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2025-05-15 00:00:00
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|---|---|---|---|---|---|---|---|---|
Z. Suszynski, M. Kosikowski, R. Duer
|
The application of Artificial Neural Network for the assessment of
thermal properties of multi-layer semiconductor structure
|
Dans Proceedings of 12th International Workshop on Thermal
investigations of ICs - THERMINIC 2006, Nice : France (2006)
| null | null |
cond-mat.mtrl-sci
|
In this paper, the solution of the problem of identification of thermal
properties of investigated multi-layer structure is presented. In order of
that, artificial neural network was used to find the set of thermal properties
for which the complex contrast characteric derived fits the best to the one
evaluated basing upon experimenatal data.
|
[{'version': 'v1', 'created': 'Wed, 12 Sep 2007 13:30:55 GMT'}]
|
2007-09-13
|
Gabriele C. Sosso (1), Giacomo Miceli (1), Sebastiano Caravati (2),
J\"org Behler (3), and Marco Bernasconi (1) ((1) Dipartimento di Scienza dei
Materiali, Universit\`a di Milano-Bicocca, Milano, Italy, (2) Computational
Science, Department of Chemistry and Applied Biosciences ETH Zurich, USI
Campus, Lugano, Switzerland, (3) Lehrstuhl f\"ur Theoretische Chemie,
Ruhr-Universit\"at Bochum, Bochum, Germany)
|
A neural network interatomic potential for the phase change material
GeTe
|
Phys. Rev. B 85, 174103 (2012)
|
10.1103/PhysRevB.85.174103
| null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
GeTe is a prototypical phase change material of high interest for
applications in optical and electronic non-volatile memories. We present an
interatomic potential for the bulk phases of GeTe, which is created using a
neural network (NN) representation of the potential-energy surface obtained
from reference calculations based on density functional theory. It is
demonstrated that the NN potential provides a close to ab initio quality
description of a number of properties of liquid, crystalline and amorphous
GeTe. The availability of a reliable classical potential allows addressing a
number of issues of interest for the technological applications of phase change
materials, which are presently beyond the capability of first principles
molecular dynamics simulations.
|
[{'version': 'v1', 'created': 'Tue, 10 Jan 2012 11:35:59 GMT'}]
|
2012-08-02
|
Francesco Bonanno, Giacomo Capizzi, Grazia Lo Sciuto, Christian
Napoli, Giuseppe Pappalardo, Emiliano Tramontana
|
A Cascade Neural Network Architecture investigating Surface Plasmon
Polaritons propagation for thin metals in OpenMP
|
International conference on Artificial Intelligence and Soft
Computing (ICAISC 2014), Vol I, 22-33 (2014)
| null | null |
cs.NE cond-mat.mes-hall cond-mat.mtrl-sci cs.DC cs.LG
|
Surface plasmon polaritons (SPPs) confined along metal-dielectric interface
have attracted a relevant interest in the area of ultracompact photonic
circuits, photovoltaic devices and other applications due to their strong field
confinement and enhancement. This paper investigates a novel cascade neural
network (NN) architecture to find the dependance of metal thickness on the SPP
propagation. Additionally, a novel training procedure for the proposed cascade
NN has been developed using an OpenMP-based framework, thus greatly reducing
training time. The performed experiments confirm the effectiveness of the
proposed NN architecture for the problem at hand.
|
[{'version': 'v1', 'created': 'Thu, 12 Jun 2014 08:40:04 GMT'}]
|
2014-06-13
|
S. Alireza Ghasemi, Albert Hofstetter, Santanu Saha, Stefan Goedecker
|
Interatomic potentials for ionic systems with density functional
accuracy based on charge densities obtained by a neural network
|
Phys. Rev. B 92, 045131 (2015)
|
10.1103/PhysRevB.92.045131
| null |
cond-mat.mtrl-sci physics.chem-ph
|
Based on an analysis of the short range chemical environment of each atom in
a system, standard machine learning based approaches to the construction of
interatomic potentials aim at determining directly the central quantity which
is the total energy. This prevents for instance an accurate description of the
energetics of systems where long range charge transfer is important as well as
of ionized systems. We propose therefore not to target directly with machine
learning methods the total energy but an intermediate physical quantity namely
the charge density, which then in turn allows to determine the total energy. By
allowing the electronic charge to distribute itself in an optimal way over the
system, we can describe not only neutral but also ionized systems with
unprecedented accuracy. We demonstrate the power of our approach for both
neutral and ionized NaCl clusters where charge redistribution plays a decisive
role for the energetics. We are able to obtain chemical accuracy, i.e. errors
of less than a milli Hartree per atom compared to the reference density
functional results. The introduction of physically motivated quantities which
are determined by the short range atomic environment via a neural network leads
also to an increased stability of the machine learning process and
transferability of the potential.
|
[{'version': 'v1', 'created': 'Thu, 29 Jan 2015 05:19:01 GMT'}]
|
2015-08-05
|
Samad Hajinazar, Junping Shao, Aleksey N. Kolmogorov
|
Stratified construction of neural network based interatomic models for
multicomponent materials
|
Phys. Rev. B 95, 014114 (2017)
|
10.1103/PhysRevB.95.014114
| null |
cond-mat.mtrl-sci
|
Recent application of neural networks (NNs) to modeling interatomic
interactions has shown the learning machines' encouragingly accurate
performance for select elemental and multicomponent systems. In this study, we
explore the possibility of building a library of NN-based models by introducing
a hierarchical NN training. In such a stratified procedure NNs for
multicomponent systems are obtained by sequential training from the bottom up:
first unaries, then binaries, and so on. Advantages of constructing NN sets
with shared parameters include acceleration of the training process and intact
description of the constituent systems. We use an automated generation of
diverse structure sets for NN training on density functional theory-level
reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and
Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are
found to have essentially identical accuracy for defect energies, phonon
dispersions, formation energies, etc. The models' robustness is further
illustrated via unconstrained evolutionary structure searches in which the NN
is used for the local optimization of crystal unit cells.
|
[{'version': 'v1', 'created': 'Tue, 27 Sep 2016 14:06:16 GMT'}, {'version': 'v2', 'created': 'Wed, 1 Feb 2017 01:15:44 GMT'}]
|
2017-02-08
|
P. Anees, M. C. Valsakumar and B. K. Panigrahi
|
Delineating the role of ripples on thermal expansion of honeycomb
materials:graphene, 2D-h-BN and monolayer(ML)-MoS2
|
Phys. Chem. Chem. Phys., 19, 10518, (2017)
|
10.1039/C6CP08635G
| null |
cond-mat.mtrl-sci
|
We delineated the role of thermally excited ripples on thermal expansion
properties of 2D honeycomb materials (free-standing graphene, 2D h-BN, and
ML-MoS2), by explicitly carrying out three-dimensional (3D) and two-dimensional
(2D) molecular dynamics simulations. In 3D simulations, the in-plane lattice
parameter (a-lattice) of graphene and 2D h-BN shows thermal contraction over a
wide range of temperatures and exhibits a strong system size dependence. The 2D
simulations of the very same system show a reverse trend, where the a-lattice
is expanding in the whole computed temperature range. Contrary to graphene and
2D h-BN, the a-lattice of ML-MoS2 shows thermal expansion in both 2D and 3D
simulations and their system size dependence is marginal. By analyzing the
phonon dispersion at 300 K, we found that the discrepancy between 2D and 3D
simulations of graphene and 2D h-BN is due to the absence of out-of-plane
bending mode (ZA) in 2D simulations, which is responsible for thermal
contraction of a-lattice at low temperature. Meanwhile, all the phonon modes
are present in 2D phonon dispersion of ML-MoS2, which indicates that the origin
of ZA mode is not purely due to out-of-plane movement of atoms and also its
effect on thermal expansion is not significant as found in graphene and 2D h-BN
|
[{'version': 'v1', 'created': 'Tue, 25 Oct 2016 08:43:49 GMT'}]
|
2017-07-25
|
Daniel Valencia, Evan Wilson, Zhengping Jiang, Gustavo A.
