope (TEM), by using the deep learning approach of convolutional neural
network (CNN). We show that the CNN model recognizes the interface very well,
even when the interface is too rough to draw the boundary line manually. Power
spectral density of interface roughness was calculated.
|
[{'version': 'v1', 'created': 'Wed, 14 Oct 2020 08:28:01 GMT'}]
|
2021-01-08
|
Shehtab Zaman, Christopher Owen, Kenneth Chiu, Michael Lawler
|
Graph Neural Network for Metal Organic Framework Potential Energy
Approximation
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal
ions and organic linkers. MOFs play an important role in industrial
applications such as gas separation, gas purification, and electrolytic
catalysis. Important MOF properties such as potential energy are currently
computed via techniques such as density functional theory (DFT). Although DFT
provides accurate results, it is computationally costly. We propose a machine
learning approach for estimating the potential energy of candidate MOFs,
decomposing it into separate pair-wise atomic interactions using a graph neural
network. Such a technique will allow high-throughput screening of candidates
MOFs. We also generate a database of 50,000 spatial configurations and
high-quality potential energy values using DFT.
|
[{'version': 'v1', 'created': 'Thu, 29 Oct 2020 19:47:44 GMT'}]
|
2020-11-02
|
Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
|
Deep Learning of Accurate Force Field of Ferroelectric HfO$_2$
|
Phys. Rev. B 103, 024108 (2021)
|
10.1103/PhysRevB.103.024108
| null |
cond-mat.mtrl-sci
|
The discovery of ferroelectricity in HfO$_2$-based thin films opens up new
opportunities for using this silicon-compatible ferroelectric to realize
low-power logic circuits and high-density non-volatile memories. The functional
performances of ferroelectrics are intimately related to their dynamic
responses to external stimuli such as electric fields at finite temperatures.
Molecular dynamics is an ideal technique for investigating dynamical processes
on large length and time scales, though its applications to new materials is
often hindered by the limited availability and accuracy of classical force
fields. Here we present a deep neural network-based interatomic force field of
HfO$_2$ learned from {\em ab initio} data using a concurrent learning
procedure. The model potential is able to predict structural properties such as
elastic constants, equation of states, phonon dispersion relationships, and
phase transition barriers of various hafnia polymorphs with accuracy comparable
with density functional theory calculations. The validity of this model
potential is further confirmed by the reproduction of experimental sequences of
temperature-driven ferroelectric-paraelectric phase transitions of HfO$_2$ with
isobaric-isothermal ensemble molecular dynamics simulations. We suggest a
general approach to extend the model potential of HfO$_2$ to related material
systems including dopants and defects.
|
[{'version': 'v1', 'created': 'Fri, 30 Oct 2020 05:21:20 GMT'}]
|
2021-02-03
|
Yusuf Shaidu, Emine Kucukbenli, Ruggero Lot, Franco Pellegrini,
Efthimios Kaxiras, Stefano de Gironcoli
|
A Systematic Approach to Generating Accurate Neural Network Potentials:
the Case of Carbon
| null | null | null |
cond-mat.mtrl-sci
|
Availability of affordable and widely applicable interatomic potentials is
the key needed to unlock the riches of modern materials modelling. Artificial
neural network based approaches for generating potentials are promising;
however neural network training requires large amounts of data, sampled
adequately from an often unknown potential energy surface. Here we propose a
self-consistent approach that is based on crystal structure prediction
formalism and is guided by unsupervised data analysis, to construct an
accurate, inexpensive and transferable artificial neural network potential.
Using this approach, we construct an interatomic potential for Carbon and
demonstrate its ability to reproduce first principles results on elastic and
vibrational properties for diamond, graphite and graphene, as well as energy
ordering and structural properties of a wide range of crystalline and amorphous
phases.
|
[{'version': 'v1', 'created': 'Mon, 9 Nov 2020 17:58:48 GMT'}]
|
2020-11-10
|
Yinan Wang, Diane Oyen, Weihong (Grace) Guo, Anishi Mehta, Cory Braker
Scott, Nishant Panda, M. Giselle Fern\'andez-Godino, Gowri Srinivasan,
Xiaowei Yue
|
StressNet: Deep Learning to Predict Stress With Fracture Propagation in
Brittle Materials
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Catastrophic failure in brittle materials is often due to the rapid growth
and coalescence of cracks aided by high internal stresses. Hence, accurate
prediction of maximum internal stress is critical to predicting time to failure
and improving the fracture resistance and reliability of materials. Existing
high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are
limited by their high computational cost. Therefore, to reduce computational
cost while preserving accuracy, a novel deep learning model, "StressNet," is
proposed to predict the entire sequence of maximum internal stress based on
fracture propagation and the initial stress data. More specifically, the
Temporal Independent Convolutional Neural Network (TI-CNN) is designed to
capture the spatial features of fractures like fracture path and spall regions,
and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to
capture the temporal features. By fusing these features, the evolution in time
of the maximum internal stress can be accurately predicted. Moreover, an
adaptive loss function is designed by dynamically integrating the Mean Squared
Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the
fluctuations in maximum internal stress. After training, the proposed model is
able to compute accurate multi-step predictions of maximum internal stress in
approximately 20 seconds, as compared to the FDEM run time of 4 hours, with an
average MAPE of 2% relative to test data.
|
[{'version': 'v1', 'created': 'Fri, 20 Nov 2020 05:49:12 GMT'}]
|
2020-11-23
|
Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling,
Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang,
Olivier De Castro, Jean-Nicolas Audinot, Inge Nelissen, Tom Wirtz, Andreas
Maier, Silke Christiansen
|
Synthetic Image Rendering Solves Annotation Problem in Deep Learning
Nanoparticle Segmentation
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.CV eess.IV physics.app-ph
|
Nanoparticles occur in various environments as a consequence of man-made
processes, which raises concerns about their impact on the environment and
human health. To allow for proper risk assessment, a precise and statistically
relevant analysis of particle characteristics (such as e.g. size, shape and
composition) is required that would greatly benefit from automated image
analysis procedures. While deep learning shows impressive results in object
detection tasks, its applicability is limited by the amount of representative,
experimentally collected and manually annotated training data. Here, we present
an elegant, flexible and versatile method to bypass this costly and tedious
data acquisition process. We show that using a rendering software allows to
generate realistic, synthetic training data to train a state-of-the art deep
neural network. Using this approach, we derive a segmentation accuracy that is
comparable to man-made annotations for toxicologically relevant metal-oxide
nanoparticle ensembles which we chose as examples. Our study paves the way
towards the use of deep learning for automated, high-throughput particle
detection in a variety of imaging techniques such as microscopies and
spectroscopies, for a wide variety of studies and applications, including the
detection of plastic micro- and nanoparticles.
|
[{'version': 'v1', 'created': 'Fri, 20 Nov 2020 17:05:36 GMT'}]
|
2020-11-23
|
Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert
|
Supervised deep learning prediction of the formation enthalpy of the
full set of configurations in complex phases: the $\sigma-$phase as an
example
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Machine learning (ML) methods are becoming integral to scientific inquiry in
numerous disciplines, such as material sciences. In this manuscript, we
demonstrate how ML can be used to predict several properties in solid-state
chemistry, in particular the heat of formation of a given complex
crystallographic phase (here the $\sigma-$phase, $tP30$, $D8_{b}$). Based on an
independent and unprecedented large first principles dataset containing about
10,000 $\sigma-$compounds with $n=14$ different elements, we used a supervised
learning approach, to predict all the $\sim$500,000 possible configurations
within a mean absolute error of 23 meV/at ($\sim$2 kJ.mol$^{-1}$) on the heat
of formation and $\sim$0.06 Ang. on the tetragonal cell parameters. We showed
that neural network regression algorithms provide a significant improvement in
accuracy of the predicted output compared to traditional regression techniques.
Adding descriptors having physical nature (atomic radius, number of valence
electrons) improves the learning precision. Based on our analysis, the training
database composed of the only binary-compositions plays a major role in
predicting the higher degree system configurations. Our result opens a broad
avenue to efficient high-throughput investigations of the combinatorial binary
calculation for multicomponent prediction of a complex phase.
|
[{'version': 'v1', 'created': 'Sat, 21 Nov 2020 22:07:15 GMT'}]
|
2020-11-24
|
Qiangqiang Gu, Linfeng Zhang and Ji Feng
|
Neural network representation of electronic structure from $ab$ $initio$
molecular dynamics
|
Science Bulletin 67, 29 (2022)
|
10.1016/j.scib.2021.09.010
| null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Despite their rich information content, electronic structure data amassed at
high volumes in $ab$ $initio$ molecular dynamics simulations are generally
under-utilized. We introduce a transferable high-fidelity neural network
representation of such data in the form of tight-binding Hamiltonians for
crystalline materials. This predictive representation of $ab$ $initio$
electronic structure, combined with machine-learning boosted molecular
dynamics, enables efficient and accurate electronic evolution and sampling.
When applied to a one-dimension charge-density wave material, carbyne, we are
able to compute the spectral function and optical conductivity in the canonical
ensemble. The spectral functions evaluated during soliton-antisoliton pair
annihilation process reveal significant renormalization of low-energy edge
modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer
limit. The availability of an efficient and reusable surrogate model for the
electronic structure dynamical system will enable calculating many interesting
physical properties, paving way to previously inaccessible or challenging
avenues in materials modeling.
|
[{'version': 'v1', 'created': 'Fri, 27 Nov 2020 15:13:13 GMT'}, {'version': 'v2', 'created': 'Mon, 11 Jan 2021 14:02:19 GMT'}, {'version': 'v3', 'created': 'Mon, 21 Feb 2022 04:03:34 GMT'}]
|
2022-02-22
|
Chen Qian, Yunhai Xiong and Xiang Chen
|
Directed Graph Attention Neural Network Utilizing 3D Coordinates for
Molecular Property Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
The prosperity of computer vision (CV) and natural language procession (NLP)
in recent years has spurred the development of deep learning in many other
domains. The advancement in machine learning provides us with an alternative
option besides the computationally expensive density functional theories (DFT).
Kernel method and graph neural networks have been widely studied as two
mainstream methods for property prediction. The promising graph neural networks
have achieved comparable accuracy to the DFT method for specific objects in the
recent study. However, most of the graph neural networks with high precision so
far require fully connected graphs with pairwise distance distribution as edge
information. In this work, we shed light on the Directed Graph Attention Neural
Network (DGANN), which only takes chemical bonds as edges and operates on bonds
and atoms of molecules. DGANN distinguishes from previous models with those
features: (1) It learns the local chemical environment encoding by graph
attention mechanism on chemical bonds. Every initial edge message only flows
into every message passing trajectory once. (2) The transformer blocks
aggregate the global molecular representation from the local atomic encoding.
(3) The position vectors and coordinates are used as inputs instead of
distances. Our model has matched or outperformed most baseline graph neural
networks on QM9 datasets even without thorough hyper-parameters searching.
Moreover, this work suggests that models directly utilizing 3D coordinates can
still reach high accuracies for molecule representation even without rotational
and translational invariance incorporated.
|
[{'version': 'v1', 'created': 'Tue, 1 Dec 2020 11:06:40 GMT'}]
|
2020-12-02
|
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
|
Leveraging Uncertainty from Deep Learning for Trustworthy Materials
Discovery Workflows
| null | null | null |
cond-mat.mtrl-sci cs.CV cs.LG physics.app-ph
|
In this paper, we leverage predictive uncertainty of deep neural networks to
answer challenging questions material scientists usually encounter in machine
learning based materials applications workflows. First, we show that by
leveraging predictive uncertainty, a user can determine the required training
data set size necessary to achieve a certain classification accuracy. Next, we
propose uncertainty guided decision referral to detect and refrain from making
decisions on confusing samples. Finally, we show that predictive uncertainty
can also be used to detect out-of-distribution test samples. We find that this
scheme is accurate enough to detect a wide range of real-world shifts in data,
e.g., changes in the image acquisition conditions or changes in the synthesis
conditions. Using microstructure information from scanning electron microscope
(SEM) images as an example use case, we show that leveraging uncertainty-aware
deep learning can significantly improve the performance and dependability of
classification models.
|
[{'version': 'v1', 'created': 'Wed, 2 Dec 2020 19:34:16 GMT'}, {'version': 'v2', 'created': 'Thu, 22 Apr 2021 23:29:30 GMT'}]
|
2021-04-26
|
Debjyoti Bhattacharya and Tarak K Patra
|
dPOLY: Deep Learning of Polymer Phases and Phase Transition
| null |
10.1021/acs.macromol.0c02655
| null |
cond-mat.soft cond-mat.mtrl-sci
|
Machine learning (ML) and artificial intelligence (AI) have the remarkable
ability to classify, recognize, and characterize complex patterns and trends in
large data sets. Here, we adopt a subclass of machine learning methods viz.,
deep learnings and develop a general-purpose AI tool - dPOLY for analyzing
molecular dynamics trajectory and predicting phases and phase transitions in
polymers. An unsupervised deep neural network is used within this framework to
map a molecular dynamics trajectory undergoing thermophysical treatment such as
cooling, heating, drying, or compression to a lower dimension. A supervised
deep neural network is subsequently developed based on the lower dimensional
data to characterize the phases and phase transition. As a proof of concept, we
employ this framework to study coil to globule transition of a model polymer
system. We conduct coarse-grained molecular dynamics simulations to collect
molecular dynamics trajectories of a single polymer chain over a wide range of
temperatures and use dPOLY framework to predict polymer phases. The dPOLY
framework accurately predicts the critical temperatures for the coil to globule
transition for a wide range of polymer sizes. This method is generic and can be
extended to capture various other phase transitions and dynamical crossovers in
polymers and other soft materials.
|
[{'version': 'v1', 'created': 'Sun, 6 Dec 2020 04:51:40 GMT'}]
|
2021-06-09
|
Robbie Sadre, Colin Ophus, Anstasiia Butko, and Gunther H Weber
|
Deep Learning Segmentation of Complex Features in Atomic-Resolution
Phase Contrast Transmission Electron Microscopy Images
| null |
10.1017/S1431927621000167
| null |
cond-mat.mtrl-sci cs.LG
|
Phase contrast transmission electron microscopy (TEM) is a powerful tool for
imaging the local atomic structure of materials. TEM has been used heavily in
studies of defect structures of 2D materials such as monolayer graphene due to
its high dose efficiency. However, phase contrast imaging can produce complex
nonlinear contrast, even for weakly-scattering samples. It is therefore
difficult to develop fully-automated analysis routines for phase contrast TEM
studies using conventional image processing tools. For automated analysis of
large sample regions of graphene, one of the key problems is segmentation
between the structure of interest and unwanted structures such as surface
contaminant layers. In this study, we compare the performance of a conventional
Bragg filtering method to a deep learning routine based on the U-Net
architecture. We show that the deep learning method is more general, simpler to
apply in practice, and produces more accurate and robust results than the
conventional algorithm. We provide easily-adaptable source code for all results
in this paper, and discuss potential applications for deep learning in
fully-automated TEM image analysis.
|
[{'version': 'v1', 'created': 'Wed, 9 Dec 2020 21:17:34 GMT'}]
|
2021-09-01
|
Zhao Fan and Evan Ma
|
Predicting orientation-dependent plastic susceptibility from static
structure in amorphous solids via deep learning
|
Nature Communications 12, 1506 (2021)
|
10.1038/s41467-021-21806-z
| null |
cond-mat.mtrl-sci
|
It has been a long-standing materials science challenge to establish
structure-property relations in amorphous solids. Here we introduce a
rotation-variant local structure representation that enables different
predictions for different loading orientations, which is found essential for
high-fidelity prediction of the propensity for stress-driven shear
transformations. This novel structure representation, when combined with
convolutional neural network (CNN), a powerful deep learning algorithm, leads
to unprecedented accuracy for identifying atoms with high propensity for shear
transformations (i.e., plastic susceptibility), solely from the static
structure - the spatial atomic positions - in both two- and three-dimensional
model glasses. The data-driven models trained on samples at one composition and
a given processing history are found transferrable to glass samples with
different processing histories or at different compositions in the same alloy
system. Our analysis of the new structure representation also provides valuable
insight into key atomic packing features that influence the local mechanical
response and its anisotropy in glasses.
|
[{'version': 'v1', 'created': 'Fri, 11 Dec 2020 00:28:51 GMT'}]
|
2022-03-15
|
Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu
|
Computational discovery of new 2D materials using deep learning
generative models
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Two dimensional (2D) materials have emerged as promising functional materials
with many applications such as semiconductors and photovoltaics because of
their unique optoelectronic properties. While several thousand 2D materials
have been screened in existing materials databases, discovering new 2D
materials remains to be challenging. Herein we propose a deep learning
generative model for composition generation combined with random forest based
2D materials classifier to discover new hypothetical 2D materials. Furthermore,
a template based element substitution structure prediction approach is
developed to predict the crystal structures of a subset of the newly predicted
hypothetical formulas, which allows us to confirm their structure stability
using DFT calculations. So far, we have discovered 267,489 new potential 2D
materials compositions and confirmed twelve 2D/layered materials by DFT
formation energy calculation. Our results show that generative machine learning
models provide an effective way to explore the vast chemical design space for
new 2D materials discovery.
|
[{'version': 'v1', 'created': 'Wed, 16 Dec 2020 23:10:48 GMT'}]
|
2020-12-18
|
Jiale Zhang, Danni Wei, Feng Zhang, Xi Chen, and Dawei Wang
|
Structural phase transition of two-dimensional monolayer SnTe from
artificial neural network
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
As machine learning becomes increasingly important in engineering and
science, it is inevitable that machine learning techniques will be applied to
the investigation of materials, and in particular the structural phase
transitions common in ferroelectric materials. Here, we build and train an
artificial neural network to accurately predict the energy change associated
with atom displacements and use the trained artificial neural network in
Monte-Carlo simulations on ferroelectric materials to investigate their phase
transitions. We apply this approach to two-dimensional monolayer SnTe and show
that it can indeed be used to simulate the phase transitions and predict the
transition temperature. The artificial neural network, when viewed as a
universal mathematical structure, can be readily transferred to the
investigation of other ferroelectric materials when training data generated
with ab initio methods are available.
|
[{'version': 'v1', 'created': 'Mon, 21 Dec 2020 06:33:53 GMT'}, {'version': 'v2', 'created': 'Mon, 29 Mar 2021 03:43:05 GMT'}]
|
2021-03-30
|
Jeffrey M. Ede
|
Advances in Electron Microscopy with Deep Learning
| null |
10.5281/zenodo.4399748
| null |
eess.IV cond-mat.mtrl-sci cs.CV cs.LG
|
This doctoral thesis covers some of my advances in electron microscopy with
deep learning. Highlights include a comprehensive review of deep learning in
electron microscopy; large new electron microscopy datasets for machine
learning, dataset search engines based on variational autoencoders, and
automatic data clustering by t-distributed stochastic neighbour embedding;
adaptive learning rate clipping to stabilize learning; generative adversarial
networks for compressed sensing with spiral, uniformly spaced and other fixed
sparse scan paths; recurrent neural networks trained to piecewise adapt sparse
scan paths to specimens by reinforcement learning; improving signal-to-noise;
and conditional generative adversarial networks for exit wavefunction
reconstruction from single transmission electron micrographs. This thesis adds
to my publications by presenting their relationships, reflections, and holistic
conclusions. This version of my thesis is typeset for online dissemination to
improve readability, whereas the thesis submitted to the University of Warwick
in support of my application for the degree of Doctor of Philosophy in Physics
is typeset for physical printing and binding.
|
[{'version': 'v1', 'created': 'Mon, 4 Jan 2021 13:49:37 GMT'}, {'version': 'v2', 'created': 'Sat, 9 Jan 2021 17:30:04 GMT'}, {'version': 'v3', 'created': 'Fri, 5 Mar 2021 12:06:00 GMT'}, {'version': 'v4', 'created': 'Tue, 9 Mar 2021 14:53:24 GMT'}, {'version': 'v5', 'created': 'Thu, 11 Mar 2021 17:25:33 GMT'}]
|
2021-03-12
|
Yi-Shen Lin, Ganga P. Purja Pun and Yuri Mishin
|
Development of a physically-informed neural network interatomic
potential for tantalum
|
Computational Materials Science 205, 111180 (2022)
|
10.1016/j.commatsci.2021.111180
| null |
cond-mat.mtrl-sci
|
Large-scale atomistic simulations of materials heavily rely on interatomic
potentials, which predict the system energy and atomic forces. One of the
recent developments in the field is constructing interatomic potentials by
machine-learning (ML) methods. ML potentials predict the energy and forces by
numerical interpolation using a large reference database generated by
quantum-mechanical calculations. While high accuracy of interpolation can be
achieved, extrapolation to unknown atomic environments is unpredictable. The
recently proposed physically-informed neural network (PINN) model significantly
improves the transferability by combining a neural network regression with a
physics-based bond-order interatomic potential. Here, we demonstrate that
general-purpose PINN potentials can be developed for body-centered cubic (BCC)
metals. The proposed PINN potential for tantalum reproduces the reference
energies within 2.8 meV/atom. It accurately predicts a broad spectrum of
physical properties of Ta, including (but not limited to) lattice dynamics,
thermal expansion, energies of point and extended defects, the dislocation core
structure and the Peierls barrier, the melting temperature, the structure of
liquid Ta, and the liquid surface tension. The potential enables large-scale
simulations of physical and mechanical behavior of Ta with nearly
first-principles accuracy while being orders of magnitude faster. This approach
can be readily extended to other BCC metals.
|
[{'version': 'v1', 'created': 'Sat, 16 Jan 2021 22:49:09 GMT'}]
|
2022-02-09
|
Mani Valleti, Sergei V. Kalinin, Christopher T. Nelson, Jonathan J. P.
Peters, Wen Dong, Richard Beanland, Xiaohang Zhang, Ichiro Takeuchi, Maxim
Ziatdinov
|
Unsupervised learning of ferroic variants from atomically resolved STEM
images
| null |
10.1063/5.0105406
| null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
An approach for the analysis of atomically resolved scanning transmission
electron microscopy data with multiple ferroic variants in the presence of
imaging non-idealities and chemical variabilities based on a rotationally
invariant variational autoencoder (rVAE) is presented. We show that an optimal
local descriptor for the analysis is a sub-image centered at specific atomic
units, since materials and microscope distortions preclude the use of an ideal
lattice as a reference point. The applicability of unsupervised clustering and
dimensionality reduction methods is explored and are shown to produce clusters
dominated by chemical and microscope effects, with a large number of classes
required to establish the presence of rotational variants. Comparatively, the
rVAE allows extraction of the angle corresponding to the orientation of ferroic
variants explicitly, enabling straightforward identification of the ferroic
variants as regions with constant or smoothly changing latent variables and
sharp orientational changes. This approach allows further exploration of the
chemical variability by separating the rotational degrees of freedom via rVAE
and searching for remaining variability in the system. The code used in the
manuscript is available at
https://github.com/saimani5/ferroelectric_domains_rVAE.
|
[{'version': 'v1', 'created': 'Mon, 18 Jan 2021 06:00:41 GMT'}, {'version': 'v2', 'created': 'Mon, 20 Jun 2022 13:37:26 GMT'}]
|
2024-06-19
|
Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y.
Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, and
Peter A. Crozier
|
Developing and Evaluating Deep Neural Network-based Denoising for
Nanoparticle TEM Images with Ultra-low Signal-to-Noise
|
Microscopy and Microanalysis, vol 27, no 6, pp 1431--1447, Dec
2021
|
10.1017/S1431927621012678
| null |
cond-mat.mtrl-sci eess.IV
|
A deep convolutional neural network has been developed to denoise
atomic-resolution TEM image datasets of nanoparticles acquired using direct
electron counting detectors, for applications where the image signal is
severely limited by shot noise. The network was applied to a model system of
CeO2-supported Pt nanoparticles. We leverage multislice image simulations to
generate a large and flexible dataset for training and testing the network. The
proposed network outperforms state-of-the-art denoising methods by a
significant margin both on simulated and experimental test data. Factors
contributing to the performance are identified, including most importantly (a)
the geometry of the images used during training and (b) the size of the
network's receptive field. Through a gradient-based analysis, we investigate
the mechanisms learned by the network to denoise experimental images. This
shows that the network exploits global and local information in the noisy
measurements, for example, by adapting its filtering approach when it
encounters atomic-level defects at the nanoparticle surface. Extensive analysis
has been done to characterize the network's ability to correctly predict the
exact atomic structure at the nanoparticle surface. Finally, we develop an
approach based on the log-likelihood ratio test that provides a quantitative
measure of the agreement between the noisy observation and the atomic-level
structure in the network-denoised image.
|
[{'version': 'v1', 'created': 'Tue, 19 Jan 2021 18:34:18 GMT'}, {'version': 'v2', 'created': 'Wed, 17 Mar 2021 19:37:16 GMT'}]
|
2025-03-03
|
Doyl Dickel, Mashroor Nitol, Christopher Barrett
|
LAMMPS Implementation of Rapid Artificial Neural Network Derived
Interatomic Potentials
| null | null | null |
cond-mat.mtrl-sci
|
While machine learning approaches have been successfully used to represent
interatomic potentials, their speed has typically lagged behind conventional
formalisms. This is often due to the complexity of the structural fingerprints
used to describe the local atomic environment and the large cutoff radii and
neighbor lists used in the calculation of these fingerprints. Even recent
machine learned methods are at least 10 times slower than traditional
formalisms. An implementation of a rapid artificial neural network (RANN) style
potential in the LAMMPS molecular dynamics package is presented here which
utilizes angular screening to reduce computational complexity without reducing
accuracy. For the smallest neural network architectures, this formalism rivals
the modified embedded atom method (MEAM) for speed and accuracy, while the
networks approximately one third as fast as MEAM were capable of reproducing
the training database with chemical accuracy. The numerical accuracy of the
LAMMPS implementation is assessed by verifying conservation of energy and
agreement between calculated forces and pressures and the observed derivatives
of the energy as well as by assessing the stability of the potential in dynamic
simulation. The potential style is tested using a force field for magnesium and
the computational efficiency for a variety of architectures is compared to a
traditional potential models as well as alternative ANN formalisms. The
predictive accuracy is found to rival that of slower methods.
|
[{'version': 'v1', 'created': 'Thu, 4 Feb 2021 00:06:08 GMT'}, {'version': 'v2', 'created': 'Fri, 19 Feb 2021 21:55:12 GMT'}]
|
2021-02-23
|
Chi Chen and Shyue Ping Ong
|
AtomSets -- A Hierarchical Transfer Learning Framework for Small and
Large Materials Datasets
| null |
10.1038/s41524-021-00639-w
| null |
cond-mat.mtrl-sci
|
Predicting materials properties from composition or structure is of great
interest to the materials science community. Deep learning has recently
garnered considerable interest in materials predictive tasks with low model
errors when dealing with large materials data. However, deep learning models
suffer in the small data regime that is common in materials science. Here we
leverage the transfer learning concept and the graph network deep learning
framework and develop the AtomSets machine learning framework for consistent
high model accuracy at both small and large materials data. The AtomSets models
can work with both compositional and structural materials data. By combining
with transfer learned features from graph networks, they can achieve
state-of-the-art accuracy from using small compositional data (<400) to large
structural data (>130,000). The AtomSets models show much lower errors than the
state-of-the-art graph network models at small data limits and the classical
machine learning models at large data limits. They also transfer better in the
simulated materials discovery process where the targeted materials have
property values out of the training data limits. The models require minimal
domain knowledge inputs and are free from feature engineering. The presented
AtomSets model framework opens new routes for machine learning-assisted
materials design and discovery.
|
[{'version': 'v1', 'created': 'Thu, 4 Feb 2021 04:02:23 GMT'}, {'version': 'v2', 'created': 'Fri, 5 Feb 2021 18:41:06 GMT'}]
|
2021-11-01
|
Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin, Anthony M.
DeGennaro, Andi M. Barbour
|
Noise Reduction in X-ray Photon Correlation Spectroscopy with
Convolutional Neural Networks Encoder-Decoder Models
| null |
10.1038/s41598-021-93747-y
| null |
cond-mat.mtrl-sci cs.LG
|
Like other experimental techniques, X-ray Photon Correlation Spectroscopy is
subject to various kinds of noise. Random and correlated fluctuations and
heterogeneities can be present in a two-time correlation function and obscure
the information about the intrinsic dynamics of a sample. Simultaneously
addressing the disparate origins of noise in the experimental data is
challenging. We propose a computational approach for improving the
signal-to-noise ratio in two-time correlation functions that is based on
Convolutional Neural Network Encoder-Decoder (CNN-ED) models. Such models
extract features from an image via convolutional layers, project them to a low
dimensional space and then reconstruct a clean image from this reduced
representation via transposed convolutional layers. Not only are ED models a
general tool for random noise removal, but their application to low
signal-to-noise data can enhance the data quantitative usage since they are
able to learn the functional form of the signal. We demonstrate that the CNN-ED
models trained on real-world experimental data help to effectively extract
equilibrium dynamics parameters from two-time correlation functions, containing
statistical noise and dynamic heterogeneities. Strategies for optimizing the
models performance and their applicability limits are discussed.
|
[{'version': 'v1', 'created': 'Sun, 7 Feb 2021 18:38:59 GMT'}, {'version': 'v2', 'created': 'Thu, 5 Aug 2021 18:22:34 GMT'}]
|
2021-08-09
|
Hossein Mirhosseini, Hossein Tahmasbi, Sai Ram Kuchana, S. Alireza
Ghasemi, Thomas D. K\"uhne
|
An automated approach for developing neural network interatomic
potentials with FLAME
| null | null | null |
cond-mat.mtrl-sci
|
The performance of machine learning interatomic potentials relies on the
quality of the training dataset. In this work, we present an approach for
generating diverse and representative training data points which initiates with
\it{ab initio} calculations for bulk structures. The data generation and
potential construction further proceed side-by-side in a cyclic process of
training the neural network and crystal structure prediction based on the
developed interatomic potentials. All steps of the data generation and
potential development are performed with minimal human intervention. We show
the reliability of our approach by assessing the performance of neural network
potentials developed for two inorganic systems.
|
[{'version': 'v1', 'created': 'Mon, 8 Feb 2021 09:48:27 GMT'}]
|
2021-02-09
|
Boyu Zhang, Mushen Zhou, Jianzhong Wu, Fuchang Gao
|
Predicting Material Properties Using a 3D Graph Neural Network with
Invariant Local Descriptors
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
Accurate prediction of physical properties is critical for discovering and
designing novel materials. Machine learning technologies have attracted
significant attention in the materials science community for their potential
for large-scale screening. Graph Convolution Neural Network (GCNN) is one of
the most successful machine learning methods because of its flexibility and
effectiveness in describing 3D structural data. Most existing GCNN models focus
on the topological structure but overly simplify the three-dimensional
geometric structure. However, in materials science, the 3D-spatial distribution
of atoms is crucial for determining the atomic states and interatomic forces.
This paper proposes an adaptive GCNN with a novel convolution mechanism that
simultaneously models atomic interactions among all neighbor atoms in
three-dimensional space. We apply the proposed model to two distinctly
challenging problems on predicting material properties. The first is Henry's
constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is
notoriously difficult because of its high sensitivity to atomic configurations.
The second is the ion conductivity in solid-state crystal materials, which is
difficult because of few labeled data available for training. The new model
outperforms existing graph-based models on both data sets, suggesting that the
critical three-dimensional geometric information is indeed captured.
|
[{'version': 'v1', 'created': 'Tue, 16 Feb 2021 19:56:54 GMT'}, {'version': 'v2', 'created': 'Mon, 22 Nov 2021 22:08:52 GMT'}]
|
2021-11-24
|
Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek,
Hongxiang Li, Stefan Bauer, Markus Rampp, Leigh Stephenson
|
Convolutional neural network-assisted recognition of nanoscale L12
ordered structures in face-centred cubic alloys
|
NPJ Computational Materials 7, 8 (2021)
|
10.1038/s41524-020-00472-7
| null |
cond-mat.mtrl-sci physics.data-an
|
Nanoscale L12-type ordered structures are widely used in face-centred cubic
(FCC) alloys to exploit their hardening capacity and thereby improve mechanical
properties. These fine-scale particles are typically fully coherent with matrix
with the same atomic configuration disregarding chemical species, which makes
them challenging to be characterized. Spatial distribution maps (SDMs) are used
to probe local order by interrogating the three-dimensional (3D) distribution
of atoms within reconstructed atom probe tomography (APT) data. However, it is
almost impossible to manually analyse the complete point cloud ($>10$ million)
in search for the partial crystallographic information retained within the
data. Here, we proposed an intelligent L12-ordered structure recognition method
based on convolutional neural networks (CNNs). The SDMs of a simulated
L12-ordered structure and the FCC matrix were firstly generated. These
simulated images combined with a small amount of experimental data were used to
train a CNN-based L12-ordered structure recognition model. Finally, the
approach was successfully applied to reveal the 3D distribution of L12-type
$\delta^\prime$-Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a
FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down
to 5 \r{A}. The proposed CNN-APT method is promising to be extended to
recognize other nanoscale ordered structures and even more-challenging
short-range ordered phenomena in the near future.
|
[{'version': 'v1', 'created': 'Tue, 16 Feb 2021 09:41:50 GMT'}]
|
2021-02-23
|
Debjyoti Bhattacharya and Tarak K Patra
|
Deep Learning Order Parameter for Polymer Phase Transition
| null | null | null |
cond-mat.mtrl-sci
|
We report a deep learning (DL) framework viz. deep autoencoder that
autonomously discovers an appropriate order parameter from molecular dynamics
(MD) simulation data to characterize the coil to globule phase transition of a
polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a
polymer in a one-dimensional feature space and, subsequently, decodes the
one-dimensional feature to its original 3N dimensional polymer trajectory. The
feature space representation of a polymer provides a new order parameter that
accurately describes the coil to globule phase transition as a function of
temperature. This method is very generic and extensible to identify flexible
order parameters to characterize wide range of phase transitions that take
place in polymers and other soft materials. Moreover, this MD-DL approach is
computational very efficient than a pure MD based characterization of phase
transition, and has potential implications in accelerating phase prediction.