Valencia-Zapata, Gerhard Klimeck and Michael Povolotskyi
|
Grain Boundary Resistance in Copper Interconnects from an Atomistic
Model to a Neural Network
|
Phys. Rev. Applied 9, 044005 (2018)
|
10.1103/PhysRevApplied.9.044005
| null |
cond-mat.mtrl-sci
|
Orientation effects on the resistivity of copper grain boundaries are studied
systematically with two different atomistic tight binding methods. A
methodology is developed to model the resistivity of grain boundaries using the
Embedded Atom Model, tight binding methods and non-equilibrum Green's functions
(NEGF). The methodology is validated against first principles calculations for
small, ultra-thin body grain boundaries (<5nm) with 6.4% deviation in the
resistivity. A statistical ensemble of 600 large, random structures with grains
is studied. For structures with three grains, it is found that the distribution
of resistivities is close to normal. Finally, a compact model for grain
boundary resistivity is constructed based on a neural network.
|
[{'version': 'v1', 'created': 'Tue, 17 Jan 2017 23:24:10 GMT'}, {'version': 'v2', 'created': 'Sat, 4 Feb 2017 04:16:11 GMT'}, {'version': 'v3', 'created': 'Sun, 8 Oct 2017 20:03:43 GMT'}]
|
2018-04-11
|
Kyle Mills, Michael Spanner, and Isaac Tamblyn
|
Deep learning and the Schr\"odinger equation
|
Phys. Rev. A 96, 042113 (2017)
|
10.1103/PhysRevA.96.042113
| null |
cond-mat.mtrl-sci cs.LG physics.chem-ph
|
We have trained a deep (convolutional) neural network to predict the
ground-state energy of an electron in four classes of confining two-dimensional
electrostatic potentials. On randomly generated potentials, for which there is
no analytic form for either the potential or the ground-state energy, the
neural network model was able to predict the ground-state energy to within
chemical accuracy, with a median absolute error of 1.49 mHa. We also
investigate the performance of the model in predicting other quantities such as
the kinetic energy and the first excited-state energy of random potentials.
|
[{'version': 'v1', 'created': 'Sun, 5 Feb 2017 02:58:58 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2017 20:39:27 GMT'}, {'version': 'v3', 'created': 'Fri, 3 Nov 2017 13:10:51 GMT'}]
|
2017-11-06
|
Seyed Majid Azimi, Dominik Britz, Michael Engstler, Mario Fritz, Frank
M\"ucklich
|
Advanced Steel Microstructural Classification by Deep Learning Methods
| null |
10.1038/s41598-018-20037-5
| null |
cs.CV cond-mat.mtrl-sci
|
The inner structure of a material is called microstructure. It stores the
genesis of a material and determines all its physical and chemical properties.
While microstructural characterization is widely spread and well known, the
microstructural classification is mostly done manually by human experts, which
gives rise to uncertainties due to subjectivity. Since the microstructure could
be a combination of different phases or constituents with complex substructures
its automatic classification is very challenging and only a few prior studies
exist. Prior works focused on designed and engineered features by experts and
classified microstructures separately from the feature extraction step.
Recently, Deep Learning methods have shown strong performance in vision
applications by learning the features from data together with the
classification step. In this work, we propose a Deep Learning method for
microstructural classification in the examples of certain microstructural
constituents of low carbon steel. This novel method employs pixel-wise
segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a
max-voting scheme. Our system achieves 93.94% classification accuracy,
drastically outperforming the state-of-the-art method of 48.89% accuracy.
Beyond the strong performance of our method, this line of research offers a
more robust and first of all objective way for the difficult task of steel
quality appreciation.
|
[{'version': 'v1', 'created': 'Tue, 20 Jun 2017 14:29:42 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Feb 2018 14:30:16 GMT'}]
|
2018-02-16
|
Weizong Xu and James M. LeBeau
|
A Deep Convolutional Neural Network to Analyze Position Averaged
Convergent Beam Electron Diffraction Patterns
| null |
10.1016/j.ultramic.2018.03.004
| null |
physics.data-an cond-mat.mtrl-sci
|
We establish a series of deep convolutional neural networks to automatically
analyze position averaged convergent beam electron diffraction patterns. The
networks first calibrate the zero-order disk size, center position, and
rotation without the need for pretreating the data. With the aligned data,
additional networks then measure the sample thickness and tilt. The performance
of the network is explored as a function of a variety of variables including
thickness, tilt, and dose. A methodology to explore the response of the neural
network to various pattern features is also presented. Processing patterns at a
rate of $\sim$0.1 s/pattern, the network is shown to be orders of magnitude
faster than a brute force method while maintaining accuracy. The approach is
thus suitable for automatically processing big, 4D STEM data. We also discuss
the generality of the method to other materials/orientations as well as a
hybrid approach that combines the features of the neural network with least
squares fitting for even more robust analysis. The source code is available at
https://github.com/subangstrom/DeepDiffraction.
|
[{'version': 'v1', 'created': 'Thu, 3 Aug 2017 14:38:30 GMT'}]
|
2018-06-05
|
A. Ziletti, D. Kumar, M. Scheffler, L. M. Ghiringhelli
|
Insightful classification of crystal structures using deep learning
|
Nature Communications 9, 2775 (2018)
|
10.1038/s41467-018-05169-6
| null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Computational methods that automatically extract knowledge from data are
critical for enabling data-driven materials science. A reliable identification
of lattice symmetry is a crucial first step for materials characterization and
analytics. Current methods require a user-specified threshold, and are unable
to detect average symmetries for defective structures. Here, we propose a
machine-learning-based approach to automatically classify structures by crystal
symmetry. First, we represent crystals by calculating a diffraction image, then
construct a deep-learning neural-network model for classification. Our approach
is able to correctly classify a dataset comprising more than 100 000 simulated
crystal structures, including heavily defective ones. The internal operations
of the neural network are unraveled through attentive response maps,
demonstrating that it uses the same landmarks a materials scientist would use,
although never explicitly instructed to do so. Our study paves the way for
crystal-structure recognition of - possibly noisy and incomplete -
three-dimensional structural data in big-data materials science.
|
[{'version': 'v1', 'created': 'Thu, 7 Sep 2017 15:09:27 GMT'}, {'version': 'v2', 'created': 'Wed, 30 May 2018 06:11:23 GMT'}]
|
2018-07-19
|
M. Carrillo, J. A. Gonz\'alez, S. Hern\'andez-Ortiz, C. E. L\'opez, A.
Raya
|
Bloch oscillations in graphene from an artificial neural network study
| null | null | null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
We develop an artificial neural network (ANN) approach to classify simulated
signals corrsponding to the semi-classical description of Bloch oscillations in
pristine graphene. After the ANN is properly trained, we consider the inverse
problem of Bloch oscillations (BO),namely, a new signal is classified according
to the external electric field strength oriented along either the zig-zag or
arm-chair edges of the graphene membrane, with a correct classification that
ranges from 82.6% to 99.3% depending on the accuracy of the predicted electric
field. This approach can be improved depending on the time spent in training
the network and the computational power available. Findings in this work can be
straightforwardly extended to a variety of Dirac-Weyl materials.
|
[{'version': 'v1', 'created': 'Wed, 4 Oct 2017 17:46:07 GMT'}]
|
2017-10-05
|
Adrien Bouhon and Annica M. Black-Schaffer
|
Bulk topology of line-nodal structures protected by space group
symmetries in class AI
| null | null | null |
cond-mat.mtrl-sci
|
We give an exhaustive characterization of the topology of band structures in
class AI, using nonsymmorphic space group 33 ($Pna2_1$) as a representative
example where a great variety of symmetry protected line-nodal structures can
be formed. We start with the topological classification of all line-nodal
structures given through the combinatorics of valence irreducible
representations (IRREPs) at a few high-symmetry points (HSPs) at a fixed
filling. We decompose the total topology of nodal valence band bundles through
the local topology of elementary (i.e. inseparable) nodal structures and the
global topology that constrains distinct elementary nodal elements over the
Brillouin zone (BZ). Generalizing from the cases of simple point nodes and
simple nodal lines (NLs), we argue that the local topology of every elementary
nodal structure is characterized by a set of poloidal-toroidal charges, one
monopole, and one thread charge (when threading the BZ torus), while the global
topology only allows pairs of nontrivial monopole and thread charges. We show
that all these charges are given in terms of symmetry protected topological
invariants, defined through quantized Wilson loop phases over symmetry
constrained momentum loops, which we derive entirely algebraically from the
valence IRREPs at the HSPs. In particular, we find highly connected line-nodal
structures, line-nodal monopole pairs, and line-nodal thread pairs, that are
all protected by the unitary crystalline symmetries only. Furthermore, we show
symmetry preserving topological Lifshitz transitions through which independent
NLs can be connected, disconnected, or linked. Our work constitutes a heuristic
approach to the systematic topological classification and characterization of
all momentum space line-nodal structures protected by space group symmetries in
class AI.
|
[{'version': 'v1', 'created': 'Fri, 13 Oct 2017 11:10:07 GMT'}]
|
2017-10-16
|
Maxwell L. Hutchinson, Erin Antono, Brenna M. Gibbons, Sean Paradiso,
Julia Ling, Bryce Meredig
|
Overcoming data scarcity with transfer learning
| null | null | null |
cs.LG cond-mat.mtrl-sci stat.ML
|
Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measured or computed differently depending on the data source. These hidden
contextual differences introduce irreducible errors into analyses,
fundamentally limiting their accuracy. Transfer learning, where information
from one dataset is used to inform a model on another, can be an effective tool
for bridging sparse data while preserving the contextual differences in the
underlying measurements. Here, we describe and compare three techniques for
transfer learning: multi-task, difference, and explicit latent variable
architectures. We show that difference architectures are most accurate in the
multi-fidelity case of mixed DFT and experimental band gaps, while multi-task
most improves classification performance of color with band gaps. For
activation energies of steps in NO reduction, the explicit latent variable
method is not only the most accurate, but also enjoys cancellation of errors in
functions that depend on multiple tasks. These results motivate the publication
of high quality materials datasets that encode transferable information,
independent of industrial or academic interest in the particular labels, and
encourage further development and application of transfer learning methods to
materials informatics problems.