|
[{'version': 'v1', 'created': 'Wed, 24 Feb 2021 01:04:15 GMT'}]
|
2021-02-25
|
Andreas Erlebach, Petr Nachtigall, and Luk\'a\v{s} Grajciar
|
Accurate large-scale simulations of siliceous zeolites by neural network
potentials
|
npj Comput Mater 8, 174 (2022)
|
10.1038/s41524-022-00865-w
| null |
cond-mat.mtrl-sci
|
The computational discovery and design of zeolites is a crucial part of the
chemical industry. Finding highly accurate while computationally feasible
protocol for identification of hypothetical zeolites that could be targeted
experimentally is a great challenge. To tackle the challenge, we trained neural
network potentials (NNP) with the SchNet architecture on a structurally diverse
database of density functional theory (DFT) data. This database was iteratively
extended by active learning to cover not only low-energy equilibrium
configurations but also high-energy transition states. We demonstrate that the
resulting reactive NNPs retain the accuracy of the DFT reference for
thermodynamic stabilities, vibrational properties, and reactive and
non-reactive phase transformations. The novel NNPs outperforms specialized,
analytical force fields for silica, such as ReaxFF, by order(s) of magnitude in
accuracy, while speeding up the calculations in comparison to DFT by at least
three orders of magnitude. As a showcase, we screened an existing zeolite
database containing 330 thousand structures and revealed more than 20 thousand
additional hypothetical frameworks in the thermodynamically accessible range of
zeolite synthesis. Hence, our NNPs are expected to be essential for future
high-throughput studies on the structure and reactivity of hypothetical and
existing zeolites.
|
[{'version': 'v1', 'created': 'Wed, 24 Feb 2021 16:44:18 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Jan 2022 14:35:01 GMT'}, {'version': 'v3', 'created': 'Fri, 19 Aug 2022 14:12:44 GMT'}]
|
2022-08-22
|
Wesley F. Reinhart
|
Unsupervised learning of atomic environments from simple features
| null |
10.1016/j.commatsci.2021.110511
| null |
cond-mat.mtrl-sci
|
I present a strategy for unsupervised manifold learning on local atomic
environments in molecular simulations based on simple rotation- and
permutation-invariant three-body features. These features are highly
descriptive, generalize to multiple chemical species, and are
human-interpretable. The low-dimensional embeddings of each atomic environment
can be used to understand and quantify messy crystal structures such as those
near interfaces and defects or well-ordered crystal lattices such as in bulk
materials without modification. The same method can also yield collective
variables describing collections of particles such as for an entire simulation
domain. I demonstrate the method on colloidal crystallization, ice crystals,
and binary mesophases to illustrate its broad applicability. In each case, the
learned latent space yields insights into the details of the observed
microstructures. For ices and mesophases, supervised classifiers are trained
based on the learned manifolds and directly compared against a recent
neural-network-based approach. Notably, while this method provides comparable
classification performance, it can also be deployed on even a handful of
observed environments without labels or \textit{a priori} knowledge. Thus, the
current approach provides an incredibly versatile strategy to characterize and
classify local atomic environments, and may unlock insights in a wide variety
of molecular simulation contexts.
|
[{'version': 'v1', 'created': 'Sun, 28 Feb 2021 11:37:27 GMT'}, {'version': 'v2', 'created': 'Sat, 10 Apr 2021 13:46:17 GMT'}]
|
2023-01-03
|
Andreas Leitherer, Angelo Ziletti, and Luca M. Ghiringhelli
|
Robust recognition and exploratory analysis of crystal structures via
Bayesian deep learning
|
Leitherer, A., Ziletti, A. & Ghiringhelli, L.M. Robust recognition
and exploratory analysis of crystal structures via Bayesian deep learning.
Nat. Commun. 12, 6234 (2021)
|
10.1038/s41467-021-26511-5
| null |
cond-mat.mtrl-sci
|
Due to their ability to recognize complex patterns, neural networks can drive
a paradigm shift in the analysis of materials science data. Here, we introduce
ARISE, a crystal-structure identification method based on Bayesian deep
learning. As a major step forward, ARISE is robust to structural noise and can
treat more than 100 crystal structures, a number that can be extended on
demand. While being trained on ideal structures only, ARISE correctly
characterizes strongly perturbed single- and polycrystalline systems, from both
synthetic and experimental resources. The probabilistic nature of the
Bayesian-deep-learning model allows to obtain principled uncertainty estimates,
which are found to be correlated with crystalline order of metallic
nanoparticles in electron tomography experiments. Applying unsupervised
learning to the internal neural-network representations reveals grain
boundaries and (unapparent) structural regions sharing easily interpretable
geometrical properties. This work enables the hitherto hindered analysis of
noisy atomic structural data from computations or experiments.
|
[{'version': 'v1', 'created': 'Wed, 17 Mar 2021 17:04:13 GMT'}, {'version': 'v2', 'created': 'Wed, 21 Apr 2021 17:33:31 GMT'}, {'version': 'v3', 'created': 'Tue, 14 Sep 2021 19:37:09 GMT'}, {'version': 'v4', 'created': 'Fri, 1 Oct 2021 14:24:19 GMT'}, {'version': 'v5', 'created': 'Mon, 8 Nov 2021 15:03:00 GMT'}]
|
2021-11-09
|
Noopur Jamnikar, Sen Liu, Craig Brice, and Xiaoli Zhang
|
Comprehensive process-molten pool relations modeling using CNN for
wire-feed laser additive manufacturing
| null | null | null |
cond-mat.mtrl-sci cs.LG eess.SP stat.ML
|
Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to
its high level of automation, high deposition rates, and good quality of
printed parts. In-process monitoring and feedback controls that would reduce
the uncertainty in the quality of the material are in the early stages of
development. Machine learning promises the ability to accelerate the adoption
of new processes and property design in additive manufacturing by making
process-structure-property connections between process setting inputs and
material quality outcomes. The molten pool dimensional information and
temperature are the indicators for achieving the high quality of the build,
which can be directly controlled by processing parameters. For the purpose of
in situ quality control, the process parameters should be controlled in
real-time based on sensed information from the process, in particular the
molten pool. Thus, the molten pool-process relations are of preliminary
importance. This paper analyzes experimentally collected in situ sensing data
from the molten pool under a set of controlled process parameters in a WLAM
system. The variations in the steady-state and transient state of the molten
pool are presented with respect to the change of independent process
parameters. A multi-modality convolutional neural network (CNN) architecture is
proposed for predicting the control parameter directly from the measurable
molten pool sensor data for achieving desired geometric and microstructural
properties. Dropout and regularization are applied to the CNN architecture to
avoid the problem of overfitting. The results highlighted that the multi-modal
CNN, which receives temperature profile as an external feature to the features
extracted from the image data, has improved prediction performance compared to
the image-based uni-modality CNN approach.
|
[{'version': 'v1', 'created': 'Mon, 22 Mar 2021 05:27:20 GMT'}]
|
2021-03-23
|
C.H.Wong, S.M. Ng, C.W.Leung, A.F.Zatsepin
|
The effectiveness of data augmentation in porous substrate, nanowire,
fiber and tip images at the level of deep learning intelligence
| null | null | null |
cond-mat.mtrl-sci
|
To prepare for identifying the composition of nanowire-fiber mixtures in
Scanning Electron Microscope (SEM) images, we optimize the performance of image
classification between nanowires, fibers and tips due to their geometric
similarities. The SEM images are analyzed by deep learning techniques where the
validation accuracies of 11 convolutional neural network (CNN) models are
compared. By increasing the diversity of data such as reflection, translation
and scale factor approaches, the highest validation accuracy of recognizing
nanowires, fibers and tips is 97.1%. We proceed to classify the level of
porosity in anodized aluminum oxide for the self-assisted nanowire growth where
the validation accuracy is optimized at 93%. Our software allow scientists to
count the percentage of fibers in any nanowire-fiber composite and design the
porous substrate for embedding different sizes of nanowires automatically,
which assists the software development in Nanoscience Foundries & Fine Analysis
(NFFA) Europe Projects.
|
[{'version': 'v1', 'created': 'Thu, 11 Mar 2021 10:05:13 GMT'}]
|
2021-03-24
|
Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman,
George Em Karniadakis
|
A physics-informed neural network for quantifying the microstructure
properties of polycrystalline Nickel using ultrasound data
| null |
10.1109/MSP.2021.3118904
| null |
cond-mat.mtrl-sci physics.comp-ph
|
We employ physics-informed neural networks (PINNs) to quantify the
microstructure of a polycrystalline Nickel by computing the spatial variation
of compliance coefficients (compressibility, stiffness and rigidity) of the
material. The PINN is supervised with realistic ultrasonic surface acoustic
wavefield data acquired at an ultrasonic frequency of 5 MHz for the
polycrystalline material. The ultrasonic wavefield data is represented as a
deformation on the top surface of the material with the deformation measured
using the method of laser vibrometry. The ultrasonic data is further
complemented with wavefield data generated using a finite element based solver.
The neural network is physically-informed by the in-plane and out-of-plane
elastic wave equations and its convergence is accelerated using adaptive
activation functions. The overarching goal of this work is to infer the spatial
variation of compliance coefficients of materials using PINNs, which for
ultrasound involves the spatially varying speed of the elastic waves. More
broadly, the resulting PINN based surrogate model shows a promising approach
for solving ill-posed inverse problems, often encountered in the
non-destructive evaluation of materials.
|
[{'version': 'v1', 'created': 'Thu, 25 Mar 2021 19:47:17 GMT'}, {'version': 'v2', 'created': 'Tue, 5 Oct 2021 17:27:05 GMT'}]
|
2022-01-12
|
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Qingsong Tu,
Gerbrand Ceder
|
A probabilistic deep learning approach to automate the interpretation of
multi-phase diffraction spectra
| null |
10.1021/acs.chemmater.1c01071
| null |
cond-mat.mtrl-sci cs.LG
|
Autonomous synthesis and characterization of inorganic materials requires the
automatic and accurate analysis of X-ray diffraction spectra. For this task, we
designed a probabilistic deep learning algorithm to identify complex
multi-phase mixtures. At the core of this algorithm lies an ensemble
convolutional neural network trained on simulated diffraction spectra, which
are systematically augmented with physics-informed perturbations to account for
artifacts that can arise during experimental sample preparation and synthesis.
Larger perturbations associated with off-stoichiometry are also captured by
supplementing the training set with hypothetical solid solutions. Spectra
containing mixtures of materials are analyzed with a newly developed branching
algorithm that utilizes the probabilistic nature of the neural network to
explore suspected mixtures and identify the set of phases that maximize
confidence in the prediction. Our model is benchmarked on simulated and
experimentally measured diffraction spectra, showing exceptional performance
with accuracies exceeding those given by previously reported methods based on
profile matching and deep learning. We envision that the algorithm presented
here may be integrated in experimental workflows to facilitate the
high-throughput and autonomous discovery of inorganic materials.
|
[{'version': 'v1', 'created': 'Tue, 30 Mar 2021 20:13:01 GMT'}]
|
2021-05-27
|
Lars Banko, Phillip M. Maffettone, Dennis Naujoks, Daniel Olds, Alfred
Ludwig
|
Deep learning for visualization and novelty detection in large X-ray
diffraction datasets
| null | null | null |
cond-mat.mtrl-sci physics.data-an
|
We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data
analysis on both simulated and experimental thin-film data. We show that
crystal structure representations learned by a VAE reveal latent information,
such as the structural similarity of textured diffraction patterns. While other
artificial intelligence (AI) agents are effective at classifying XRD data into
known phases, a similarly conditioned VAE is uniquely effective at knowing what
it does not know, rapidly identifying novel phases and mixtures. These
capabilities demonstrate that a VAE is a valuable AI agent for materials
discovery and understanding XRD measurements both on-the-fly and during post
hoc analysis.
|
[{'version': 'v1', 'created': 'Fri, 9 Apr 2021 14:31:22 GMT'}]
|
2021-04-12
|
Wouter Klessens, Ivan Vasconcelos, Yang Jiao
|
AI-driven Bayesian inference of statistical microstructure descriptors
from finite-frequency waves
| null | null | null |
physics.geo-ph cond-mat.mtrl-sci eess.IV
|
The ability to image materials at the microscale from long-wavelength wave
data is a major challenge to the geophysical, engineering and medical fields.
Here, we present a framework to constrain microstructure geometry and
properties from long-scale waves. To realistically quantify microstructures we
use two-point statistics, from which we derive scale-dependent effective wave
properties - wavespeed and attenuation - using strong-contrast expansions (SCE)
for (visco)elastic wavefields. By evaluating various two-point correlation
functions we observe that both effective wavespeeds and attenuation of
long-scale waves predominantly depend on volume fraction and phase properties,
and that especially attenuation at small scales is highly sensitive to the
geometry of microstructure heterogeneity (e.g. geometric hyperuniformity) due
to incoherent inference of sub-wavelength multiple scattering. Our goal is to
infer microstructure properties from observed effective wave parameters. To
this end, we use the supervised machine learning method of Random Forests (RF)
to construct a Bayesian inference approach. We can accurately resolve two-point
correlation functions sampled from various microstructural configurations,
including: a bead pack, Berea sandstone and Ketton limestone samples.
Importantly, we show that inversion of small scale-induced effective elastic
waves yields the best results, particularly compared to single-wave-mode (e.g.,
acoustic only) information. Additionally, we show that the retrieval of
microscale medium contrasts is more difficult - as it is highly ill-posed - and
can only be achieved with specific a priori knowledge. Our results are
promising for many applications, such as earthquake hazard
monitoring,non-destructive testing, imaging fluid flow in porous media,
quantifying tissue properties in medical ultrasound, or designing materials
with tailor-made wave properties.
|
[{'version': 'v1', 'created': 'Fri, 16 Apr 2021 13:43:52 GMT'}]
|
2021-04-19
|
Zi-Shan Liao, Hong-Hao Zhang, Zhongbo Yan
|
Nonlinear Hall effect in two-dimensional class AI metals
|
Phys. Rev. B 103, 235151 (2021)
|
10.1103/PhysRevB.103.235151
| null |
cond-mat.mtrl-sci quant-ph
|
In a time-reversal invariant system, while the anomalous Hall effect
identically vanishes in the linear response regime due to the constraint of
time-reversal symmetry on the distribution of Berry curvature, a nonlinear Hall
effect can emerge in the second-order response regime if the inversion symmetry
is broken to allow a nonzero Berry curvature dipole (BCD) on the Fermi surface.
In this work, we study the nonlinear Hall effect of the BCD origin in
two-dimensional doped insulators and semimetals belonging to the symmetry class
AI which has spinless time-reversal symmetry. Despite that the class AI does
not host any strong topological insulator phase in two dimensions, we find that
they can still be classified as topologically obstructed insulators and trivial
insulators if putting certain constraint on the Hamiltonians. When the
insulator gets closer to the phase boundary of the two distinct phases, we find
that the BCDs will become more prominent if the doping level is located near
the band edge. Moreover, when the insulator undergoes a phase transition
between the two distinct phases, we find that the BCDs will dramatically change
their signs. For the semimetals without inversion symmetry, we find that the
BCDs will sharply reverse their signs when the doping level crosses the Dirac
points. With the shift of the locations of Dirac points in energy, the critical
doping level at which the BCDs sharply reverse their signs will accordingly
change. Our study reveals that class AI materials can also have interesting
geometrical and topological properties, and remarkable nonlinear Hall effect
can also appear in this class of materials even though the spin-orbit coupling
is negligible. Our findings broaden the scope of materials to study the
nonlinear Hall effect and provide new perspectives for the application of this
effect.
|
[{'version': 'v1', 'created': 'Sat, 17 Apr 2021 07:50:59 GMT'}]
|
2021-06-30
|
Yunxing Zuo, Mingde Qin, Chi Chen, Weike Ye, Xiangguo Li, Jian Luo,
Shyue Ping Ong
|
Accelerating Materials Discovery with Bayesian Optimization and Graph
Deep Learning
| null | null | null |
cond-mat.mtrl-sci
|
Machine learning (ML) models utilizing structure-based features provide an
efficient means for accurate property predictions across diverse chemical
spaces. However, obtaining equilibrium crystal structures typically requires
expensive density functional theory (DFT) calculations, which limits ML-based
exploration to either known crystals or a small number of hypothetical
crystals. Here, we demonstrate that the application of Bayesian optimization
with symmetry constraints using a graph deep learning energy model can be used
to perform "DFT-free" relaxations of crystal structures. Using this approach to
significantly improve the accuracy of ML-predicted formation energies and
elastic moduli of hypothetical crystals, two novel ultra-incompressible hard
materials MoWC2 (P63/mmc) and ReWB (Pca21) were identified and successfully
synthesized via in-situ reactive spark plasma sintering from a screening of
399,960 transition metal borides and carbides. This work addresses a critical
bottleneck to accurate property predictions for hypothetical materials, paving
the way to ML-accelerated discovery of new materials with exceptional
properties.