|
[{'version': 'v1', 'created': 'Thu, 2 Nov 2017 12:54:51 GMT'}]
|
2017-11-15
|
Eric Gossett, Cormac Toher, Corey Oses, Olexandr Isayev, Fleur
Legrain, Frisco Rose, Eva Zurek, Jes\'us Carrete, Natalio Mingo, Alexander
Tropsha, Stefano Curtarolo
|
AFLOW-ML: A RESTful API for machine-learning predictions of materials
properties
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Machine learning approaches, enabled by the emergence of comprehensive
databases of materials properties, are becoming a fruitful direction for
materials analysis. As a result, a plethora of models have been constructed and
trained on existing data to predict properties of new systems. These powerful
methods allow researchers to target studies only at interesting materials
$\unicode{x2014}$ neglecting the non-synthesizable systems and those without
the desired properties $\unicode{x2014}$ thus reducing the amount of resources
spent on expensive computations and/or time-consuming experimental synthesis.
However, using these predictive models is not always straightforward. Often,
they require a panoply of technical expertise, creating barriers for general
users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine
$\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use
of the machine learning methods developed within the AFLOW consortium. The
framework provides an open RESTful API to directly access the continuously
updated algorithms, which can be transparently integrated into any workflow to
retrieve predictions of electronic, thermal and mechanical properties. These
types of interconnected cloud-based applications are envisioned to be capable
of further accelerating the adoption of machine learning methods into materials
development.
|
[{'version': 'v1', 'created': 'Wed, 29 Nov 2017 09:35:46 GMT'}]
|
2017-11-30
|
Ruijin Cang, Hechao Li, Hope Yao, Yang Jiao, Yi Ren
|
Improving Direct Physical Properties Prediction of Heterogeneous
Materials from Imaging Data via Convolutional Neural Network and a
Morphology-Aware Generative Model
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
Direct prediction of material properties from microstructures through
statistical models has shown to be a potential approach to accelerating
computational material design with large design spaces. However, statistical
modeling of highly nonlinear mappings defined on high-dimensional
microstructure spaces is known to be data-demanding. Thus, the added value of
such predictive models diminishes in common cases where material samples (in
forms of 2D or 3D microstructures) become costly to acquire either
experimentally or computationally. To this end, we propose a generative machine
learning model that creates an arbitrary amount of artificial material samples
with negligible computation cost, when trained on only a limited amount of
authentic samples. The key contribution of this work is the introduction of a
morphology constraint to the training of the generative model, that enforces
the resultant artificial material samples to have the same morphology
distribution as the authentic ones. We show empirically that the proposed model
creates artificial samples that better match with the authentic ones in
material property distributions than those generated from a state-of-the-art
Markov Random Field model, and thus is more effective at improving the
prediction performance of a predictive structure-property model.
|
[{'version': 'v1', 'created': 'Thu, 7 Dec 2017 06:49:29 GMT'}]
|
2017-12-12
|
Kristof T. Sch\"utt, Huziel E. Sauceda, Pieter-Jan Kindermans,
Alexandre Tkatchenko, Klaus-Robert M\"uller
|
SchNet - a deep learning architecture for molecules and materials
| null |
10.1063/1.5019779
| null |
physics.chem-ph cond-mat.mtrl-sci
|
Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interactions, enabling to model nonlinear potential-energy
surfaces or enhancing the exploration of chemical compound space. Here we
present the deep learning architecture SchNet that is specifically designed to
model atomistic systems by making use of continuous-filter convolutional
layers. We demonstrate the capabilities of SchNet by accurately predicting a
range of properties across chemical space for \emph{molecules and materials}
where our model learns chemically plausible embeddings of atom types across the
periodic table. Finally, we employ SchNet to predict potential-energy surfaces
and energy-conserving force fields for molecular dynamics simulations of small
molecules and perform an exemplary study of the quantum-mechanical properties
of C$_{20}$-fullerene that would have been infeasible with regular ab initio
molecular dynamics.
|
[{'version': 'v1', 'created': 'Sun, 17 Dec 2017 13:55:03 GMT'}, {'version': 'v2', 'created': 'Wed, 7 Mar 2018 12:42:19 GMT'}, {'version': 'v3', 'created': 'Thu, 22 Mar 2018 11:12:43 GMT'}]
|
2018-04-18
|
Maxim Ziatdinov, Ondrej Dyck, Artem Maksov, Xufan Li, Xiahan Sang, Kai
Xiao, Raymond R. Unocic, Rama Vasudevan, Stephen Jesse, Sergei V. Kalinin
|
Deep Learning of Atomically Resolved Scanning Transmission Electron
Microscopy Images: Chemical Identification and Tracking Local Transformations
|
ACS Nano, 2017, 11 (12), pp 12742-12752
|
10.1021/acsnano.7b07504
| null |
cond-mat.mtrl-sci
|
Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size and quality of datasets produced by microscopic and
spectroscopic experimental techniques. These developments necessitate adequate
methods for extracting relevant physical and chemical information from the
large datasets, for which a priori information on the structures of various
atomic configurations and lattice defects is limited or absent. Here we
demonstrate an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species and type of
defects. We develop a 'weakly-supervised' approach that uses information on the
coordinates of all atomic species in the image, extracted via a deep neural
network, to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic and
defect transformation, including switching between different coordination of
silicon dopants in graphene as a function of time, formation of peculiar
silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of
molecular 'rotor'. This deep learning based approach resembles logic of a human
operator, but can be scaled leading to significant shift in the way of
extracting and analyzing information from raw experimental data.
|
[{'version': 'v1', 'created': 'Wed, 17 Jan 2018 20:45:52 GMT'}]
|
2018-01-19
|
Jacob Madsen, Pei Liu, Jens Kling, Jakob Birkedal Wagner, Thomas
Willum Hansen, Ole Winther, Jakob Schi{\o}tz
|
A deep learning approach to identify local structures in
atomic-resolution transmission electron microscopy images
|
Adv. Theory Simul. 1, 1800037 (2018)
|
10.1002/adts.201800037
| null |
cond-mat.mtrl-sci
|
Recording atomic-resolution transmission electron microscopy (TEM) images is
becoming increasingly routine. A new bottleneck is then analyzing this
information, which often involves time-consuming manual structural
identification. We have developed a deep learning-based algorithm for
recognition of the local structure in TEM images, which is stable to microscope
parameters and noise. The neural network is trained entirely from simulation
but is capable of making reliable predictions on experimental images. We apply
the method to single sheets of defected graphene, and to metallic nanoparticles
on an oxide support.
|
[{'version': 'v1', 'created': 'Thu, 8 Feb 2018 18:57:20 GMT'}, {'version': 'v2', 'created': 'Fri, 9 Feb 2018 10:43:02 GMT'}]
|
2018-09-13
|
Rama K. Vasudevan, Nouamane Laanait, Erik M. Ferragut, Kai Wang, David
B. Geohegan, Kai Xiao, Maxim A. Ziatdinov, Stephen Jesse, Ondrej E. Dyck,
Sergei V. Kalinin
|
Mapping mesoscopic phase evolution during e-beam induced transformations
via deep learning of atomically resolved images
| null | null | null |
cond-mat.mtrl-sci
|
Understanding transformations under electron beam irradiation requires
mapping the structural phases and their evolution in real time. To date, this
has mostly been a manual endeavor comprising of difficult frame-by-frame
analysis that is simultaneously tedious and prone to error. Here, we turn
towards the use of deep convolutional neural networks (DCNN) to automatically
determine the Bravais lattice symmetry present in atomically-resolved images. A
DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier
transform of the input image. Monte-Carlo dropout is used for determining the
prediction probability, and results are shown for both simulated and real
atomically-resolved images from scanning tunneling microscopy and scanning
transmission electron microscopy. A reduced representation of the final layer
output allows to visualize the separation of classes in the DCNN and agrees
with physical intuition. We then apply the trained network to electron
beam-induced transformations in WS2, which allows tracking and determination of
growth rate of voids. These results are novel in two ways: (1) It shows that
DCNNs can be trained to recognize diffraction patterns, which is markedly
different from the typical "real image" cases, and (2) it provides a method
with in-built uncertainty quantification, allowing the real-time analysis of
phases present in atomically resolved images.
|
[{'version': 'v1', 'created': 'Wed, 28 Feb 2018 16:27:35 GMT'}]
|
2018-03-01
|
B.D. Conduit, N.G. Jones, H.J. Stone, G.J. Conduit
|
Probabilistic design of a molybdenum-base alloy using a neural network
|
Scripta Materialia 146, 82 (2018)
| null | null |
cond-mat.mtrl-sci cs.LG physics.comp-ph
|
An artificial intelligence tool is exploited to discover and characterize a
new molybdenum-base alloy that is the most likely to simultaneously satisfy
targets of cost, phase stability, precipitate content, yield stress, and
hardness. Experimental testing demonstrates that the proposed alloy fulfils the
computational predictions, and furthermore the physical properties exceed those
of other commercially available Mo-base alloys for forging-die applications.
|
[{'version': 'v1', 'created': 'Fri, 2 Mar 2018 15:11:49 GMT'}]
|
2018-03-05
|
Youngjun Cho, Nadia Bianchi-Berthouze, Nicolai Marquardt and Simon J.
Julier
|
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild
through Deep Learning of Spatial Surface Temperature Patterns
| null |
10.1145/3173574.3173576
| null |
cs.CV cond-mat.mtrl-sci cs.HC cs.LG
|
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.
|
[{'version': 'v1', 'created': 'Tue, 6 Mar 2018 17:29:08 GMT'}]
|
2018-03-28
|
B.D. Conduit, N.G. Jones, H.J. Stone, and G.J. Conduit
|
Design of a nickel-base superalloy using a neural network
|
Materials & Design 131, 358 (2017)
| null | null |
cond-mat.mtrl-sci cs.LG physics.comp-ph
|
A new computational tool has been developed to model, discover, and optimize
new alloys that simultaneously satisfy up to eleven physical criteria. An
artificial neural network is trained from pre-existing materials data that
enables the prediction of individual material properties both as a function of
composition and heat treatment routine, which allows it to optimize the
material properties to search for the material with properties most likely to
exceed a target criteria. We design a new polycrystalline nickel-base
superalloy with the optimal combination of cost, density, gamma' phase content
and solvus, phase stability, fatigue life, yield stress, ultimate tensile
strength, stress rupture, oxidation resistance, and tensile elongation.
Experimental data demonstrates that the proposed alloy fulfills the
computational predictions, possessing multiple physical properties,
particularly oxidation resistance and yield stress, that exceed existing
commercially available alloys.
|
[{'version': 'v1', 'created': 'Thu, 8 Mar 2018 11:04:58 GMT'}]
|
2018-03-09
|
Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan,
Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim
Ziatdinov
|
Deep Learning Analysis of Defect and Phase Evolution During Electron
Beam Induced Transformations in WS2
|
npj Computational Materials 5, Article number: 12 (2019)
|
10.1038/s41524-019-0152-9
| null |
cond-mat.mtrl-sci
|
Understanding elementary mechanisms behind solid-state phase transformations
and reactions is the key to optimizing desired functional properties of many
technologically relevant materials. Recent advances in scanning transmission
electron microscopy (STEM) allow the real-time visualization of solid-state
transformations in materials, including those induced by an electron beam and
temperature, with atomic resolution. However, despite the ever-expanding
capabilities for high-resolution data acquisition, the inferred information
about kinetics and thermodynamics of the process and single defect dynamics and
interactions is minima, due to the inherent limitations of manual ex-situ
analysis of the collected volumes of data. To circumvent this problem, we
developed a deep learning framework for dynamic STEM imaging that is trained to
find the structures (defects) that break a crystal lattice periodicity and
apply it for mapping solid state reactions and transformations in layered WS2
doped with Mo. This framework allows extracting thousands of lattice defects
from raw STEM data (single images and movies) in a matter of seconds, which are
then classified into different categories using unsupervised clustering
methods. We further expanded our framework to extract parameters of diffusion
for the sulfur vacancies and analyzed transition probabilities associated with
switching between different configurations of defect complexes consisting of Mo
dopant and sulfur vacancy, providing insight into point defect dynamics and
reactions. This approach is universal and its application to beam induced
reactions allows mapping chemical transformation pathways in solids at the
atomic level.
|
[{'version': 'v1', 'created': 'Wed, 14 Mar 2018 16:16:21 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Mar 2018 05:29:36 GMT'}, {'version': 'v3', 'created': 'Thu, 16 Aug 2018 06:54:26 GMT'}]
|
2019-02-05
|
Xiaolin Li, Yichi Zhang, He Zhao, Craig Burkhart, L Catherine Brinson,
Wei Chen
|
A Transfer Learning Approach for Microstructure Reconstruction and
Structure-property Predictions
| null | null | null |
cond-mat.mtrl-sci cs.CE physics.comp-ph
|
Stochastic microstructure reconstruction has become an indispensable part of
computational materials science, but ongoing developments are specific to
particular material systems. In this paper, we address this generality problem
by presenting a transfer learning-based approach for microstructure
reconstruction and structure-property predictions that is applicable to a wide
range of material systems. The proposed approach incorporates an
encoder-decoder process and feature-matching optimization using a deep
convolutional network. For microstructure reconstruction, model pruning is
implemented in order to study the correlation between the microstructural
features and hierarchical layers within the deep convolutional network.
Knowledge obtained in model pruning is then leveraged in the development of a
structure-property predictive model to determine the network architecture and
initialization conditions. The generality of the approach is demonstrated
numerically for a wide range of material microstructures with geometrical
characteristics of varying complexity. Unlike previous approaches that only
apply to specific material systems or require a significant amount of prior
knowledge in model selection and hyper-parameter tuning, the present approach
provides an off-the-shelf solution to handle complex microstructures, and has
the potential of expediting the discovery of new materials.
|
[{'version': 'v1', 'created': 'Tue, 8 May 2018 00:01:48 GMT'}]
|
2018-05-09
|
Y. Liu, Q. M. Sun, Dr. W. H. Lu, Dr. H. L. Wang, Y. Sun, Z. T. Wang,
X. Lu, Prof. K. Y. Zeng
|
General Resolution Enhancement Method in Atomic Force Microscopy (AFM)
Using Deep Learning
| null | null | null |
physics.data-an cond-mat.mtrl-sci
|
This paper develops a resolution enhancement method for post-processing the
images from Atomic Force Microscopy (AFM). This method is based on deep
learning neural networks in the AFM topography measurements. In this study, a
very deep convolution neural network is developed to derive the high-resolution
topography image from the low-resolution topography image. The AFM measured
images from various materials are tested in this study. The derived
high-resolution AFM images are comparable with the experimental measured
high-resolution images measured at the same locations. The results suggest that
this method can be developed as a general post-processing method for AFM image
analysis.
|
[{'version': 'v1', 'created': 'Tue, 11 Sep 2018 07:09:14 GMT'}]
|
2018-09-12
|
Yuan Dong, Chuhan Wu, Chi Zhang, Yingda Liu, Jianlin Cheng and Jian
Lin
|
Deep Learning Bandgaps of Topologically Doped Graphene
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Manipulation of material properties via precise doping affords enormous
tunable phenomena to explore. Recent advance shows that in the atomic and nano
scales topological states of dopants play crucial roles in determining their
properties. However, such determination is largely unknown due to the
incredible size of topological states. Here, we present a case study of
developing deep learning algorithms to predict bandgaps of boron-nitrogen pair
doped graphene with arbitrary dopant topologies. A material descriptor system
that enables to correlate structures with the bandgaps was developed for
convolutional neuron networks (CNNs). Bandgaps calculated by the ab initio
calculations and the corresponding structures were fed as input datasets to
train VGG16 convolutional network, residual convolutional network, and
concatenate convolutional network. Then these trained CNNs were used to predict
bandgaps of doped graphene with various dopant topologies. All of them afford
great prediction accuracy, showing square of the coefficient of correlation
(R2) of > 90% and root-mean-square errors of ~ 0.1 eV for the predicted
bandgaps. They are much better than those predicted by a shallow machine
learning method - support vector machine. The transfer learning was further
performed by leveraging data generated from smaller systems to improve the
prediction for large systems. Success of this work provides a cornerstone for
future investigation of topologically doped graphene and other 2D materials.
Moreover, given ubiquitous existence of topologies in materials, this work will
stimulate widespread interests in applying deep learning algorithms to
topological design of materials crossing atomic, nano-, meso-, and macro-
scales.
|
[{'version': 'v1', 'created': 'Fri, 28 Sep 2018 05:02:49 GMT'}]
|
2018-10-01
|
Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek
Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar
|
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with
Multitask Learning for Material Property Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci stat.ML
|
Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of universal descriptor of materials to predict its various properties.