|
[{'version': 'v1', 'created': 'Tue, 20 Apr 2021 20:37:00 GMT'}]
|
2021-04-22
|
Yongtao Liu, Roger Proksch, Chun Yin Wong, Maxim Ziatdinov, and Sergei
V. Kalinin
|
Disentangling ferroelectric wall dynamics and identification of pinning
mechanisms via deep learning
| null | null | null |
cond-mat.dis-nn cond-mat.mtrl-sci
|
Field-induced domain wall dynamics in ferroelectric materials underpins
multiple applications ranging from actuators to information technology devices
and necessitates a quantitative description of the associated mechanisms
including giant electromechanical couplings, controlled non-linearities, or low
coercive voltages. While the advances in dynamic Piezoresponse Force Microscopy
measurements over the last two decades have rendered visualization of
polarization dynamics relatively straightforward, the associated insights into
the local mechanisms have been elusive. Here we explore the domain dynamics in
model polycrystalline materials using a workflow combining deep learning-based
segmentation of the domain structures with non-linear dimensionality reduction
using multilayer rotationally-invariant autoencoders (rVAE). The former allows
unambiguous identification and classification of the ferroelectric and
ferroelastic domain walls. The rVAE discover the latent representations of the
domain wall geometries and their dynamics, thus providing insight into the
intrinsic mechanisms of polarization switching, that can further be compared to
simple physical models. The rVAE disentangles the factors affecting the pinning
efficiency of ferroelectric walls, offering insights into the correlation of
ferroelastic wall distribution and ferroelectric wall pinning.
|
[{'version': 'v1', 'created': 'Sat, 15 May 2021 03:39:19 GMT'}]
|
2021-05-18
|
Maxim Ziatdinov, Ayana Ghosh, Tommy Wong, and Sergei V. Kalinin
|
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy
Data in (Scanning) Transmission Electron Microscopy and Beyond
|
Nat Mach Intell 4, 1101-1112 (2022)
|
10.1038/s42256-022-00555-8
| null |
physics.data-an cond-mat.dis-nn cond-mat.mtrl-sci cs.LG
|
AtomAI is an open-source software package bridging instrument-specific Python
libraries, deep learning, and simulation tools into a single ecosystem. AtomAI
allows direct applications of the deep convolutional neural networks for atomic
and mesoscopic image segmentation converting image and spectroscopy data into
class-based local descriptors for downstream tasks such as statistical and
graph analysis. For atomically-resolved imaging data, the output is types and
positions of atomic species, with an option for subsequent refinement. AtomAI
further allows the implementation of a broad range of image and spectrum
analysis functions, including invariant variational autoencoders (VAEs). The
latter consists of VAEs with rotational and (optionally) translational
invariance for unsupervised and class-conditioned disentanglement of
categorical and continuous data representations. In addition, AtomAI provides
utilities for mapping structure-property relationships via im2spec and spec2im
type of encoder-decoder models. Finally, AtomAI allows seamless connection to
the first principles modeling with a Python interface, including molecular
dynamics and density functional theory calculations on the inferred atomic
position. While the majority of applications to date were based on atomically
resolved electron microscopy, the flexibility of AtomAI allows straightforward
extension towards the analysis of mesoscopic imaging data once the labels and
feature identification workflows are established/available. The source code and
example notebooks are available at https://github.com/pycroscopy/atomai.
|
[{'version': 'v1', 'created': 'Sun, 16 May 2021 17:44:59 GMT'}]
|
2022-12-29
|
Zhe Wang and Claude Guet
|
Deep learning in physics: a study of dielectric quasi-cubic particles in
a uniform electric field
| null | null | null |
physics.class-ph cond-mat.mtrl-sci cs.LG physics.comp-ph
|
Solving physics problems for which we know the equations, boundary conditions
and symmetries can be done by deep learning. The constraints can be either
imposed as terms in a loss function or used to formulate a neural ansatz. In
the present case study, we calculate the induced field inside and outside a
dielectric cube placed in a uniform electric field, wherein the dielectric
mismatch at edges and corners of the cube makes accurate calculations
numerically challenging. The electric potential is expressed as an ansatz
incorporating neural networks with known leading order behaviors and symmetries
and the Laplace's equation is then solved with boundary conditions at the
dielectric interface by minimizing a loss function. The loss function ensures
that both Laplace's equation and boundary conditions are satisfied everywhere
inside a large solution domain. We study how the electric potential inside and
outside a quasi-cubic particle evolves through a sequence of shapes from a
sphere to a cube. The neural network being differentiable, it is
straightforward to calculate the electric field over the whole domain, the
induced surface charge distribution and the polarizability. The neural network
being retentive, one can efficiently follow how the field changes upon
particle's shape or dielectric constant by iterating from any previously
converged solution. The present work's objective is two-fold, first to show how
an a priori knowledge can be incorporated into neural networks to achieve
efficient learning and second to apply the method and study how the induced
field and polarizability change when a dielectric particle progressively
changes its shape from a sphere to a cube.
|
[{'version': 'v1', 'created': 'Tue, 11 May 2021 10:40:03 GMT'}]
|
2021-05-21
|
Brendan P. Croom, Michael Berkson, Robert K. Mueller, Michael Presley,
Steven Storck
|
Deep learning prediction of stress fields in additively manufactured
metals with intricate defect networks
| null | null | null |
cond-mat.mtrl-sci
|
In context of the universal presence of defects in additively manufactured
(AM) metals, efficient computational tools are required to rapidly screen AM
microstructures for mechanical integrity. To this end, a deep learning approach
is used to predict the elastic stress fields in images of defect-containing
metal microstructures. A large dataset consisting of the stress response of
100,000 random microstructure images is generated using high-resolution Fast
Fourier Transform-based finite element (FFT-FE) calculations, which is then
used to train a modified U-Net style convolutional neural network (CNN) model.
The trained U-Net model more accurately predicted the stress response compared
to alternative CNN architectures, exceeded the accuracy of low-resolution
FFT-FE calculations, and was generalizable to microstructures with complex
defect geometries. The model was applied to images of real AM microstructures
with severe lack of fusion defects, and predicted a strong linear increase of
maximum stress as a function of pore fraction. Together, the proposed CNN
offers an efficient and accurate way to predict the structural response of
defect-containing AM microstructures.
|
[{'version': 'v1', 'created': 'Fri, 21 May 2021 20:44:44 GMT'}]
|
2021-05-25
|
Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, and
Sergei V. Kalinin
|
Semi-supervised learning of images with strong rotational disorder:
assembling nanoparticle libraries
| null | null | null |
cs.LG cond-mat.dis-nn cond-mat.mtrl-sci physics.data-an
|
The proliferation of optical, electron, and scanning probe microscopies gives
rise to large volumes of imaging data of objects as diversified as cells,
bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the
experimental data streams contain images having arbitrary rotations and
translations within the image. At the same time, for many cases, small amounts
of labeled data are available in the form of prior published results, image
collections, and catalogs, or even theoretical models. Here we develop an
approach that allows generalizing from a small subset of labeled data with a
weak orientational disorder to a large unlabeled dataset with a much stronger
orientational (and positional) disorder, i.e., it performs a classification of
image data given a small number of examples even in the presence of a
distribution shift between the labeled and unlabeled parts. This approach is
based on the semi-supervised rotationally invariant variational autoencoder
(ss-rVAE) model consisting of the encoder-decoder "block" that learns a
rotationally (and translationally) invariant continuous latent representation
of data and a classifier that encodes data into a finite number of discrete
classes. The classifier part of the trained ss-rVAE inherits the rotational
(and translational) invariances and can be deployed independently of the other
parts of the model. The performance of the ss-rVAE is illustrated using the
synthetic data sets with known factors of variation. We further demonstrate its
application for experimental data sets of nanoparticles, creating nanoparticle
libraries and disentangling the representations defining the physical factors
of variation in the data. The code reproducing the results is available at
https://github.com/ziatdinovmax/Semi-Supervised-VAE-nanoparticles.
|
[{'version': 'v1', 'created': 'Mon, 24 May 2021 18:01:57 GMT'}]
|
2021-05-26
|
Gihan Panapitiya, Michael Girard, Aaron Hollas, Vijay Murugesan, Wei
Wang, Emily Saldanha
|
Predicting Aqueous Solubility of Organic Molecules Using Deep Learning
Models with Varied Molecular Representations
| null |
10.1021/acsomega.2c00642
| null |
cond-mat.mtrl-sci cs.LG
|
Determining the aqueous solubility of molecules is a vital step in many
pharmaceutical, environmental, and energy storage applications. Despite efforts
made over decades, there are still challenges associated with developing a
solubility prediction model with satisfactory accuracy for many of these
applications. The goal of this study is to develop a general model capable of
predicting the solubility of a broad range of organic molecules. Using the
largest currently available solubility dataset, we implement deep
learning-based models to predict solubility from molecular structure and
explore several different molecular representations including molecular
descriptors, simplified molecular-input line-entry system (SMILES) strings,
molecular graphs, and three-dimensional (3D) atomic coordinates using four
different neural network architectures - fully connected neural networks
(FCNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and
SchNet. We find that models using molecular descriptors achieve the best
performance, with GNN models also achieving good performance. We perform
extensive error analysis to understand the molecular properties that influence
model performance, perform feature analysis to understand which information
about molecular structure is most valuable for prediction, and perform a
transfer learning and data size study to understand the impact of data
availability on model performance.
|
[{'version': 'v1', 'created': 'Wed, 26 May 2021 15:54:54 GMT'}, {'version': 'v2', 'created': 'Thu, 27 May 2021 01:03:43 GMT'}]
|
2022-09-05
|
Kamal Choudhary, Brian DeCost
|
Atomistic Line Graph Neural Network for Improved Materials Property
Predictions
| null |
10.1038/s41524-021-00650-1
| null |
cond-mat.mtrl-sci
|
Graph neural networks (GNN) have been shown to provide substantial
performance improvements for atomistic material representation and modeling
compared with descriptor-based machine learning models. While most existing GNN
models for atomistic predictions are based on atomic distance information, they
do not explicitly incorporate bond angles, which are critical for
distinguishing many atomic structures. Furthermore, many material properties
are known to be sensitive to slight changes in bond angles. We present an
Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs
message passing on both the interatomic bond graph and its line graph
corresponding to bond angles. We demonstrate that angle information can be
explicitly and efficiently included, leading to improved performance on
multiple atomistic prediction tasks. We ALIGNN models for predicting 52
solid-state and molecular properties available in the JARVIS-DFT, Materials
project, and QM9 databases. ALIGNN can outperform some previously reported GNN
models on atomistic prediction tasks by up to 85% in accuracy with better or
comparable model training speed.
|
[{'version': 'v1', 'created': 'Thu, 3 Jun 2021 13:26:06 GMT'}, {'version': 'v2', 'created': 'Tue, 7 Sep 2021 13:53:44 GMT'}, {'version': 'v3', 'created': 'Thu, 7 Apr 2022 00:13:11 GMT'}]
|
2022-04-08
|
Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari, Saeed Tamimi, Mohsen
Mohammadi, Laurent K. Beland, Mark R. Daymond, Isaac Tamblyn
|
Deep Learning and Crystal Plasticity: A Preconditioning Approach for
Accurate Orientation Evolution Prediction
| null |
10.1016/j.cma.2021.114392
| null |
cond-mat.mtrl-sci
|
Efficient and precise prediction of plasticity by data-driven models relies
on appropriate data preparation and a well-designed model. Here we introduce an
unsupervised machine learning-based data preparation method to maximize the
trainability of crystal orientation evolution data during deformation. For
Taylor model crystal plasticity data, the preconditioning procedure improves
the test score of an artificial neural network from 0.831 to 0.999, while
decreasing the training iterations by an order of magnitude. The efficacy of
the approach was further improved with a recurrent neural network. Electron
backscattered (EBSD) lab measurements of crystal rotation during rolling were
compared with the results of the surrogate model, and despite error introduced
by Taylor model simplifying assumptions, very reasonable agreement between the
surrogate model and experiment was observed. Our method is foundational for
further data-driven studies, enabling the efficient and precise prediction of
texture evolution from experimental and simulated crystal plasticity results.
|
[{'version': 'v1', 'created': 'Thu, 24 Jun 2021 02:32:46 GMT'}]
|
2021-12-22
|
Yoshinori Shiihara, Ryosuke Kanazawa, Daisuke Matsunaka, Ivan
Lobzenko, Tomohito Tsuru, Masanori Kohyama, Hideki Mori
|
Artificial neural network molecular mechanics of iron grain boundaries
| null | null | null |
cond-mat.mtrl-sci
|
This study reports grain boundary (GB) energy calculations for 46
symmetric-tilt GBs in alpha-iron using molecular mechanics based on an
artificial neural network (ANN) potential and compares the results with
calculations based on the density functional theory (DFT), the embedded atom
method (EAM), and the modified EAM (MEAM). The results by the ANN potential are
in excellent agreement with those of the DFT (5% on average), while the EAM and
MEAM significantly differ from the DFT results (about 27% on average). In a
uniaxial tensile calculation of Sigma 3 (1-12) GB, the ANN potential reproduced
the brittle fracture tendency of the GB observed in the DFT while the EAM and
MEAM showed mistakenly showed ductile behaviors. These results demonstrate the
effectiveness of the ANN potential in grain boundary calculations of iron as a
fast and accurate simulation highly in demand in the modern industrial world.
|
[{'version': 'v1', 'created': 'Thu, 24 Jun 2021 03:05:43 GMT'}]
|
2021-06-25
|
So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen
Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke
Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada,
Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide
Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka
|
Towards Universal Neural Network Potential for Material Discovery
Applicable to Arbitrary Combination of 45 Elements
| null |
10.1038/s41467-022-30687-9
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Computational material discovery is under intense study owing to its ability
to explore the vast space of chemical systems. Neural network potentials (NNPs)
have been shown to be particularly effective in conducting atomistic
simulations for such purposes. However, existing NNPs are generally designed
for narrow target materials, making them unsuitable for broader applications in
material discovery. To overcome this issue, we have developed a universal NNP
called PreFerred Potential (PFP), which is able to handle any combination of 45
elements. Particular emphasis is placed on the datasets, which include a
diverse set of virtual structures used to attain the universality. We
demonstrated the applicability of PFP in selected domains: lithium diffusion in
LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, an
order-disorder transition of Cu-Au alloys, and material discovery for a
Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology
provides a highly useful tool for material discovery.
|
[{'version': 'v1', 'created': 'Mon, 28 Jun 2021 11:32:13 GMT'}, {'version': 'v2', 'created': 'Fri, 1 Apr 2022 15:48:16 GMT'}]
|
2022-07-06
|
Bruce Lim, Ewen Bellec, Maxime Dupraz, Steven Leake, Andrea Resta,
Alessandro Coati, Michael Sprung, Ehud Almog, Eugen Rabkin, Tobias Sch\"ulli
and Marie-Ingrid Richard
|
A convolutional neural network for defect classification in Bragg
coherent X-ray diffraction
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Coherent diffraction imaging enables the imaging of individual defects, such
as dislocations or stacking faults, in materials.These defects and their
surrounding elastic strain fields have a critical influence on the macroscopic
properties and functionality of materials. However, their identification in
Bragg coherent diffraction imaging remains a challenge and requires significant
data mining. The ability to identify defects from the diffraction pattern alone
would be a significant advantage when targeting specific defect types and
accelerates experiment design and execution. Here, we exploit a computational
tool based on a three-dimensional (3D) parametric atomistic model and a
convolutional neural network to predict dislocations in a crystal from its 3D
coherent diffraction pattern. Simulated diffraction patterns from several
thousands of relaxed atomistic configurations of nanocrystals are used to train
the neural network and to predict the presence or absence of dislocations as
well as their type(screw or edge). Our study paves the way for defect
recognition in 3D coherent diffraction patterns for material science
|
[{'version': 'v1', 'created': 'Wed, 30 Jun 2021 16:15:29 GMT'}]
|
2021-07-01
|
Gerardo Valadez Huerta, Yusuke Nanba, Iori Kurata, Kosuke Nakago, So
Takamoto, Chikashi Shinagawa, Michihisa Koyama
|
Calculations of Real-System Nanoparticles Using Universal Neural Network
Potential PFP
| null | null | null |
cond-mat.mtrl-sci
|
It is essential to explore the stability and activity of real-system
nanoparticles theoretically. While applications of theoretical methods for this
purpose can be found in literature, the expensive computational costs of
conventional theoretical methods hinder their massive applications to practical
materials design. With the recent development of neural network algorithms
along with the advancement of computer systems, neural network potentials have
emerged as a promising candidate for the description of a wide range of
materials, including metals and molecules, with a reasonable computational
time. In this study, we successfully validate a universal neural network
potential, PFP, for the description of monometallic Ru nanoparticles, PdRuCu
ternary alloy nanoparticles, and the NO adsorption on Rh nanoparticles against
first-principles calculations. We further conduct molecular dynamics
simulations on the NO-Rh system and challenge the PFP to describe a large,
supported Pt nanoparticle system.