The limited availability of materials data can be addressed through transfer
learning, while the generic representation was recently addressed by Xie and
Grossman [1], where they developed a crystal graph convolutional neural network
(CGCNN) that provides a unified representation of crystals. In this work, we
develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning
based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN
by simultaneous prediction of various material properties such as Formation
Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774
materials). MT-CGCNN is able to reduce the test error when employed on
correlated properties by upto 8%. The model prediction has lower test error
compared to CGCNN, even when the training data is reduced by 10%. We also
demonstrate our model's better performance through prediction of end user
scenario related to metal/non-metal classification. These results encourage
further development of machine learning approaches which leverage multi-task
learning to address the aforementioned challenges in the discovery of new
materials. We make MT-CGCNN's source code available to encourage reproducible
research.
|
[{'version': 'v1', 'created': 'Wed, 14 Nov 2018 06:13:29 GMT'}]
|
2018-11-15
|
Kevin Ryczko, David Strubbe, Isaac Tamblyn
|
Deep Learning and Density Functional Theory
|
Phys. Rev. A 100, 022512 (2019)
|
10.1103/PhysRevA.100.022512
| null |
cond-mat.mtrl-sci physics.comp-ph
|
We show that deep neural networks can be integrated into, or fully replace,
the Kohn-Sham density functional theory scheme for multi-electron systems in
simple harmonic oscillator and random external potentials with no feature
engineering. We first show that self-consistent charge densities calculated
with different exchange-correlation functionals can be used as input to an
extensive deep neural network to make predictions for correlation, exchange,
external, kinetic and total energies simultaneously. Additionally, we show that
one can also make all of the same predictions with the external potential
rather than the self-consistent charge density, which allows one to circumvent
the Kohn-Sham scheme altogether. We then show that a self-consistent charge
density found from a non-local exchange-correlation functional can be used to
make energy predictions for a semi-local exchange-correlation functional.
Lastly, we use a deep convolutional inverse graphics network to predict the
charge density given an external potential for different exchange-correlation
functionals and assess the viability of the predicted charge densities. This
work shows that extensive deep neural networks are generalizable and
transferable given the variability of the potentials (maximum total energy
range $\approx100$ Ha), because they require no feature engineering, and
because they can scale to an arbitrary system size with an $\mathcal{O}(N)$
computational cost.
|
[{'version': 'v1', 'created': 'Wed, 21 Nov 2018 20:03:01 GMT'}, {'version': 'v2', 'created': 'Wed, 24 Feb 2021 15:13:46 GMT'}]
|
2021-02-25
|
Rahul Singh, Aayush Sharma, Onur Rauf Bingol, Aditya Balu, Ganesh
Balasubramanian, Duane D. Johnson and Soumik Sarkar
|
3D Deep Learning with voxelized atomic configurations for modeling
atomistic potentials in complex solid-solution alloys
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.comp-ph stat.ML
|
The need for advanced materials has led to the development of complex,
multi-component alloys or solid-solution alloys. These materials have shown
exceptional properties like strength, toughness, ductility, electrical and
electronic properties. Current development of such material systems are
hindered by expensive experiments and computationally demanding
first-principles simulations. Atomistic simulations can provide reasonable
insights on properties in such material systems. However, the issue of
designing robust potentials still exists. In this paper, we explore a deep
convolutional neural-network based approach to develop the atomistic potential
for such complex alloys to investigate materials for insights into controlling
properties. In the present work, we propose a voxel representation of the
atomic configuration of a cell and design a 3D convolutional neural network to
learn the interaction of the atoms. Our results highlight the performance of
the 3D convolutional neural network and its efficacy in machine-learning the
atomistic potential. We also explore the role of voxel resolution and provide
insights into the two bounding box methodologies implemented for voxelization.
|
[{'version': 'v1', 'created': 'Fri, 23 Nov 2018 23:12:22 GMT'}]
|
2018-11-27
|
Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima, Yuki Sakishita, Ryo
Ogawa, Iwao Hosako, Atsutaka Maeda
|
Deep Learning Model for Finding New Superconductors
|
Phys. Rev. B 103, 014509 (2021)
|
10.1103/PhysRevB.103.014509
| null |
cs.LG cond-mat.mtrl-sci cond-mat.supr-con cs.CL physics.comp-ph
|
Exploration of new superconductors still relies on the experience and
intuition of experts and is largely a process of experimental trial and error.
In one study, only 3% of the candidate materials showed superconductivity.
Here, we report the first deep learning model for finding new superconductors.
We introduced the method named "reading periodic table" which represented the
periodic table in a way that allows deep learning to learn to read the periodic
table and to learn the law of elements for the purpose of discovering novel
superconductors that are outside the training data. It is recognized that it is
difficult for deep learning to predict something outside the training data.
Although we used only the chemical composition of materials as information, we
obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in
a database of superconductors. We also introduced the method named "garbage-in"
to create synthetic data of non-superconductors that do not exist.
Non-superconductors are not reported, but the data must be required for deep
learning to distinguish between superconductors and non-superconductors. We
obtained three remarkable results. The deep learning can predict
superconductivity for a material with a precision of 62%, which shows the
usefulness of the model; it found the recently discovered superconductor CaBi2
and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor
database; and it found Fe-based high-temperature superconductors (discovered in
2008) from the training data before 2008. These results open the way for the
discovery of new high-temperature superconductor families. The candidate
materials list, data, and method are openly available from the link
https://github.com/tomo835g/Deep-Learning-to-find-Superconductors.
|
[{'version': 'v1', 'created': 'Mon, 3 Dec 2018 05:30:34 GMT'}, {'version': 'v2', 'created': 'Mon, 3 Jun 2019 07:22:53 GMT'}, {'version': 'v3', 'created': 'Sun, 3 Nov 2019 14:29:01 GMT'}, {'version': 'v4', 'created': 'Thu, 14 Jan 2021 14:36:38 GMT'}]
|
2021-01-20
|
Myungjoon Kim, Byung Chul Yeo, Sang Soo Han, Donghun Kim
|
Slab Graph Convolutional Neural Network for Discovery of N2
Electroreduction Catalysts
| null | null | null |
cond-mat.mtrl-sci
|
The catalyst development for N2 electroreduction reaction (NRR) with low
onset potential and high Faradaic efficiency is highly desired, but remains
challenging. Machine learning (ML) recently emerged as a complementary tool to
accelerate material discovery; however a ML model for NRR has yet to be
developed. Here, we develop and report slab-graph convolutional neural network
(SGCNN), an accurate and flexible ML model that is applicable to catalytic
surface reactions. With the self-accumulated database of 2,699 surface
calculations, SGCNN predict binding energies, ranging over 8 eV, of five key
adsorbates (*H, *N2, *N2H, *NH, *NH2) related to NRR performance with the
mean-absolute-error of only 0.23eV. Unlike previously available models, SGCNN
avoids using ab initio level inputs, instead is solely based on elemental
properties that are all readily available in Periodic-Table-of-Elements; true
accelerations can be realized. t-distributed stochastic neighbor embedding
(t-SNE) analysis reveals that binary intermetallics of averaged d-electron
occupation between 4 and 5 could potentially lower the onset potential in N2
electroreduction.
|
[{'version': 'v1', 'created': 'Fri, 7 Dec 2018 08:50:31 GMT'}, {'version': 'v2', 'created': 'Wed, 27 Mar 2019 01:07:09 GMT'}]
|
2019-03-28
|
Shweta Mehta, Sheena Agarwal, and Kavita Joshi
|
Combining DFT with ML to study size specific interactions between metal
clusters and adsorbates
| null | null | null |
cond-mat.mtrl-sci
|
To date, density functional theory (DFT) is one of the most accurate and yet
practical theory to gain insight about materials properties. Although
successful, the computational cost is the main hurdle even today. A way out is
combining DFT with machine learning (ML) to reduce the computational cost
without compromising accuracy. However, the success of this approach hinges on
the correctness of the descriptors. In the present work, we demonstrate that,
based on {\it only} interatomic distances as descriptors, our ML model predicts
interaction energy between an adsorbate and Al cluster with absolute mean error
(AME) $\sim$ 0.05 eV (or less) and reproduces the PES experienced by an
incoming atom. Our extensive DFT calculations reveal that atoms experiencing
identical environment within a cluster have identical interaction energy
patterns. Further, we demonstrate that our model is not specific to Al
clusters, and could be applied to clusters of different elements as well. Its
application to compute PES experienced by various test atoms and molecules in
the vicinity of different clusters proves the transferability of the model not
just to clusters of different elements but also to various molecules. The
descriptors chosen are invariant to rotation, translation, and permutation yet
very simple to compute is one of the most crucial points of the present work.
|
[{'version': 'v1', 'created': 'Wed, 12 Dec 2018 13:21:40 GMT'}, {'version': 'v2', 'created': 'Wed, 2 Jan 2019 05:22:03 GMT'}, {'version': 'v3', 'created': 'Tue, 9 Apr 2019 10:00:00 GMT'}, {'version': 'v4', 'created': 'Thu, 18 Apr 2019 06:46:13 GMT'}]
|
2019-04-19
|
Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey
C. Grossman
|
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale
Dynamics in Materials
|
Nat. Commun. 10, 2667 (2019)
|
10.1038/s41467-019-10663-6
| null |
cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Understanding the dynamical processes that govern the performance of
functional materials is essential for the design of next generation materials
to tackle global energy and environmental challenges. Many of these processes
involve the dynamics of individual atoms or small molecules in condensed
phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten
atoms at interfaces, etc., which are difficult to understand due to the
complexity of local environments. In this work, we develop graph dynamical
networks, an unsupervised learning approach for understanding atomic scale
dynamics in arbitrary phases and environments from molecular dynamics
simulations. We show that important dynamical information can be learned for
various multi-component amorphous material systems, which is difficult to
obtain otherwise. With the large amounts of molecular dynamics data generated
everyday in nearly every aspect of materials design, this approach provides a
broadly useful, automated tool to understand atomic scale dynamics in material
systems.
|
[{'version': 'v1', 'created': 'Mon, 18 Feb 2019 23:17:27 GMT'}, {'version': 'v2', 'created': 'Wed, 22 May 2019 20:58:39 GMT'}]
|
2019-07-11
|
Nouamane Laanait and Qian He and Albina Y. Borisevich
|
Reconstruction of 3-D Atomic Distortions from Electron Microscopy with
Deep Learning
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Deep learning has demonstrated superb efficacy in processing imaging data,
yet its suitability in solving challenging inverse problems in scientific
imaging has not been fully explored. Of immense interest is the determination
of local material properties from atomically-resolved imaging, such as electron
microscopy, where such information is encoded in subtle and complex data
signatures, and whose recovery and interpretation necessitate intensive
numerical simulations subject to the requirement of near-perfect knowledge of
the experimental setup. We demonstrate that an end-to-end deep learning model
can successfully recover 3-dimensional atomic distortions of a variety of oxide
perovskite materials from a single 2-dimensional experimental scanning
transmission electron (STEM) micrograph, in the process resolving a
longstanding question in the recovery of 3-D atomic distortions from STEM
experiments. Our results indicate that deep learning is a promising approach to
efficiently address unsolved inverse problems in scientific imaging and to
underpin novel material investigations at atomic resolution.
|
[{'version': 'v1', 'created': 'Tue, 19 Feb 2019 03:31:53 GMT'}]
|
2019-02-20
|
Mohammad Rashidi, Jeremiah Croshaw, Kieran Mastel, Marcus Tamura,
Hedieh Hosseinzadeh, and Robert A. Wolkow
|
Deep Learning-Guided Surface Characterization for Autonomous Hydrogen
Lithography
|
Mach. Learn.: Sci. Technol. 1 025001 (2020)
|
10.1088/2632-2153/ab6d5e
| null |
cond-mat.mtrl-sci
|
As the development of atom scale devices transitions from novel,
proof-of-concept demonstrations to state-of-the-art commercial applications,
automated assembly of such devices must be implemented. Here we present an
automation method for the identification of defects prior to atomic fabrication
via hydrogen lithography using deep learning. We trained a convolutional neural
network to locate and differentiate between surface features of the
technologically relevant hydrogen-terminated silicon surface imaged using a
scanning tunneling microscope. Once the positions and types of surface features
are determined, the predefined atomic structures are patterned in a defect-free
area. By training the network to differentiate between common defects we are
able to avoid charged defects as well as edges of the patterning terraces.
Augmentation with previously developed autonomous tip shaping and patterning
modules allows for atomic scale lithography with minimal user intervention.
|
[{'version': 'v1', 'created': 'Sat, 23 Feb 2019 17:37:28 GMT'}, {'version': 'v2', 'created': 'Fri, 11 Oct 2019 18:57:15 GMT'}]
|
2020-03-26
|
Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong, Satoshi Watanabe, Seungwu Han
|
Atomic energy mapping of neural network potential
|
Phys. Rev. Materials 3, 093802 (2019)
|
10.1103/PhysRevMaterials.3.093802
| null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
We show that the intelligence of the machine-learning potential arises from
its ability to infer the reference atomic-energy function from a given set of
total energies. By utilizing invariant points in the feature space at which the
atomic energy has a fixed reference value, we examine the atomic energy mapping
of neural network potentials. Through a series of examples on Si, we
demonstrate that the neural network potential is vulnerable to 'ad hoc' mapping
in which the total energy appears to be trained accurately while the atomic
energy mapping is incorrect in spite of its capability. We show that the energy
mapping can be improved by choosing the training set carefully and monitoring
the atomic energy at the invariant points during the training procedure.
|
[{'version': 'v1', 'created': 'Mon, 11 Mar 2019 15:21:01 GMT'}]
|
2019-09-05
|
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler, Subramanian K.R.S.
Sankaranarayanan, David N. Seidman, Prasanna Balaprakash, and Olle Heinonen
|
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based
Edge Detection
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Atom-probe tomography (APT) facilitates nano- and atomic-scale
characterization and analysis of microstructural features. Specifically, APT is
well suited to study the interfacial properties of granular or heterophase
systems. Traditionally, the identification of the interface between, for
precipitate and matrix phases, in APT data has been obtained either by
extracting iso-concentration surfaces based on a user-supplied concentration
value or by manually perturbing the concentration value until the
iso-concentration surface qualitatively matches the interface. These approaches
are subjective, not scalable, and may lead to inconsistencies due to local
composition inhomogeneities.
We propose a digital image segmentation approach based on deep neural
networks that transfer learned knowledge from natural images to automatically
segment the data obtained from APT into different phases. This approach not
only provides an efficient way to segment the data and extract interfacial
properties but does so without the need for expensive interface labeling for
training the segmentation model.
We consider here a system with a precipitate phase in a matrix and with three
different interface modalities---layered, isolated, and interconnected---that
are obtained for different relative geometries of the precipitate phase. We
demonstrate the accuracy of our segmentation approach through qualitative
visualization of the interfaces, as well as through quantitative comparisons
with proximity histograms obtained by using more traditional approaches.
|
[{'version': 'v1', 'created': 'Wed, 10 Apr 2019 20:53:33 GMT'}]
|
2019-04-12
|
Jutta Rogal, Elia Schneider, Mark E. Tuckerman
|
Neural network based path collective variables for enhanced sampling of
phase transformations
|
Phys. Rev. Lett. 123, 245701 (2019)
|
10.1103/PhysRevLett.123.245701
| null |
cond-mat.mtrl-sci cond-mat.stat-mech physics.comp-ph
|
We propose a rigorous construction of a 1D path collective variable to sample
structural phase transformations in condensed matter. The path collective
variable is defined in a space spanned by global collective variables that
serve as classifiers derived from local structural units. A reliable
identification of local structural environments is achieved by employing a
neural network based classification. The 1D path collective variable is
subsequently used together with enhanced sampling techniques to explore the
complex migration of a phase boundary during a solid-solid phase transformation
in molybdenum.
|
[{'version': 'v1', 'created': 'Sat, 4 May 2019 18:05:54 GMT'}]
|
2022-12-09
|
Liang Li, Mindren Lu, and Maria K. Y. Chan
|
A Deep Learning Model for Atomic Structures Prediction Using X-ray
Absorption Spectroscopic Data
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
A deep neural network (DNN) model consisting of two hidden layers was
proposed for predicting the immediate environments of specific atoms based on
X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be
adjusted to form a classifier or regressor, to predict the local and overall
coordination environments, respectively. Using Li3FeO3.5 as a model system, it
was demonstrated that the prediction accuracy of the DNN classifier is higher
than 98%, and the predictions of the DNN regressor also showed notable
agreement with the ground truth. Therefore, despite its simplicity, this DNN
architecture can be expected to be generally capable of predicting the
structural properties of various systems. Fine tuning of the hyperparameters,
bias-variance tradeoff, and strategies to enrich the versatility of the model
were also discussed.
|
[{'version': 'v1', 'created': 'Fri, 10 May 2019 04:08:40 GMT'}]
|
2019-05-13
|
Emi Minamitani, Masayoshi Ogura, Satoshi Watanabe
|
Simulating lattice thermal conductivity in semiconducting materials
using high-dimensional neural network potential
| null |
10.7567/1882-0786/ab36bc
| null |
cond-mat.mtrl-sci physics.comp-ph
|
We demonstrate that a high-dimensional neural network potential (HDNNP) can
predict the lattice thermal conductivity of semiconducting materials with an
accuracy comparable to that of density functional theory (DFT) calculation.
After a training procedure based on the force, the root mean square error
between the forces predicted by the HDNNP and DFT is less than 40 meV/{\AA}. As
typical examples, we present the results for Si and GaN bulk crystals. The
deviation from the thermal conductivity calculated using DFT is within 1% at
200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN.
|
[{'version': 'v1', 'created': 'Tue, 21 May 2019 09:13:33 GMT'}]
|
2019-08-16
|
Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk,
Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, and T.