|
[{'version': 'v1', 'created': 'Fri, 2 Jul 2021 10:51:24 GMT'}]
|
2021-07-05
|
Carlos J. G. Rojas, Marco L. Bitterncourt, Jos\'e L. Boldrini
|
Parameter identification for a damage model using a physics informed
neural network
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
This work applies concepts of artificial neural networks to identify the
parameters of a mathematical model based on phase fields for damage and
fracture. Damage mechanics is the part of the continuum mechanics that models
the effects of the micro-defect formation using state variables at the
macroscopic level. The equations that define the model are derived from
fundamental laws of physics and provide important relationships between state
variables. Simulations using the model considered in this work produce good
qualitative and quantitative results, but many parameters must be adjusted to
reproduce a certain material behavior. The identification of model parameters
is considered by solving an inverse problem that uses pseudo-experimental data
to find the values that produce the best fit to the data. We apply a
physics-informed neural network and combine some classical estimation methods
to identify the material parameters that appear in the damage equation of the
model. Our strategy consists of a neural network that acts as an approximating
function of the damage evolution with its output regularized using the residue
of the differential equation. Three stages of optimization seek the best
possible values for the neural network and the material parameters. The
training alternates between the fitting of only the pseudo-experimental data or
the total loss that includes the regularizing terms. We test the robustness of
the method to noisy data and its generalization capabilities using a simple
physical case for the damage model. This procedure deals better with noisy data
in comparison with a PDE-constrained optimization method, and it also provides
good approximations of the material parameters and the evolution of damage.
|
[{'version': 'v1', 'created': 'Fri, 25 Jun 2021 13:04:00 GMT'}]
|
2021-07-21
|
Chenxi Sui, Yao-Yu Li, Xiuqiang Li, Genesis Higueros, Keyu Wang,
Wanrong Xie, Po-Chun Hsu
|
Bio-inspired vascularized electrodes for high-performance fast-charging
batteries designed by deep learning
| null | null | null |
cond-mat.mtrl-sci
|
Slow ionic transport and high voltage drop (IR drop) of homogeneous porous
electrodes are the critical causes of severe performance degradation of
lithium-ion (Li-ion) batteries under high charging rates. Herein, we
demonstrate that a bio-inspired vascularized porous electrode can
simultaneously solve these two problems by introducing low tortuous channels
and graded porosity. To optimize the vasculature structural parameters, we
employ artificial neural networks (ANNs) to accelerate the computation of
possible structures with high accuracy. Furthermore, an inverse-design
searching library is compiled to find the optimal vascular structures under
different industrial fabrication and design criteria. The prototype delivers a
customizable package containing optimal geometric parameters and their
uncertainty and sensitivity analysis. Finally, the full-vascularized cell shows
a 66% improvement of charging capacity than the traditional homogeneous cell
under 3.2C current density. This research provides an innovative methodology to
solve the fast-charging problem in batteries and broaden the applicability of
deep learning algorithm to different scientific or engineering areas.
|
[{'version': 'v1', 'created': 'Thu, 29 Jul 2021 02:30:19 GMT'}]
|
2021-07-30
|
Aur\`ele Goetz, Ali Riza Durmaz, Martin M\"uller, Akhil Thomas,
Dominik Britz, Pierre Kerfriden and Chris Eberl
|
Addressing materials' microstructure diversity using transfer learning
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Materials' microstructures are signatures of their alloying composition and
processing history. Therefore, microstructures exist in a wide variety. As
materials become increasingly complex to comply with engineering demands,
advanced computer vision (CV) approaches such as deep learning (DL) inevitably
gain relevance for quantifying microstrucutures' constituents from micrographs.
While DL can outperform classical CV techniques for many tasks, shortcomings
are poor data efficiency and generalizability across datasets. This is
inherently in conflict with the expense associated with annotating materials
data through experts and extensive materials diversity. To tackle poor domain
generalizability and the lack of labeled data simultaneously, we propose to
apply a sub-class of transfer learning methods called unsupervised domain
adaptation (UDA). These algorithms address the task of finding domain-invariant
features when supplied with annotated source data and unannotated target data,
such that performance on the latter distribution is optimized despite the
absence of annotations. Exemplarily, this study is conducted on a lath-shaped
bainite segmentation task in complex phase steel micrographs. Here, the domains
to bridge are selected to be different metallographic specimen preparations
(surface etchings) and distinct imaging modalities. We show that a
state-of-the-art UDA approach surpasses the na\"ive application of source
domain trained models on the target domain (generalization baseline) to a large
extent. This holds true independent of the domain shift, despite using little
data, and even when the baseline models were pre-trained or employed data
augmentation. Through UDA, mIoU was improved over generalization baselines from
82.2%, 61.0%, 49.7% to 84.7%, 67.3%, 73.3% on three target datasets,
respectively. This underlines this techniques' potential to cope with materials
variance.
|
[{'version': 'v1', 'created': 'Thu, 29 Jul 2021 09:13:11 GMT'}]
|
2021-07-30
|
Ryo Tamura, Momo Matsuda, Jianbo Lin, Yasunori Futamura, Tetsuya
Sakurai, Tsuyoshi Miyazaki
|
Unsupervised learning-based structural analysis: Search for a
characteristic low-dimensional space by local structures in atomistic
simulations
| null |
10.1103/PhysRevB.105.075107
| null |
cond-mat.mtrl-sci
|
Owing to the advances in computational techniques and the increase in
computational power, atomistic simulations of materials can simulate large
systems with higher accuracy. Complex phenomena can be observed in such
state-of-the-art atomistic simulations. However, it has become increasingly
difficult to understand what is actually happening and mechanisms, for example,
in molecular dynamics (MD) simulations. We propose an unsupervised machine
learning method to analyze the local structure around a target atom. The
proposed method, which uses the two-step locality preserving projections
(TS-LPP), can find a low-dimensional space wherein the distributions of
datapoints for each atom or groups of atoms can be properly captured. We
demonstrate that the method is effective for analyzing the MD simulations of
crystalline, liquid, and amorphous states and the melt-quench process from the
perspective of local structures. The proposed method is demonstrated on a
silicon single-component system, a silicon-germanium binary system, and a
copper single-component system.
|
[{'version': 'v1', 'created': 'Thu, 29 Jul 2021 20:19:42 GMT'}]
|
2022-02-16
|
Johannes Allotey, Keith T. Butler and Jeyan Thiyagalingam
|
Entropy-based Active Learning of Graph Neural Network Surrogate Models
for Materials Properties
| null |
10.1063/5.0065694
| null |
cond-mat.mtrl-sci
|
Graph neural networks, trained on experimental or calculated data are
becoming an increasingly important tool in computational materials science.
Networks, once trained, are able to make highly accurate predictions at a
fraction of the cost of experiments or first-principles calculations of
comparable accuracy. However these networks typically rely on large databases
of labelled experiments to train the model. In scenarios where data is scarce
or expensive to obtain this can be prohibitive. By building a neural network
that provides a confidence on the predicted properties, we are able to develop
an active learning scheme that can reduce the amount of labelled data required,
by identifying the areas of chemical space where the model is most uncertain.
We present a scheme for coupling a graph neural network with a Gaussian process
to featurise solid-state materials and predict properties \textit{including} a
measure of confidence in the prediction. We then demonstrate that this scheme
can be used in an active learning context to speed up the training of the
model, by selecting the optimal next experiment for obtaining a data label. Our
active learning scheme can double the rate at which the performance of the
model on a test data set improves with additional data compared to choosing the
next sample at random. This type of uncertainty quantification and active
learning has the potential to open up new areas of materials science, where
data are scarce and expensive to obtain, to the transformative power of graph
neural networks.
|
[{'version': 'v1', 'created': 'Wed, 4 Aug 2021 14:22:57 GMT'}, {'version': 'v2', 'created': 'Fri, 13 Aug 2021 10:49:29 GMT'}]
|
2024-06-19
|
Leonid Kahle and Federico Zipoli
|
On the Quality of Uncertainty Estimates from Neural Network Potential
Ensembles
|
Phys. Rev. E 105 (2022), 015311
|
10.1103/PhysRevE.105.015311
| null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Neural network potentials (NNPs) combine the computational efficiency of
classical interatomic potentials with the high accuracy and flexibility of the
ab initio methods used to create the training set, but can also result in
unphysical predictions when employed outside their training set distribution.
Estimating the epistemic uncertainty of an NNP is required in active learning
or on-the-fly generation of potentials. Inspired from their use in other
machine-learning applications, NNP ensembles have been used for uncertainty
prediction in several studies, with the caveat that ensembles do not provide a
rigorous Bayesian estimate of the uncertainty. To test whether NNP ensembles
provide accurate uncertainty estimates, we train such ensembles in four
different case studies, and compare the predicted uncertainty with the errors
on out-of-distribution validation sets. Our results indicate that NNP ensembles
are often overconfident, underestimating the uncertainty of the model, and
require to be calibrated for each system and architecture. We also provide
evidence that Bayesian NNPs, obtained by sampling the posterior distribution of
the model parameters using Monte-Carlo techniques, can provide better
uncertainty estimates.
|
[{'version': 'v1', 'created': 'Thu, 12 Aug 2021 13:36:51 GMT'}, {'version': 'v2', 'created': 'Fri, 21 Jan 2022 17:17:12 GMT'}]
|
2022-01-24
|
Suheng Xu, Alexander S. McLeod, Xinzhong Chen, Daniel J. Rizzo, Bjarke
S. Jessen, Ziheng Yao, Zhiyuan Sun, Sara Shabani, Abhay N. Pasupathy, Andrew
J. Millis, Cory R. Dean, James C. Hone, Mengkun Liu, D. N. Basov
|
Deep learning analysis of polaritonic waves images
|
ACS Nano 15, 11, 18182-18191(2020)
|
10.1021/acsnano.1c07011
| null |
cond-mat.mtrl-sci physics.data-an physics.optics
|
Deep learning (DL) is an emerging analysis tool across sciences and
engineering. Encouraged by the successes of DL in revealing quantitative trends
in massive imaging data, we applied this approach to nano-scale deeply
sub-diffractional images of propagating polaritonic waves in complex materials.
We developed a practical protocol for the rapid regression of images that
quantifies the wavelength and the quality factor of polaritonic waves utilizing
the convolutional neural network (CNN). Using simulated near-field images as
training data, the CNN can be made to simultaneously extract polaritonic
characteristics and materials parameters in a timescale that is at least three
orders of magnitude faster than common fitting/processing procedures. The
CNN-based analysis was validated by examining the experimental near-field
images of charge-transfer plasmon polaritons at Graphene/{\alpha}-RuCl3
interfaces. Our work provides a general framework for extracting quantitative
information from images generated with a variety of scanning probe methods.
|
[{'version': 'v1', 'created': 'Wed, 11 Aug 2021 02:33:41 GMT'}, {'version': 'v2', 'created': 'Wed, 10 Jul 2024 05:26:52 GMT'}]
|
2024-07-11
|
Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley,
Kevin G. Field, and Dane Morgan
|
A Deep Learning Based Automatic Defect Analysis Framework for In-situ
TEM Ion Irradiations
| null |
10.1016/j.commatsci.2021.110560
| null |
cs.CV cond-mat.mtrl-sci
|
Videos captured using Transmission Electron Microscopy (TEM) can encode
details regarding the morphological and temporal evolution of a material by
taking snapshots of the microstructure sequentially. However, manual analysis
of such video is tedious, error-prone, unreliable, and prohibitively
time-consuming if one wishes to analyze a significant fraction of frames for
even videos of modest length. In this work, we developed an automated TEM video
analysis system for microstructural features based on the advanced object
detection model called YOLO and tested the system on an in-situ ion irradiation
TEM video of dislocation loops formed in a FeCrAl alloy. The system provides
analysis of features observed in TEM including both static and dynamic
properties using the YOLO-based defect detection module coupled to a geometry
analysis module and a dynamic tracking module. Results show that the system can
achieve human comparable performance with an F1 score of 0.89 for fast,
consistent, and scalable frame-level defect analysis. This result is obtained
on a real but exceptionally clean and stable data set and more challenging data
sets may not achieve this performance. The dynamic tracking also enabled
evaluation of individual defect evolution like per defect growth rate at a
fidelity never before achieved using common human analysis methods. Our work
shows that automatically detecting and tracking interesting microstructures and
properties contained in TEM videos is viable and opens new doors for evaluating
materials dynamics.
|
[{'version': 'v1', 'created': 'Thu, 19 Aug 2021 19:15:44 GMT'}]
|
2021-08-23
|
Mingren Shen, Guanzhao Li, Dongxia Wu, Yuhan Liu, Jacob Greaves, Wei
Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan
Sanchez, Oigimer Torres, Wei Li, Kevin Field, and Dane Morgan
|
Multi defect detection and analysis of electron microscopy images with
deep learning
| null |
10.1016/j.commatsci.2021.110576
| null |
cs.CV cond-mat.mtrl-sci
|
Electron microscopy is widely used to explore defects in crystal structures,
but human detecting of defects is often time-consuming, error-prone, and
unreliable, and is not scalable to large numbers of images or real-time
analysis. In this work, we discuss the application of machine learning
approaches to find the location and geometry of different defect clusters in
irradiated steels. We show that a deep learning based Faster R-CNN analysis
system has a performance comparable to human analysis with relatively small
training data sets. This study proves the promising ability to apply deep
learning to assist the development of automated microscopy data analysis even
when multiple features are present and paves the way for fast, scalable, and
reliable analysis systems for massive amounts of modern electron microscopy
data.
|
[{'version': 'v1', 'created': 'Thu, 19 Aug 2021 19:16:24 GMT'}]
|
2021-08-23
|
Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan
Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes
|
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with
Constraint Reasoning
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.AI
|
Crystal-structure phase mapping is a core, long-standing challenge in
materials science that requires identifying crystal structures, or mixtures
thereof, in synthesized materials. Materials science experts excel at solving
simple systems but cannot solve complex systems, creating a major bottleneck in
high-throughput materials discovery. Herein we show how to automate
crystal-structure phase mapping. We formulate phase mapping as an unsupervised
pattern demixing problem and describe how to solve it using Deep Reasoning
Networks (DRNets). DRNets combine deep learning with constraint reasoning for
incorporating scientific prior knowledge and consequently require only a modest
amount of (unlabeled) data. DRNets compensate for the limited data by
exploiting and magnifying the rich prior knowledge about the thermodynamic
rules governing the mixtures of crystals with constraint reasoning seamlessly
integrated into neural network optimization. DRNets are designed with an
interpretable latent space for encoding prior-knowledge domain constraints and
seamlessly integrate constraint reasoning into neural network optimization.
DRNets surpass previous approaches on crystal-structure phase mapping,
unraveling the Bi-Cu-V oxide phase diagram, and aiding the discovery of
solar-fuels materials.
|
[{'version': 'v1', 'created': 'Sat, 21 Aug 2021 15:01:38 GMT'}]
|
2021-08-24
|
Arindam Debnath, Adam M. Krajewski, Hui Sun, Shuang Lin, Marcia Ahn,
Wenjie Li, Shanshank Priya, Jogender Singh, Shunli Shang, Allison M. Beese,
Zi-Kui Liu, Wesley F. Reinhart
|
Generative deep learning as a tool for inverse design of high-entropy
refractory alloys
| null |
10.20517/jmi.2021.05
| null |
cond-mat.mtrl-sci
|
Generative deep learning is powering a wave of new innovations in materials
design. In this article, we discuss the basic operating principles of these
methods and their advantages over rational design through the lens of a case
study on refractory high-entropy alloys for ultra-high-temperature
applications. We present our computational infrastructure and workflow for the
inverse design of new alloys powered by these methods. Our preliminary results
show that generative models can learn complex relationships in order to
generate novelty on demand, making them a valuable tool for materials
informatics.
|
[{'version': 'v1', 'created': 'Thu, 26 Aug 2021 19:59:45 GMT'}, {'version': 'v2', 'created': 'Tue, 31 Aug 2021 18:25:06 GMT'}]
|
2023-01-03
|
Junqi Yin and Zongrui Pei and Michael Gao
|
Neural network based order parameter for phase transitions and its
applications in high-entropy alloys
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Phase transition is one of the most important phenomena in nature and plays a
central role in materials design. All phase transitions are characterized by
suitable order parameters, including the order-disorder phase transition.
However, finding a representative order parameter for complex systems is
nontrivial, such as for high-entropy alloys. Given variational autoencoder's
(VAE) strength of reducing high dimensional data into few principal components,
here we coin a new concept of "VAE order parameter". We propose that the
Manhattan distance in the VAE latent space can serve as a generic order
parameter for order-disorder phase transitions. The physical properties of the
order parameter are quantitatively interpreted and demonstrated by multiple
refractory high-entropy alloys. Assisted by it, a generally applicable alloy
design concept is proposed by mimicking the nature mixing of elements. Our
physically interpretable "VAE order parameter" lays the foundation for the
understanding of and alloy design by chemical ordering.
|
[{'version': 'v1', 'created': 'Sun, 12 Sep 2021 19:54:36 GMT'}]
|
2021-09-14
|
Seunghyun Moon, Ruimin Ma, Ross Attardo, Charles Tomonto, Mark Nordin,
Paul Wheelock, Michael Glavicic, Maxwell Layman, Richard Billo, Tengfei Luo
|
Impact of Surface and Pore Characteristics on Fatigue Life of Laser
Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.app-ph
|
In this study, the effects of surface roughness and pore characteristics on
fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were
investigated. The 197 fatigue bars were printed using the same laser power but
with varied scanning speeds. These actions led to variations in the geometries
of microscale pores, and such variations were characterized using
micro-computed tomography. To generate differences in surface roughness in
fatigue bars, half of the samples were grit-blasted and the other half
machined. Fatigue behaviors were analyzed with respect to surface roughness and
statistics of the pores. For the grit-blasted samples, the contour laser scan
in the LPBF strategy led to a pore-depletion zone isolating surface and
internal pores with different features. For the machined samples, where surface
pores resemble internal pores, the fatigue life was highly correlated with the
average pore size and projected pore area in the plane perpendicular to the
stress direction. Finally, a machine learning model using a drop-out neural
network (DONN) was employed to establish a link between surface and pore
features to the fatigue data (logN), and good prediction accuracy was
demonstrated. Besides predicting fatigue lives, the DONN can also estimate the
prediction uncertainty.
|
[{'version': 'v1', 'created': 'Sat, 28 Aug 2021 02:51:04 GMT'}]
|
2021-09-21
|
Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna
Balaprakash, Ross J. Harder, and Mathew J. Cherukara
|
AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale
Bragg Coherent Diffraction Imaging
| null | null | null |
physics.app-ph cond-mat.mtrl-sci cs.AI cs.CV
|
The problem of phase retrieval, or the algorithmic recovery of lost phase
information from measured intensity alone, underlies various imaging methods
from astronomy to nanoscale imaging. Traditional methods of phase retrieval are
iterative in nature, and are therefore computationally expensive and time
consuming. More recently, deep learning (DL) models have been developed to
either provide learned priors to iterative phase retrieval or in some cases
completely replace phase retrieval with networks that learn to recover the lost
phase information from measured intensity alone. However, such models require
vast amounts of labeled data, which can only be obtained through simulation or
performing computationally prohibitive phase retrieval on hundreds of or even
thousands of experimental datasets. Using a 3D nanoscale X-ray imaging modality
(Bragg Coherent Diffraction Imaging or BCDI) as a representative technique, we
demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase
problem without labeled data. By incorporating the physics of the imaging
technique into the DL model during training, AutoPhaseNN learns to invert 3D
BCDI data from reciprocal space to real space in a single shot without ever
being shown real space images. Once trained, AutoPhaseNN is about one hundred
times faster than traditional iterative phase retrieval methods while providing
comparable image quality.
|
[{'version': 'v1', 'created': 'Tue, 28 Sep 2021 21:16:34 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Apr 2022 15:11:33 GMT'}]
|
2022-04-05
|
Tanishq Gupta, Mohd Zaki, N. M. Anoop Krishnan, Mausam
|
MatSciBERT: A Materials Domain Language Model for Text Mining and
Information Extraction
| null | null | null |
cs.CL cond-mat.mtrl-sci
|
An overwhelmingly large amount of knowledge in the materials domain is
generated and stored as text published in peer-reviewed scientific literature.
Recent developments in natural language processing, such as bidirectional
encoder representations from transformers (BERT) models, provide promising
tools to extract information from these texts. However, direct application of
these models in the materials domain may yield suboptimal results as the models
themselves may not be trained on notations and jargon that are specific to the
domain. Here, we present a materials-aware language model, namely, MatSciBERT,
which is trained on a large corpus of scientific literature published in the
materials domain. We further evaluate the performance of MatSciBERT on three
downstream tasks, namely, abstract classification, named entity recognition,
and relation extraction, on different materials datasets. We show that
MatSciBERT outperforms SciBERT, a language model trained on science corpus, on
all the tasks. Further, we discuss some of the applications of MatSciBERT in
the materials domain for extracting information, which can, in turn, contribute
to materials discovery or optimization. Finally, to make the work accessible to
the larger materials community, we make the pretrained and finetuned weights
and the models of MatSciBERT freely accessible.
|
[{'version': 'v1', 'created': 'Thu, 30 Sep 2021 17:35:02 GMT'}]
|
2021-10-01
|
Hongyu Yu, Changsong Xu, Feng Lou, L. Bellaiche, Zhenpeng Hu, Xingao
Gong, Hongjun Xiang
|
Complex Spin Hamiltonian Represented by Artificial Neural Network
|
Phys. Rev. B 105, (2022)
|
10.1103/PhysRevB.105.174422
| null |
cond-mat.mtrl-sci cs.LG
|
The effective spin Hamiltonian method is widely adopted to simulate and
understand the behavior of magnetism. However, the magnetic interactions of
some systems, such as itinerant magnets, are too complex to be described by any
explicit function, which prevents an accurate description of magnetism in such
systems. Here, we put forward a machine learning (ML) approach, applying an
artificial neural network (ANN) and a local spin descriptor to develop
effective spin potentials for any form of interaction. The constructed
Hamiltonians include an explicit Heisenberg part and an implicit non-linear ANN
part. Such a method successfully reproduces artificially constructed models and
also sufficiently describe the itinerant magnetism of bulk Fe3GeTe2. Our work
paves a new way for investigating complex magnetic phenomena (e.g., skyrmions)
of magnetic materials.
|
[{'version': 'v1', 'created': 'Sat, 2 Oct 2021 04:38:28 GMT'}]
|
2022-05-20
|
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
|
Lagrangian Neural Network with Differentiable Symmetries and Relational
Inductive Bias
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.AI math.DS
|
Realistic models of physical world rely on differentiable symmetries that, in
turn, correspond to conservation laws. Recent works on Lagrangian and
Hamiltonian neural networks show that the underlying symmetries of a system can
be easily learned by a neural network when provided with an appropriate
inductive bias. However, these models still suffer from issues such as
inability to generalize to arbitrary system sizes, poor interpretability, and
most importantly, inability to learn translational and rotational symmetries,
which lead to the conservation laws of linear and angular momentum,
respectively. Here, we present a momentum conserving Lagrangian neural network
(MCLNN) that learns the Lagrangian of a system, while also preserving the
translational and rotational symmetries. We test our approach on linear and
non-linear spring systems, and a gravitational system, demonstrating the energy
and momentum conservation. We also show that the model developed can generalize
to systems of any arbitrary size. Finally, we discuss the interpretability of
the MCLNN, which directly provides physical insights into the interactions of
multi-particle systems.
|
[{'version': 'v1', 'created': 'Thu, 7 Oct 2021 08:49:57 GMT'}, {'version': 'v2', 'created': 'Tue, 12 Oct 2021 04:41:08 GMT'}]
|
2021-10-13
|
Aldair E. Gongora, Siddharth Mysore, Beichen Li, Wan Shou, Wojciech
Matusik, Elise F. Morgan, Keith A. Brown, Emily Whiting
|
Designing Composites with Target Effective Young's Modulus using
Reinforcement Learning
| null | null | null |
cond-mat.mtrl-sci cs.GR cs.LG
|
Advancements in additive manufacturing have enabled design and fabrication of
materials and structures not previously realizable. In particular, the design
space of composite materials and structures has vastly expanded, and the
resulting size and complexity has challenged traditional design methodologies,
such as brute force exploration and one factor at a time (OFAT) exploration, to
find optimum or tailored designs. To address this challenge, supervised machine
learning approaches have emerged to model the design space using curated
training data; however, the selection of the training data is often determined
by the user. In this work, we develop and utilize a Reinforcement learning
(RL)-based framework for the design of composite structures which avoids the
need for user-selected training data. For a 5 $\times$ 5 composite design space
comprised of soft and compliant blocks of constituent material, we find that
using this approach, the model can be trained using 2.78% of the total design
space consists of $2^{25}$ design possibilities. Additionally, the developed
RL-based framework is capable of finding designs at a success rate exceeding
90%. The success of this approach motivates future learning frameworks to
utilize RL for the design of composites and other material systems.
|
[{'version': 'v1', 'created': 'Thu, 7 Oct 2021 05:44:48 GMT'}]
|
2021-10-12
|
Ryan Jacobs, Mingren Shen, Yuhan Liu, Wei Hao, Xiaoshan Li, Ruoyu He,
Jacob RC Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G.
Field, Dane Morgan
|
Performance, Successes and Limitations of Deep Learning Semantic
Segmentation of Multiple Defects in Transmission Electron Micrographs
| null | null | null |
cs.CV cond-mat.mtrl-sci
|
In this work, we perform semantic segmentation of multiple defect types in
electron microscopy images of irradiated FeCrAl alloys using a deep learning
Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an
in-depth analysis of key model performance statistics, with a focus on
quantities such as predicted distributions of defect shapes, defect sizes, and
defect areal densities relevant to informing modeling and understanding of
irradiated Fe-based materials properties. To better understand the performance
and present limitations of the model, we provide examples of useful evaluation
tests which include a suite of random splits, and dataset size-dependent and
domain-targeted cross validation tests. Overall, we find that the current model
is a fast, effective tool for automatically characterizing and quantifying
multiple defect types in microscopy images, with a level of accuracy on par
with human domain expert labelers. More specifically, the model can achieve
average defect identification F1 scores as high as 0.8, and, based on random
cross validation, have low overall average (+/- standard deviation) defect size
and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%,
respectively. Further, our model predicts the expected material hardening to
within 10-20 MPa (about 10% of total hardening), which is about the same error
level as experiments. Our targeted evaluation tests also suggest the best path
toward improving future models is not expanding existing databases with more
labeled images but instead data additions that target weak points of the model
domain, such as images from different microscopes, imaging conditions,
irradiation environments, and alloy types. Finally, we discuss the first phase
of an effort to provide an easy-to-use, open-source object detection tool to
the broader community for identifying defects in new images.
|
[{'version': 'v1', 'created': 'Fri, 15 Oct 2021 17:57:59 GMT'}]
|
2021-10-18
|
Qi-Jun Hong
|
A melting temperature database and a neural network model for melting
temperature prediction
| null | null | null |
cond-mat.mtrl-sci
|
I build a melting temperature database that contains approximately 10,000
materials. Based on the database, I build a machine learning model that
predicts melting temperature in seconds. The model features graph neural
network and residual neural network architecture. The root-mean-square errors
of melting temperature are 90 and 160K for training and testing, respectively.
The model is deployed online and is publicly available.
|
[{'version': 'v1', 'created': 'Wed, 20 Oct 2021 19:42:49 GMT'}, {'version': 'v2', 'created': 'Sat, 30 Oct 2021 00:00:48 GMT'}]
|
2021-11-02
|
Baoqin Fu and Yandong Sun and Linfeng Zhang and Han Wang and Ben Xu
|
Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of
Silicon Carbide with Quantum Accuracy
| null | null | null |
cond-mat.mtrl-sci
|
Silicon carbide (SiC) is an essential material for next generation
semiconductors and components for nuclear plants. It's applications are
strongly dependent on its thermal conductivity, which is highly sensitive to
microstructures. Molecular dynamics (MD) simulation is the most used methods to
address thermal transportation mechanisms in devices or microstructures of
nano-meters. However, the implementation of MD is limited in SiC because of
lacking accurate inter-atomic potentials. In this work, using the Deep
Potential (DP) methodology, we developed two inter-atomic potentials (DP-IAPs)
for SiC based on two adaptively generated datasets within the density
functional approximations at the local density and the generalized gradient
levels. These two DP-IAPs manifest their speed with quantum accuracy in lattice
dynamics simulations as well as scattering rate analysis of phonon
transportation. Combining with molecular dynamics simulations, the thermal
transport and mechanical properties were systematically investigated. The
presented methodology and the inter-atomic potentials pave the way for a
systematic approach to model heat transport in SiC related devices using
multiscale modelling.
|
[{'version': 'v1', 'created': 'Thu, 21 Oct 2021 01:00:49 GMT'}]
|
2021-10-22
|
Vu Ngoc Tuoc, Nga T. T. Nguyen, Vinit Sharma, Tran Doan Huan
|
Probabilistic deep learning approach for targeted hybrid
organic-inorganic perovskites
|
Phys. Rev. Materials 5, 125402 (2021)
|
10.1103/PhysRevMaterials.5.125402
| null |
cond-mat.mtrl-sci
|
We develop a probabilistic machine learning model and use it to screen for
new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band
gap. The data set used for this work is highly diverse, containing multiple
atomic structures for each of 192 chemically distinct HOIP formulas. Therefore,
any property prediction on a given formula must be associated with an
irreducible "uncertainty" that comes from its unknown atomic details. As a
result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50
eV were identified and validated against suitable first-principles
computations. Through this demonstration we show that the probabilistic deep
learning approach is robust, versatile, and can be used to properly quantify
this uncertainty. In conclusion, the probabilistic standpoint and approach
described herein could be widely useful for the very common and inevitable data
uncertainty which is rooted at the incompleteness of information during
experiments and/or computations.
|
[{'version': 'v1', 'created': 'Mon, 25 Oct 2021 13:54:16 GMT'}, {'version': 'v2', 'created': 'Tue, 7 Dec 2021 16:18:14 GMT'}]
|
2021-12-08
|
Ru Yang, Yang Li, Danielle Zeng, Ping Guo
|
Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End
Displacement and Strain Measurement
|
Journal of Materials Processing Technology (2021): 117474
|
10.1016/j.jmatprotec.2021.117474
| null |
eess.IV cond-mat.mtrl-sci cs.CV
|
Digital image correlation (DIC) has become an industry standard to retrieve
accurate displacement and strain measurement in tensile testing and other
material characterization. Though traditional DIC offers a high precision
estimation of deformation for general tensile testing cases, the prediction
becomes unstable at large deformation or when the speckle patterns start to
tear. In addition, traditional DIC requires a long computation time and often
produces a low spatial resolution output affected by filtering and speckle
pattern quality. To address these challenges, we propose a new deep
learning-based DIC approach--Deep DIC, in which two convolutional neural
networks, DisplacementNet and StrainNet, are designed to work together for
end-to-end prediction of displacements and strains. DisplacementNet predicts
the displacement field and adaptively tracks a region of interest. StrainNet
predicts the strain field directly from the image input without relying on the
displacement prediction, which significantly improves the strain prediction
accuracy. A new dataset generation method is developed to synthesize a
realistic and comprehensive dataset, including the generation of speckle
patterns and the deformation of the speckle image with synthetic displacement
fields. Though trained on synthetic datasets only, Deep DIC gives highly
consistent and comparable predictions of displacement and strain with those
obtained from commercial DIC software for real experiments, while it
outperforms commercial software with very robust strain prediction even at
large and localized deformation and varied pattern qualities. In addition, Deep
DIC is capable of real-time prediction of deformation with a calculation time
down to milliseconds.
|
[{'version': 'v1', 'created': 'Tue, 26 Oct 2021 14:13:57 GMT'}, {'version': 'v2', 'created': 'Thu, 6 Jan 2022 20:23:24 GMT'}]
|
2022-01-10
|
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca
Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon J. L.
Billinge, Elizabeth Holm, Shyue Ping Ong and Chris Wolverton
|
Recent Advances and Applications of Deep Learning Methods in Materials
Science
| null |
10.1038/s41524-022-00734-6
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Deep learning (DL) is one of the fastest growing topics in materials data
science, with rapidly emerging applications spanning atomistic, image-based,
spectral, and textual data modalities. DL allows analysis of unstructured data
and automated identification of features. Recent development of large materials
databases has fueled the application of DL methods in atomistic prediction in
particular. In contrast, advances in image and spectral data have largely
leveraged synthetic data enabled by high quality forward models as well as by
generative unsupervised DL methods. In this article, we present a high-level
overview of deep-learning methods followed by a detailed discussion of recent
developments of deep learning in atomistic simulation, materials imaging,
spectral analysis, and natural language processing. For each modality we
discuss applications involving both theoretical and experimental data, typical
modeling approaches with their strengths and limitations, and relevant publicly
available software and datasets. We conclude the review with a discussion of
recent cross-cutting work related to uncertainty quantification in this field
and a brief perspective on limitations, challenges, and potential growth areas
for DL methods in materials science. The application of DL methods in materials
science presents an exciting avenue for future materials discovery and design.
|
[{'version': 'v1', 'created': 'Thu, 28 Oct 2021 00:09:04 GMT'}]
|
2022-05-09
|
Anindya Bhaduri, Ashwini Gupta, Lori Graham-Brady
|
Stress field prediction in fiber-reinforced composite materials using a
deep learning approach
| null |
10.1016/j.compositesb.2022.109879
| null |
cond-mat.mtrl-sci cs.LG
|
Computational stress analysis is an important step in the design of material
systems. Finite element method (FEM) is a standard approach of performing
stress analysis of complex material systems. A way to accelerate stress
analysis is to replace FEM with a data-driven machine learning based stress
analysis approach. In this study, we consider a fiber-reinforced matrix
composite material system and we use deep learning tools to find an alternative
to the FEM approach for stress field prediction. We first try to predict stress
field maps for composite material systems of fixed number of fibers with
varying spatial configurations. Specifically, we try to find a mapping between
the spatial arrangement of the fibers in the composite material and the
corresponding von Mises stress field. This is achieved by using a convolutional
neural network (CNN), specifically a U-Net architecture, using true stress maps
of systems with same number of fibers as training data. U-Net is a
encoder-decoder network which in this study takes in the composite material
image as an input and outputs the stress field image which is of the same size
as the input image. We perform a robustness analysis by taking different
initializations of the training samples to find the sensitivity of the
prediction accuracy to the small number of training samples. When the number of
fibers in the composite material system is increased for the same volume
fraction, a finer finite element mesh discretization is required to represent
the geometry accurately. This leads to an increase in the computational cost.