Yong-Jin Han
|
Predicting Compressive Strength of Consolidated Molecular Solids Using
Computer Vision and Deep Learning
| null |
10.1016/j.matdes.2020.108541
| null |
physics.comp-ph cond-mat.mtrl-sci
|
We explore the application of computer vision and machine learning (ML)
techniques to predict material properties (e.g. compressive strength) based on
SEM images. We show that it's possible to train ML models to predict materials
performance based on SEM images alone, demonstrating this capability on the
real-world problem of predicting uniaxially compressed peak stress of
consolidated molecular solids samples. Our image-based ML approach reduces mean
absolute percent error (MAPE) by an average of 24% over baselines
representative of the current state-of-the-practice (i.e., domain-expert's
analysis and correlation). We compared two complementary approaches to this
problem: (1) a traditional ML approach, random forest (RF), using
state-of-the-art computer vision features and (2) an end-to-end deep learning
(DL) approach, where features are learned automatically from raw images. We
demonstrate the complementarity of these approaches, showing that RF performs
best in the "small data" regime in which many real-world scientific
applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in
the "big data" regime, where abundant training samples are available (up to 24%
lower RMSE than RF). Finally, we demonstrate that models trained using machine
learning techniques are capable of discovering and utilizing informative
crystal attributes previously underutilized by domain experts.
|
[{'version': 'v1', 'created': 'Wed, 5 Jun 2019 16:49:00 GMT'}, {'version': 'v2', 'created': 'Sat, 9 Nov 2019 04:52:14 GMT'}, {'version': 'v3', 'created': 'Fri, 28 Feb 2020 01:54:33 GMT'}]
|
2020-03-02
|
Cheol Woo Park, Chris Wolverton
|
Developing an improved Crystal Graph Convolutional Neural Network
framework for accelerated materials discovery
|
Phys. Rev. Materials 4, 063801 (2020)
|
10.1103/PhysRevMaterials.4.063801
| null |
physics.comp-ph cond-mat.mtrl-sci physics.data-an
|
The recently proposed crystal graph convolutional neural network (CGCNN)
offers a highly versatile and accurate machine learning (ML) framework by
learning material properties directly from graph-like representations of
crystal structures ("crystal graphs"). Here, we develop an improved variant of
the CGCNN model (iCGCNN) that outperforms the original by incorporating
information of the Voronoi tessellated crystal structure, explicit 3-body
correlations of neighboring constituent atoms, and an optimized chemical
representation of interatomic bonds in the crystal graphs. We demonstrate the
accuracy of the improved framework in two distinct illustrations: First, when
trained/validated on 180,000/20,000 density functional theory (DFT) calculated
thermodynamic stability entries taken from the Open Quantum Materials Database
(OQMD) and evaluated on a separate test set of 230,000 entries, iCGCNN achieves
a predictive accuracy that is significantly improved, i.e., 20% higher than
that of the original CGCNN. Second, when used to assist high-throughput search
for materials in the ThCr2Si2 structure-type, iCGCNN exhibited a success rate
of 31% which is 310 times higher than an undirected high-throughput search and
2.4 times higher than that of the original CGCNN. Using both CGCNN and iCGCNN,
we screened 132,600 compounds with elemental decorations of the ThCr2Si2
prototype crystal structure and identified a total of 97 new unique stable
compounds by performing 757 DFT calculations, accelerating the computational
time of the high-throughput search by a factor of 130. Our results suggest that
the iCGCNN can be used to accelerate high-throughput discoveries of new
materials by quickly and accurately identifying crystalline compounds with
properties of interest.
|
[{'version': 'v1', 'created': 'Wed, 12 Jun 2019 17:47:43 GMT'}]
|
2020-07-01
|
Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car
|
Deep neural network for the dielectric response of insulators
|
Phys. Rev. B 102, 041121 (2020)
|
10.1103/PhysRevB.102.041121
| null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
We introduce a deep neural network to model in a symmetry preserving way the
environmental dependence of the centers of the electronic charge. The model
learns from ab-initio density functional theory, wherein the electronic centers
are uniquely assigned by the maximally localized Wannier functions. When
combined with the Deep Potential model of the atomic potential energy surface,
the scheme predicts the dielectric response of insulators for trajectories
inaccessible to direct ab-initio simulation. The scheme is non-perturbative and
can capture the response of a mutating chemical environment. We demonstrate the
approach by calculating the infrared spectra of liquid water at standard
conditions, and of ice under extreme pressure, when it transforms from a
molecular to an ionic crystal.
|
[{'version': 'v1', 'created': 'Thu, 27 Jun 2019 04:44:07 GMT'}, {'version': 'v2', 'created': 'Tue, 2 Jul 2019 14:45:58 GMT'}, {'version': 'v3', 'created': 'Sat, 7 Sep 2019 05:11:51 GMT'}, {'version': 'v4', 'created': 'Mon, 3 Feb 2020 11:58:17 GMT'}, {'version': 'v5', 'created': 'Tue, 9 Jun 2020 22:38:05 GMT'}]
|
2020-07-29
|
Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli
|
PANNA: Properties from Artificial Neural Network Architectures
| null |
10.1016/j.cpc.2020.107402
| null |
physics.comp-ph cond-mat.mtrl-sci
|
Prediction of material properties from first principles is often a
computationally expensive task. Recently, artificial neural networks and other
machine learning approaches have been successfully employed to obtain accurate
models at a low computational cost by leveraging existing example data. Here,
we present a software package "Properties from Artificial Neural Network
Architectures" (PANNA) that provides a comprehensive toolkit for creating
neural network models for atomistic systems. Besides the core routines for
neural network training, it includes data parser, descriptor builder and
force-field generator suitable for integration within molecular dynamics
packages. PANNA offers a variety of activation and cost functions,
regularization methods, as well as the possibility of using fully-connected
networks with custom size for each atomic species. PANNA benefits from the
optimization and hardware-flexibility of the underlying TensorFlow engine which
allows it to be used on multiple CPU/GPU/TPU systems, making it possible to
develop and optimize neural network models based on large datasets.
|
[{'version': 'v1', 'created': 'Sat, 6 Jul 2019 00:42:46 GMT'}]
|
2020-07-15
|
Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi
Kondor
|
Deep Learning for Automated Classification and Characterization of
Amorphous Materials
| null | null | null |
cond-mat.soft cond-mat.dis-nn cond-mat.mtrl-sci cs.LG stat.ML
|
It is difficult to quantify structure-property relationships and to identify
structural features of complex materials. The characterization of amorphous
materials is especially challenging because their lack of long-range order
makes it difficult to define structural metrics. In this work, we apply deep
learning algorithms to accurately classify amorphous materials and characterize
their structural features. Specifically, we show that convolutional neural
networks and message passing neural networks can classify two-dimensional
liquids and liquid-cooled glasses from molecular dynamics simulations with
greater than 0.98 AUC, with no a priori assumptions about local particle
relationships, even when the liquids and glasses are prepared at the same
inherent structure energy. Furthermore, we demonstrate that message passing
neural networks surpass convolutional neural networks in this context in both
accuracy and interpretability. We extract a clear interpretation of how message
passing neural networks evaluate liquid and glass structures by using a
self-attention mechanism. Using this interpretation, we derive three novel
structural metrics that accurately characterize glass formation. The methods
presented here provide us with a procedure to identify important structural
features in materials that could be missed by standard techniques and give us a
unique insight into how these neural networks process data.
|
[{'version': 'v1', 'created': 'Tue, 10 Sep 2019 17:49:04 GMT'}]
|
2019-09-11
|
Giovanni Drera, Chahan M. Kropf, Luigi Sangaletti
|
Deep neural network for X-ray photoelectron spectroscopy data analysis
| null | null | null |
cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph
|
In this work, we characterize the performance of a deep convolutional neural
network designed to detect and quantify chemical elements in experimental X-ray
photoelectron spectroscopy data. Given the lack of a reliable database in
literature, in order to train the neural network we computed a large ($>$100 k)
dataset of synthetic spectra, based on randomly generated materials covered
with a layer of adventitious carbon. The trained net performs as good as
standard methods on a test set of $\approx$ 500 well characterized experimental
X-ray photoelectron spectra. Fine details about the net layout, the choice of
the loss function and the quality assessment strategies are presented and
discussed. Given the synthetic nature of the training set, this approach could
be applied to the automatization of any photoelectron spectroscopy system,
without the need of experimental reference spectra and with a low computational
effort.