Thus, the secondary goal here is to predict the stress field for systems with
larger number of fibers with varying spatial configurations using information
from the true stress maps of relatively cheaper systems of smaller fiber
number.
|
[{'version': 'v1', 'created': 'Mon, 1 Nov 2021 01:52:27 GMT'}]
|
2023-01-02
|
Van-Quyen Nguyen, Viet-Cuong Nguyen, Tien-Cuong Nguyen, Tien-Lam Pham
|
Pairwise interactions for Potential energy surfaces and Atomic forces
with Deep Neural network
| null | null | null |
cond-mat.mtrl-sci
|
Molecular dynamics (MD) simulation, which is considered an important tool for
studying physical and chemical processes at the atomic scale, requires accurate
calculations of energies and forces. Although reliable energies and forces can
be obtained by electronic structure calculations such as those based on density
functional theory (DFT), this approach is computationally expensive. In this
work, we propose a full-stack model using deep neural network (NN) to enhance
the calculation of force and energy, in which the NN is designed to extract the
embedding feature of pairwise interactions of an atom and its neighbors, which
are aggregated to obtain its feature vector for predicting atomic force and
potential energy. By designing the features of the pairwise interactions, we
can control the performance of models and take into account the many-body
effects and other physics of the atomic interactions. Moreover, we demonstrated
that using the Coulomb matrix of the local structures in complement to the
pairwise information, we can improve the prediction of force and energy for
silicon systems and the transferability of our models is confirmed to larger
systems, with high accuracy.
|
[{'version': 'v1', 'created': 'Wed, 10 Nov 2021 09:51:16 GMT'}, {'version': 'v2', 'created': 'Fri, 3 Dec 2021 08:58:58 GMT'}]
|
2021-12-06
|
Andrea Pedrielli, Paolo E. Trevisanutto, Lorenzo Monacelli, Giovanni
Garberoglio, Nicola M. Pugno, Simone Taioli
|
Understanding Anharmonic Effects on Hydrogen Desorption Characteristics
of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech cs.LG
|
Magnesium hydride (MgH$_2$) has been widely studied for effective hydrogen
storage. However, its bulk desorption temperature (553 K) is deemed too high
for practical applications. Besides doping, a strategy to decrease such
reaction energy for releasing hydrogen is the use of MgH$_2$-based
nanoparticles (NPs). Here, we investigate first the thermodynamic properties of
Mg$_n$H$_{2n}$ NPs ($n<10$) from first-principles, in particular by assessing
the anharmonic effects on the enthalpy, entropy and thermal expansion by means
of the Stochastic Self Consistent Harmonic Approximation (SSCHA). The latter
method goes beyond previous approaches, typically based on molecular mechanics
and the quasi-harmonic approximation, allowing the ab initio calculation of the
fully-anharmonic free energy. We find an almost linear dependence on
temperature of the interatomic bond lengths - with a relative variation of few
percent over 300K -, alongside with a bond distance decrease of the Mg-H bonds.
In order to increase the size of NPs toward experiments of hydrogen desorption
from MgH$_2$ we devise a computationally effective Machine Learning model
trained to accurately determine the forces and total energies (i.e. the
potential energy surfaces), integrating the latter with the SSCHA model to
fully include the anharmonic effects. We find a significative decrease of the
H-desorption temperature for sub-nanometric clusters Mg$_n$H$_{2n}$ with $n
\leq 10$, with a non-negligible, although little effect due to anharmonicities
(up to 10%).
|
[{'version': 'v1', 'created': 'Sat, 27 Nov 2021 18:33:58 GMT'}]
|
2021-11-30
|
T Martinez Ostormujof (LEM3), Rrp Purushottam Raj Purohit (LEM3), S
Breumier (LEM3, IRT M2P), Nathalie Gey (LEM3), M Salib, L Germain (LEM3)
|
Deep Learning for automated phase segmentation in EBSD maps. A case
study in Dual Phase steel microstructures
| null | null | null |
cond-mat.mtrl-sci eess.IV
|
Electron Backscattering Diffraction (EBSD) provides important information to
discriminate phase transformation products in steels. This task is
conventionally performed by an expert, who carries a high degree of
subjectivity and requires time and effort. In this paper, we question if
Convolutional Neural Networks (CNNs) are able to extract meaningful features
from EBSD-based data in order to automatically classify the present phases
within a steel microstructure. The selected case of study is ferrite-martensite
discrimination and U-Net has been selected as the network architecture to work
with. Pixel-wise accuracies around ~95% have been obtained when inputting raw
orientation data, while ~98% has been reached with orientation-derived
parameters such as Kernel Average Misorientation (KAM) or pattern quality.
Compared to other available approaches in the literature for phase
discrimination, the models presented here provided higher accuracies in shorter
times. These promising results open a possibility to work on more complex steel
microstructures.
|
[{'version': 'v1', 'created': 'Fri, 26 Nov 2021 10:05:48 GMT'}]
|
2021-12-07
|
Nathan C. Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W.
Coley, Vijay Gadepally
|
Scalable Geometric Deep Learning on Molecular Graphs
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.chem-ph
|
Deep learning in molecular and materials sciences is limited by the lack of
integration between applied science, artificial intelligence, and
high-performance computing. Bottlenecks with respect to the amount of training
data, the size and complexity of model architectures, and the scale of the
compute infrastructure are all key factors limiting the scaling of deep
learning for molecules and materials. Here, we present $\textit{LitMatter}$, a
lightweight framework for scaling molecular deep learning methods. We train
four graph neural network architectures on over 400 GPUs and investigate the
scaling behavior of these methods. Depending on the model architecture,
training time speedups up to $60\times$ are seen. Empirical neural scaling
relations quantify the model-dependent scaling and enable optimal compute
resource allocation and the identification of scalable molecular geometric deep
learning model implementations.
|
[{'version': 'v1', 'created': 'Mon, 6 Dec 2021 21:29:38 GMT'}]
|
2021-12-08
|
Yong Zhao, Edirisuriya MD Siriwardane, Jianjun Hu
|
Physics guided deep learning generative models for crystal materials
discovery
|
AAAI Fall Symposium Series (FSS) 2021
| null | null |
cond-mat.mtrl-sci cs.LG
|
Deep learning based generative models such as deepfake have been able to
generate amazing images and videos. However, these models may need significant
transformation when applied to generate crystal materials structures in which
the building blocks, the physical atoms are very different from the pixels.
Naively transferred generative models tend to generate a large portion of
physically infeasible crystal structures that are not stable or synthesizable.
Herein we show that by exploiting and adding physically oriented data
augmentation, loss function terms, and post processing, our deep adversarial
network (GAN) based generative models can now generate crystal structures with
higher physical feasibility and expand our previous models which can only
create cubic structures.
|
[{'version': 'v1', 'created': 'Tue, 7 Dec 2021 06:54:48 GMT'}]
|
2021-12-15
|
Masud Alam and Liverios Lymperakis
|
Artificial neural network interatomic potential for dislocation and
fracture properties of Molybdenum
| null | null | null |
cond-mat.mtrl-sci
|
A high dimensional artificial neural network interatomic potential for Mo is
developed. To train and validate the potential density functional theory
calculations on structures and properties that correlate to fracture, such as
elastic constants, surface energies, generalized stacking fault energies, and
surface decohesion energies, have been employed. The potential provides total
energies with a root mean square error less than 5\;meV per atom both in the
training and validation data sets. The potential was applied to investigate
screw dislocation core properties as well as to conduct large scale fracture
simulations. These calculations revealed that the 1/2$\langle111\rangle$ screw
dislocation core is non-degenerate and symmetric and mode I fracture is
brittle. It is anticipated that the thus constructed potential is well suited
to be applied in large scale atomistic calculations of plasticity and fracture.
|
[{'version': 'v1', 'created': 'Thu, 9 Dec 2021 00:45:23 GMT'}]
|
2021-12-10
|
Alexander Ryabov, Petr Zhilyaev
|
Application of neural network for exchange-correlation functional
interpolation
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
Density functional theory (DFT) is one of the primary approaches to get a
solution to the many-body Schrodinger equation. The essential part of the DFT
theory is the exchange-correlation (XC) functional, which can not be obtained
in analytical form. Accordingly, the accuracy improvement of the DFT is mainly
based on the development of XC functional approximations. Commonly, they are
built upon analytic solutions in low- and high-density limits and result from
quantum Monte Carlo or post-Hartree-Fock numerical calculations. However, there
is no universal functional form to incorporate these data into XC functional.
Various parameterizations use heuristic rules to build a specific XC
functional. The neural network (NN) approach to interpolate the data from
higher precision theories can give a unified path to parametrize an XC
functional. Moreover, data from many existing quantum chemical databases could
provide the XC functional with improved accuracy. In this work, we develop NN
XC functional, which gives both exchange potential and exchange energy density.
Proposed NN architecture consists of two parts NN-E and NN-V, which could be
trained in separate ways, which adds additional flexibility. We also show the
suitability of the developed NN XC functional in the self-consistent cycle when
applied to atoms, molecules, and crystals.
|
[{'version': 'v1', 'created': 'Thu, 9 Dec 2021 13:08:09 GMT'}]
|
2021-12-10
|
Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar, Connor W. Coley,
Vijay Gadepally
|
Bringing Atomistic Deep Learning to Prime Time
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.chem-ph
|
Artificial intelligence has not yet revolutionized the design of materials
and molecules. In this perspective, we identify four barriers preventing the
integration of atomistic deep learning, molecular science, and high-performance
computing. We outline focused research efforts to address the opportunities
presented by these challenges.
|
[{'version': 'v1', 'created': 'Thu, 9 Dec 2021 15:16:46 GMT'}]
|
2021-12-10
|
Daniel Gleaves, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Nihang
Fu, Jianjun Hu
|
Semi-supervised teacher-student deep neural network for materials
discovery
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Data driven generative machine learning models have recently emerged as one
of the most promising approaches for new materials discovery. While the
generator models can generate millions of candidates, it is critical to train
fast and accurate machine learning models to filter out stable, synthesizable
materials with desired properties. However, such efforts to build supervised
regression or classification screening models have been severely hindered by
the lack of unstable or unsynthesizable samples, which usually are not
collected and deposited in materials databases such as ICSD and Materials
Project (MP). At the same time, there are a significant amount of unlabelled
data available in these databases. Here we propose a semi-supervised deep
neural network (TSDNN) model for high-performance formation energy and
synthesizability prediction, which is achieved via its unique teacher-student
dual network architecture and its effective exploitation of the large amount of
unlabeled data. For formation energy based stability screening, our
semi-supervised classifier achieves an absolute 10.3\% accuracy improvement
compared to the baseline CGCNN regression model. For synthesizability
prediction, our model significantly increases the baseline PU learning's true
positive rate from 87.9\% to 97.9\% using 1/49 model parameters.
To further prove the effectiveness of our models, we combined our
TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to
discover novel stable cubic structures. Out of 1000 recommended candidate
samples by our models, 512 of them have negative formation energies as
validated by our DFT formation energy calculations. Our experimental results
show that our semi-supervised deep neural networks can significantly improve
the screening accuracy in large-scale generative materials design.
|
[{'version': 'v1', 'created': 'Sun, 12 Dec 2021 04:00:21 GMT'}]
|
2021-12-14
|
Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins, Matthew W.
Daniels, Advait Madhavan, Lei Wan, Tiffany S. Santos, Michael Tran, Jordan A.
Katine, Patrick M. Braganca, Mark D. Stiles, and Jabez J. McClelland
|
Implementation of a Binary Neural Network on a Passive Array of Magnetic
Tunnel Junctions
|
Physical Review Applied, 18(1) 014039 (2022)
|
10.1103/PhysRevApplied.18.014039
| null |
cs.ET cond-mat.dis-nn cond-mat.mtrl-sci cs.LG physics.app-ph
|
The increasing scale of neural networks and their growing application space
have produced demand for more energy- and memory-efficient
artificial-intelligence-specific hardware. Avenues to mitigate the main issue,
the von Neumann bottleneck, include in-memory and near-memory architectures, as
well as algorithmic approaches. Here we leverage the low-power and the
inherently binary operation of magnetic tunnel junctions (MTJs) to demonstrate
neural network hardware inference based on passive arrays of MTJs. In general,
transferring a trained network model to hardware for inference is confronted by
degradation in performance due to device-to-device variations, write errors,
parasitic resistance, and nonidealities in the substrate. To quantify the
effect of these hardware realities, we benchmark 300 unique weight matrix
solutions of a 2-layer perceptron to classify the Wine dataset for both
classification accuracy and write fidelity. Despite device imperfections, we
achieve software-equivalent accuracy of up to 95.3 % with proper tuning of
network parameters in 15 x 15 MTJ arrays having a range of device sizes. The
success of this tuning process shows that new metrics are needed to
characterize the performance and quality of networks reproduced in mixed signal
hardware.
|
[{'version': 'v1', 'created': 'Thu, 16 Dec 2021 19:11:29 GMT'}, {'version': 'v2', 'created': 'Fri, 6 May 2022 12:48:41 GMT'}]
|
2022-07-20
|
Kamal Choudhary, Taner Yildirim, Daniel Siderius, Aaron Gilad Kusne,
Austin McDannald, Diana Ortiz-Montalvo
|
Graph Neural Network Predictions of Metal Organic Framework CO2
Adsorption Properties
| null |
10.1016/j.commatsci.2022.111388
| null |
cond-mat.mtrl-sci physics.chem-ph physics.comp-ph
|
The increasing CO2 level is a critical concern and suitable materials are
needed to capture such gases from the environment. While experimental and
conventional computational methods are useful in finding such materials, they
are usually slow and there is a need to expedite such processes. We use
Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption
in metal organic frameworks (MOF), which are known for their high functional
tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with
137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption
isotherms. We develop high accuracy and fast models for pre-screening
applications. We apply the trained model on CoREMOF database and
computationally rank them for experimental synthesis. In addition to the CO2
adsorption isotherm, we also train models for electronic bandgaps, surface
area, void fraction, lowest cavity diameter, and pore limiting diameter, and
illustrate the strength and limitation of such graph neural network models. For
a few candidate MOFs we carry out GCMC calculations to evaluate the
deep-learning (DL) predictions.
|
[{'version': 'v1', 'created': 'Sun, 19 Dec 2021 19:02:25 GMT'}]
|
2023-01-16
|
Simiao Ren, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla
and Jordan M. Malof
|
Inverse deep learning methods and benchmarks for artificial
electromagnetic material design
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Deep learning (DL) inverse techniques have increased the speed of artificial
electromagnetic material (AEM) design and improved the quality of resulting
devices. Many DL inverse techniques have succeeded on a number of AEM design
tasks, but to compare, contrast, and evaluate assorted techniques it is
critical to clarify the underlying ill-posedness of inverse problems. Here we
review state-of-the-art approaches and present a comprehensive survey of deep
learning inverse methods and invertible and conditional invertible neural
networks to AEM design. We produce easily accessible and rapidly implementable
AEM design benchmarks, which offers a methodology to efficiently determine the
DL technique best suited to solving different design challenges. Our
methodology is guided by constraints on repeated simulation and an easily
integrated metric, which we propose expresses the relative ill-posedness of any
AEM design problem. We show that as the problem becomes increasingly ill-posed,
the neural adjoint with boundary loss (NA) generates better solutions faster,
regardless of simulation constraints. On simpler AEM design tasks, direct
neural networks (NN) fare better when simulations are limited, while geometries
predicted by mixture density networks (MDN) and conditional variational
auto-encoders (VAE) can improve with continued sampling and re-simulation.
|
[{'version': 'v1', 'created': 'Sun, 19 Dec 2021 20:44:53 GMT'}]
|
2021-12-21
|
Adu Offei-Danso, Ali Hassanali, Alex Rodriguez
|
High Dimensional Fluctuations in Liquid Water: Combining Chemical
Intuition with Unsupervised Learning
| null | null | null |
cond-mat.soft cond-mat.mtrl-sci
|
The microscopic description of the local structure of water remains an open
challenge. Here, we adopt an agnostic approach to understanding water's
hydrogen bond network using data harvested from molecular dynamics simulations
of an empirical water model. A battery of state-of-the-art unsupervised
data-science techniques are used to characterize the free energy landscape of
water starting from encoding the water environment using local-atomic
descriptors, through dimensionality reduction and finally the use of advanced
clustering techniques. Analysis of the free energy at ambient conditions was
found to be consistent with a rough single basin and independent of the choice
of the water model. We find that the fluctuations of the water network occur in
a high-dimensional space which we characterize using a combination of both
atomic descriptors and chemical-intuition based coordinates. We demonstrate
that a combination of both types of variables are needed in order to adequately
capture the complexity of the fluctuations in the hydrogen bond network at
different length-scales both at room temperature and also close to the critical
point of water. Our results provide a general framework for examining
fluctuations in water under different conditions.
|
[{'version': 'v1', 'created': 'Wed, 22 Dec 2021 14:25:31 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Apr 2022 16:03:23 GMT'}]
|
2022-04-05
|
Pin Chen, Jianwen Chen, Hui Yan, Qing Mo, Zexin Xu, Jinyu Liu, Wenqing
Zhang, Yuedong Yang, Yutong Lu
|
Leveraging Large-scale Computational Database and Deep Learning for
Accurate Prediction of Material Properties
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Accurately predicting the physical and chemical properties of materials
remains one of the most challenging tasks in material design, and one effective
strategy is to construct a reliable data set and use it for training a machine
learning model. In this study, we constructed a large-scale material genome
database (Matgen) containing 76,463 materials collected from
experimentally-observed database, and computed their bandgap properties through
the Density functional theory (DFT) method with Perdew-Burke-Ernzehof (PBE)
functional. We verified the computation method by comparing part of our results
with those from the open Material Project (MP) and Open Quantum Materials
Database (OQMD), all with PBE computations, and found that Matgen achieved the
same computation accuracy based on both measured and computed bandgap
properties. Based on the computed properties of our comprehensive dataset, we
have developed a new graph-based deep learning model, namely CrystalNet,
through our recently developed Communicative Message Passing Neural Network
(CMPNN) framework. The model was shown to outperform other state-of-the-art
prediction models. A further fine-tuning on 1716 experimental bandgap values
(CrystalNet-TL) achieved a superior performance with mean absolute error (MAE)
of 0.77 eV on independent test, which has outperformed the pure PBE (1.14~1.45
eV). Moreover, the model was proven applicable to hypothetical materials with
MAE of 0.77 eV as referred by computations from HSE, a highly accurate quantum
mechanics (QM) method, consist better than PBE (MAE=1.13eV). We also made
material structures, computed properties by PBE, and the CrystalNet models
publically available at https://matgen.nscc-gz.cn.
|
[{'version': 'v1', 'created': 'Wed, 29 Dec 2021 07:32:01 GMT'}]
|
2021-12-30
|
Ankit Shrivastava, Jingxiao Liu, Kaushik Dayal, Hae Young Noh
|
Predicting Peak Stresses In Microstructured Materials Using
Convolutional Encoder-Decoder Learning
| null |
10.1177/10812865211055504
| null |
math.AP cond-mat.mtrl-sci cs.LG
|
This work presents a machine learning approach to predict peak-stress
clusters in heterogeneous polycrystalline materials. Prior work on using
machine learning in the context of mechanics has largely focused on predicting
the effective response and overall structure of stress fields. However, their
ability to predict peak stresses -- which are of critical importance to failure
-- is unexplored, because the peak-stress clusters occupy a small spatial
volume relative to the entire domain, and hence requires computationally
expensive training. This work develops a deep-learning based Convolutional
Encoder-Decoder method that focuses on predicting peak-stress clusters,
specifically on the size and other characteristics of the clusters in the
framework of heterogeneous linear elasticity. This method is based on
convolutional filters that model local spatial relations between
microstructures and stress fields using spatially weighted averaging
operations. The model is first trained against linear elastic calculations of
stress under applied macroscopic strain in synthetically-generated
microstructures, which serves as the ground truth. The trained model is then
applied to predict the stress field given a (synthetically-generated)
microstructure and then to detect peak-stress clusters within the predicted
stress field. The accuracy of the peak-stress predictions is analyzed using the
cosine similarity metric and by comparing the geometric characteristics of the
peak-stress clusters against the ground-truth calculations. It is observed that
the model is able to learn and predict the geometric details of the peak-stress
clusters and, in particular, performed better for higher (normalized) values of
the peak stress as compared to lower values of the peak stress. These
comparisons showed that the proposed method is well-suited to predict the
characteristics of peak-stress clusters.
|
[{'version': 'v1', 'created': 'Mon, 3 Jan 2022 15:51:52 GMT'}]
|
2024-05-10
|
Martin Kuban and Santiago Rigamonti and Markus Scheidgen and Claudia
Draxl
|
Density-of-states similarity descriptor for unsupervised learning from
materials data
| null | null | null |
cond-mat.mtrl-sci
|
We develop a materials descriptor based on the electronic density of states
and investigate the similarity of materials based on it. As an application
example, we study the Computational 2D Materials Database that hosts thousands
of two-dimensional materials with their properties calculated by
density-functional theory. Combining our descriptor with a clustering
algorithm, we identify groups of materials with similar electronic structure.
We characterize these clusters in terms of their crystal structure, their
atomic composition, and the respective electronic configurations to rationalize
the found (dis)similarities.
|
[{'version': 'v1', 'created': 'Thu, 6 Jan 2022 18:52:52 GMT'}]
|
2022-01-07
|
Chenru Duan, Daniel B. K. Chu, Aditya Nandy, and Heather J. Kulik
|
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical
Discovery with Chemical Accuracy
| null | null | null |
physics.chem-ph cond-mat.mtrl-sci cs.LG
|
Appropriately identifying and treating molecules and materials with
significant multi-reference (MR) character is crucial for achieving high data
fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS
is carried out with approximate density functional theory (DFT) using a single
functional. Despite development of numerous MR diagnostics, the extent to which
a single value of such a diagnostic indicates MR effect on chemical property
prediction is not well established. We evaluate MR diagnostics of over 10,000
transition metal complexes (TMCs) and compare to those in organic molecules. We
reveal that only some MR diagnostics are transferable across these materials
spaces. By studying the influence of MR character on chemical properties (i.e.,
MR effect) that involves multiple potential energy surfaces (i.e., adiabatic
spin splitting, $\Delta E_\mathrm{H-L}$, and ionization potential, IP), we
observe that cancellation in MR effect outweighs accumulation. Differences in
MR character are more important than the total degree of MR character in
predicting MR effect in property prediction. Motivated by this observation, we
build transfer learning models to directly predict CCSD(T)-level adiabatic
$\Delta E_\mathrm{H-L}$ and IP from lower levels of theory. By combining these
models with uncertainty quantification and multi-level modeling, we introduce a
multi-pronged strategy that accelerates data acquisition by at least a factor
of three while achieving chemical accuracy (i.e., 1 kcal/mol) for robust VHTS.
|
[{'version': 'v1', 'created': 'Tue, 11 Jan 2022 23:45:52 GMT'}]
|
2022-01-13
|
Arda Genc, Libor Kovarik, Hamish L. Fraser
|
A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in
Electron Tomography of Catalytic Materials
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Heterogeneous catalysts possess complex surface and bulk structures,
relatively poor intrinsic contrast, and often a sparse distribution of the
catalytic nanoparticles (NPs), posing a significant challenge for image
segmentation, including the current state-of-the-art deep learning methods. To
tackle this problem, we apply a deep learning-based approach for the
multi-class semantic segmentation of a $\gamma$-Alumina/Pt catalytic material
in a class imbalance situation. Specifically, we used the weighted focal loss
as a loss function and attached it to the U-Net's fully convolutional network
architecture. We assessed the accuracy of our results using Dice similarity
coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on
the overlap between the ground-truth and predicted segmentations. Our adopted
U-Net model with the weighted focal loss function achieved an average DSC score
of 0.96 $\pm$ 0.003 in the $\gamma$-Alumina support material and 0.84 $\pm$
0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap
error of less than 2 nm at the 90th percentile of HD for $\gamma$-Alumina and
Pt NPs segmentations. The complex surface morphology of the $\gamma$-Alumina
and its relation to the Pt NPs were visualized in 3D by the deep
learning-assisted automatic segmentation of a large data set of high-angle
annular dark-field (HAADF) scanning transmission electron microscopy (STEM)
tomography reconstructions.
|
[{'version': 'v1', 'created': 'Tue, 18 Jan 2022 22:45:19 GMT'}]
|
2022-01-20
|
Haoyue Guo, Qian Wang, Alexander Urban, Nongnuch Artrith
|
AI-Aided Mapping of the Structure-Composition-Conductivity Relationships
of Glass-Ceramic Lithium Thiophosphate Electrolytes
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Lithium thiophosphates (LPS) with the composition
(Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective
electrolyte materials for solid-state batteries (SSBs), owing to their
superionic conductivity at room temperature ($>10^{-3}$ S cm$^{-1}$), soft
mechanical properties, and low grain boundary resistance. Several glass-ceramic
(gc) LPS with different compositions and good Li conductivity have been
previously reported, but the relationship between composition, atomic
structure, stability, and Li conductivity remains unclear due to the challenges
in characterizing non-crystalline phases in experiments or simulations. Here,
we mapped the LPS phase diagram by combining first principles and artificial
intelligence (AI) methods, integrating density functional theory, artificial
neural network potentials, genetic-algorithm sampling, and ab initio molecular
dynamics simulations. By means of an unsupervised structure-similarity
analysis, the glassy/ceramic phases were correlated with the local structural
motifs in the known LPS crystal structures, showing that the energetically most
favorable Li environment varies with the composition. Based on the discovered
trends in the LPS phase diagram, we propose a candidate solid-state electrolyte
composition, (Li$_{2}$S)$_{x}$(P$_{2}$S$_{5}$)$_{1-x}$ ($x\sim{}0.725$), that
exhibits high ionic conductivity ($>10^{-2}$ S cm$^{-1}$) in our simulations,
thereby demonstrating a general design strategy for amorphous or glassy/ceramic
solid electrolytes with enhanced conductivity and stability.
|
[{'version': 'v1', 'created': 'Wed, 26 Jan 2022 22:01:09 GMT'}]
|
2022-01-28
|
Joydeep Munshi, Alexander Rakowski, Benjamin H Savitzky, Steven E
Zeltmann, Jim Ciston, Matthew Henderson, Shreyas Cholia, Andrew M Minor,
Maria KY Chan, and Colin Ophus
|
Disentangling multiple scattering with deep learning: application to
strain mapping from electron diffraction patterns
| null | null | null |
cond-mat.mtrl-sci cs.CV physics.app-ph
|
Implementation of a fast, robust, and fully-automated pipeline for crystal
structure determination and underlying strain mapping for crystalline materials
is important for many technological applications. Scanning electron
nanodiffraction offers a procedure for identifying and collecting strain maps
with good accuracy and high spatial resolutions. However, the application of
this technique is limited, particularly in thick samples where the electron
beam can undergo multiple scattering, which introduces signal nonlinearities.
Deep learning methods have the potential to invert these complex signals, but
previous implementations are often trained only on specific crystal systems or
a small subset of the crystal structure and microscope parameter phase space.
In this study, we implement a Fourier space, complex-valued deep neural network
called FCU-Net, to invert highly nonlinear electron diffraction patterns into
the corresponding quantitative structure factor images. We trained the FCU-Net
using over 200,000 unique simulated dynamical diffraction patterns which
include many different combinations of crystal structures, orientations,
thicknesses, microscope parameters, and common experimental artifacts. We
evaluated the trained FCU-Net model against simulated and experimental 4D-STEM
diffraction datasets, where it substantially out-performs conventional analysis
methods. Our simulated diffraction pattern library, implementation of FCU-Net,
and trained model weights are freely available in open source repositories, and
can be adapted to many different diffraction measurement problems.
|
[{'version': 'v1', 'created': 'Tue, 1 Feb 2022 03:53:39 GMT'}]
|
2022-02-02
|
Chi Chen and Shyue Ping Ong
|
A Universal Graph Deep Learning Interatomic Potential for the Periodic
Table
| null |
10.1038/s43588-022-00349-3
| null |
cond-mat.mtrl-sci physics.chem-ph
|
Interatomic potentials (IAPs), which describe the potential energy surface of
atoms, are a fundamental input for atomistic simulations. However, existing
IAPs are either fitted to narrow chemistries or too inaccurate for general
applications. Here, we report a universal IAP for materials based on graph
neural networks with three-body interactions (M3GNet). The M3GNet IAP was
trained on the massive database of structural relaxations performed by the
Materials Project over the past 10 years and has broad applications in
structural relaxation, dynamic simulations and property prediction of materials
across diverse chemical spaces. About 1.8 million materials were identified
from a screening of 31 million hypothetical crystal structures to be
potentially stable against existing Materials Project crystals based on M3GNet
energies. Of the top 2000 materials with the lowest energies above hull, 1578
were verified to be stable using DFT calculations. These results demonstrate a
machine learning-accelerated pathway to the discovery of synthesizable
materials with exceptional properties.
|
[{'version': 'v1', 'created': 'Sat, 5 Feb 2022 01:26:38 GMT'}, {'version': 'v2', 'created': 'Sun, 14 Aug 2022 22:46:23 GMT'}]
|
2022-12-06
|
Rama K. Vasudevan, Erick Orozco, Sergei V. Kalinin
|
Discovering mechanisms for materials microstructure optimization via
reinforcement learning of a generative model
| null | null | null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
The design of materials structure for optimizing functional properties and
potentially, the discovery of novel behaviors is a keystone problem in
materials science. In many cases microstructural models underpinning materials
functionality are available and well understood. However, optimization of
average properties via microstructural engineering often leads to
combinatorically intractable problems. Here, we explore the use of the
reinforcement learning (RL) for microstructure optimization targeting the
discovery of the physical mechanisms behind enhanced functionalities. We
illustrate that RL can provide insights into the mechanisms driving properties
of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we
find that non-trivial phenomena emerge if the rewards are assigned to favor
physically impossible tasks, which we illustrate through rewarding RL agents to
rotate polarization vectors to energetically unfavorable positions. We further
find that strategies to induce polarization curl can be non-intuitive, based on
analysis of learned agent policies. This study suggests that RL is a promising
machine learning method for material design optimization tasks, and for better
understanding the dynamics of microstructural simulations.
|
[{'version': 'v1', 'created': 'Tue, 22 Feb 2022 15:44:51 GMT'}]
|
2022-02-23
|
Thomas Friedrich, Chu-Ping Yu, Jo Verbeeck, Sandra Van Aert
|
Phase Object Reconstruction for 4D-STEM using Deep Learning
| null |
10.1093/micmic/ozac002
| null |
cond-mat.mtrl-sci eess.IV
|
In this study we explore the possibility to use deep learning for the
reconstruction of phase images from 4D scanning transmission electron
microscopy (4D-STEM) data. The process can be divided into two main steps.
First, the complex electron wave function is recovered for a convergent beam
electron diffraction pattern (CBED) using a convolutional neural network (CNN).
Subsequently a corresponding patch of the phase object is recovered using the
phase object approximation (POA). Repeating this for each scan position in a
4D-STEM dataset and combining the patches by complex summation yields the full
phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only,
which eliminates common, large memory requirements and enables live processing
during an experiment. The machine learning pipeline, data generation and the
reconstruction algorithm are presented. We demonstrate that the CNN can
retrieve phase information beyond the aperture angle, enabling super-resolution
imaging. The image contrast formation is evaluated showing a dependence on
thickness and atomic column type. Columns containing light and heavy elements
can be imaged simultaneously and are distinguishable. The combination of
super-resolution, good noise robustness and intuitive image contrast
characteristics makes the approach unique among live imaging methods in
4D-STEM.
|
[{'version': 'v1', 'created': 'Fri, 25 Feb 2022 10:59:56 GMT'}, {'version': 'v2', 'created': 'Tue, 30 Aug 2022 21:11:30 GMT'}]
|
2023-02-15
|
Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang,
and Jiayu Dai
|
Towards Large-Scale and Spatio-temporally Resolved Diagnosis of
Electronic Density of States by Deep Learning
|
Phys. Rev. B 105: 174109 (2022)
|
10.1103/PhysRevB.105.174109
| null |
physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.atm-clus
|
Modern laboratory techniques like ultrafast laser excitation and shock
compression can bring matter into highly nonequilibrium states with complex
structural transformation, metallization and dissociation dynamics. To
understand and model the dramatic change of both electronic structures and ion
dynamics during such dynamic processes, the traditional method faces
difficulties. Here, we demonstrate the ability of deep neural network (DNN) to
capture the atomic local-environment dependence of electronic density of states
(DOS) for both multicomponent system under exoplanet thermodynamic condition
and nonequilibrium system during super-heated melting process. Large scale and
time-resolved diagnosis of DOS can be efficiently achieved within the accuracy
of ab initio method. Moreover, the atomic contribution to DOS given by DNN
model accurately reveals the information of local neighborhood for selected
atom, thus can serve as robust order parameters to identify different phases
and intermediate local structures, strongly highlights the efficacy of this DNN
model in studying dynamic processes.
|
[{'version': 'v1', 'created': 'Wed, 9 Mar 2022 02:21:41 GMT'}, {'version': 'v2', 'created': 'Thu, 12 May 2022 00:19:52 GMT'}]
|
2022-05-24
|
Saba Kharabadze, Aidan Thorn, Ekaterina A. Koulakova, and Aleksey N.
Kolmogorov
|
Prediction of stable Li-Sn compounds: boosting ab initio searches with
neural network potentials
|
npj Computational Materials volume 8, Article number: 136 (2022)
|
10.1038/s41524-022-00825-4
| null |
cond-mat.mtrl-sci physics.comp-ph
|
The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our
present re-examination of the binary system with a combination of machine
learning and ab initio methods has allowed us to screen a vast configuration
space and uncover a number of overlooked thermodynamically stable alloys. At
ambient pressure, our evolutionary searches identified a new stable Li$_3$Sn
phase with a large BCC-based hR48 structure and a possible high-T LiSn$_4$
ground state. By building a simple model for the observed and predicted Li-Sn
BCC alloys we constructed an even larger viable hR75 structure at an exotic
19:6 stoichiometry. At 20 GPa, new 11:2, 5:1, and 9:2 phases found with our
global searches destabilize previously proposed phases with high Li content.
The findings showcase the appreciable promise machine learning interatomic
potentials hold for accelerating ab initio prediction of complex materials.
|
[{'version': 'v1', 'created': 'Fri, 11 Mar 2022 23:34:23 GMT'}]
|
2022-08-09
|
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