|
[{'version': 'v1', 'created': 'Thu, 12 Sep 2019 09:28:21 GMT'}]
|
2019-09-13
|
Mingjian Wen and Ellad B. Tadmor
|
Hybrid neural network potential for multilayer graphene
|
Phys. Rev. B 100, 195419 (2019)
|
10.1103/PhysRevB.100.195419
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Monolayer and multilayer graphene are promising materials for applications
such as electronic devices, sensors, energy generation and storage, and
medicine. In order to perform large-scale atomistic simulations of the
mechanical and thermal behavior of graphene-based devices, accurate interatomic
potentials are required. Here, we present a new interatomic potential for
multilayer graphene structures referred to as "hNN--Gr$_x$." This hybrid
potential employs a neural network to describe short-range interactions and a
theoretically-motivated analytical term to model long-range dispersion. The
potential is trained against a large dataset of monolayer graphene, bilayer
graphene, and graphite configurations obtained from ab initio total-energy
calculations based on density functional theory (DFT). The potential provides
accurate energy and forces for both intralayer and interlayer interactions,
correctly reproducing DFT results for structural, energetic, and elastic
properties such as the equilibrium layer spacing, interlayer binding energy,
elastic moduli, and phonon dispersions to which it was not fit. The potential
is used to study the effect of vacancies on thermal conductivity in monolayer
graphene and interlayer friction in bilayer graphene. The potential is
available through the OpenKIM interatomic potential repository at
\url{https://openkim.org}.
|
[{'version': 'v1', 'created': 'Mon, 23 Sep 2019 03:01:10 GMT'}, {'version': 'v2', 'created': 'Sat, 16 Nov 2019 21:41:07 GMT'}]
|
2019-11-27
|
Anton S. Bochkarev, Ambroise van Roekeghem, Stefano Mossa, Natalio
Mingo
|
Anharmonic Thermodynamics of Vacancies Using a Neural Network Potential
| null |
10.1103/PhysRevMaterials.3.093803
| null |
cond-mat.mtrl-sci
|
Lattice anharmonicity is thought to strongly affect vacancy concentrations in
metals at high temperatures. It is however non-trivial to account for this
effect directly using density functional theory (DFT). Here we develop a deep
neural network potential for aluminum that overcomes the limitations inherent
to DFT, and we use it to obtain accurate anharmonic vacancy formation free
energies as a function of temperature. While confirming the important role of
anharmonicity at high temperatures, the calculation unveils a markedly
nonlinear behavior of the vacancy formation entropy and shows that the vacancy
formation free energy only violates Arrhenius law at temperatures above 600 K,
in contrast with previous DFT calculations.
|
[{'version': 'v1', 'created': 'Mon, 23 Sep 2019 09:52:30 GMT'}]
|
2019-10-02
|
Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean
Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael
Matheson
|
Exascale Deep Learning for Scientific Inverse Problems
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.DC physics.comp-ph stat.ML
|
We introduce novel communication strategies in synchronous distributed Deep
Learning consisting of decentralized gradient reduction orchestration and
computational graph-aware grouping of gradient tensors. These new techniques
produce an optimal overlap between computation and communication and result in
near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100
GPUs on the Summit Supercomputer. We demonstrate our gradient reduction
techniques in the context of training a Fully Convolutional Neural Network to
approximate the solution of a longstanding scientific inverse problem in
materials imaging. The efficient distributed training on a dataset size of 0.5
PB, produces a model capable of an atomically-accurate reconstruction of
materials, and in the process reaching a peak performance of 2.15(4)
EFLOPS$_{16}$.
|
[{'version': 'v1', 'created': 'Tue, 24 Sep 2019 19:40:59 GMT'}]
|
2019-09-26
|
Shenghong Ju, Ryo Yoshida, Chang Liu, Kenta Hongo, Terumasa Tadano,
Junichiro Shiomi
|
Exploring diamond-like lattice thermal conductivity crystals via
feature-based transfer learning
|
Phys. Rev. Materials 5, 053801 (2021)
|
10.1103/PhysRevMaterials.5.053801
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Ultrahigh lattice thermal conductivity materials hold great importance since
they play a critical role in the thermal management of electronic and optical
devices. Models using machine learning can search for materials with
outstanding higher-order properties like thermal conductivity. However, the
lack of sufficient data to train a model is a serious hurdle. Herein we show
that big data can complement small data for accurate predictions when
lower-order feature properties available in big data are selected properly and
applied to transfer learning. The connection between the crystal information
and thermal conductivity is directly built with a neural network by
transferring descriptors acquired through a pre-trained model for the feature
property. Successful transfer learning shows the ability of extrapolative
prediction and reveals descriptors for lattice anharmonicity. Transfer learning
is employed to screen over 60000 compounds to identify novel crystals that can
serve as alternatives to diamond.
|
[{'version': 'v1', 'created': 'Wed, 25 Sep 2019 00:10:13 GMT'}]
|
2021-05-19
|
Pietro D'Antuono and Michele Ciavarella
|
Mean stress effect on Ga{\ss}ner curves interpreted as shifted W\"ohler
curves
| null | null | null |
physics.app-ph cond-mat.mtrl-sci
|
A criterion for the mean stress effect correction in the shift factor
approach for variable amplitude life prediction is presented for both smooth
and notched specimens. The criterion is applied to the simple idea proposed by
the authors in a previous note that Ga{\ss}ner curves can be interpreted as
shifted W\"ohler curves. The mean stress correction used has been proposed by
Smith, Watson and Topper and, more in general, by Walker. By applying the
correction, a new expression for the shift factor G is obtained and, through
the application of the theory of the critical distances in its point variant,
surprisingly G is demonstrated to be valid for both smooth and notched
geometries since it does not seem to depend on the geometry, but only on the
fatigue exponent and the loading history. Finally, a comparison with the SAE
Keyhole test program data is added to substantiate the findings.
|
[{'version': 'v1', 'created': 'Sun, 29 Sep 2019 17:28:27 GMT'}]
|
2019-10-01
|
Tarak K Patra, Troy D. Loeffler, Henry Chan, Mathew J. Cherukara,
Badri Narayanan and Subramanian K.R.S. Sankaranarayanan
|
A coarse-grained deep neural network model for liquid water
| null |
10.1063/1.5116591
| null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
We introduce a coarse-grained deep neural network model (CG-DNN) for liquid
water that utilizes 50 rotational and translational invariant coordinates, and
is trained exclusively against energies of ~30,000 bulk water configurations.
Our CG-DNN potential accurately predicts both the energies and molecular forces
of water; within 0.9 meV/molecule and 54 meV/angstrom of a reference
(coarse-grained bond-order potential) model. The CG-DNN water model also
provides good prediction of several structural, thermodynamic, and temperature
dependent properties of liquid water, with values close to that obtained from
the reference model. More importantly, CG-DNN captures the well-known density
anomaly of liquid water observed in experiments. Our work lays the groundwork
for a scheme where existing empirical water models can be utilized to develop
fully flexible neural network framework that can subsequently be trained
against sparse data from high-fidelity albeit expensive beyond-DFT
calculations.
|
[{'version': 'v1', 'created': 'Tue, 1 Oct 2019 08:32:01 GMT'}, {'version': 'v2', 'created': 'Mon, 14 Oct 2019 16:28:45 GMT'}]
|
2020-01-08
|
Ari Frankel, Kousuke Tachida, Reese Jones
|
Prediction of the evolution of the stress field of polycrystals
undergoing elastic-plastic deformation with a hybrid neural network model
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
Crystal plasticity theory is often employed to predict the mesoscopic states
of polycrystalline metals, and is well-known to be costly to simulate. Using a
neural network with convolutional layers encoding correlations in time and
space, we were able to predict the evolution of the stress field given only the
initial microstructure and external loading. In comparison to our recent work
we were able to predict not only the spatial average of the stress response but
the field itself. We show that the stress fields and their rates are in high
fidelity with the crystal plasticity data and have no visible artifacts.
Furthermore the distribution stress throughout the elastic to fully plastic
transition match the truth provided by held out crystal plasticity data. Lastly
we demonstrate the efficacy of the trained model in material characterization
and optimization tasks.
|
[{'version': 'v1', 'created': 'Tue, 8 Oct 2019 02:16:45 GMT'}]
|
2019-10-09
|
Ryan Jacobs, Tam Mayeshiba, Ben Afflerbach, Luke Miles, Max Williams,
Matthew Turner, Raphael Finkel, Dane Morgan
|
The Materials Simulation Toolkit for Machine Learning (MAST-ML): an
automated open source toolkit to accelerate data-driven materials research
|
Computational Materials Science, 176, 2020
|
10.1016/j.commatsci.2020.109544
| null |
physics.comp-ph cond-mat.mtrl-sci
|
As data science and machine learning methods are taking on an increasingly
important role in the materials research community, there is a need for the
development of machine learning software tools that are easy to use (even for
nonexperts with no programming ability), provide flexible access to the most
important algorithms, and codify best practices of machine learning model
development and evaluation. Here, we introduce the Materials Simulation Toolkit
for Machine Learning (MAST-ML), an open source Python-based software package
designed to broaden and accelerate the use of machine learning in materials
science research. MAST-ML provides predefined routines for many input setup,
model fitting, and post-analysis tasks, as well as a simple structure for
executing a multi-step machine learning model workflow. In this paper, we
describe how MAST-ML is used to streamline and accelerate the execution of
machine learning problems. We walk through how to acquire and run MAST-ML,
demonstrate how to execute different components of a supervised machine
learning workflow via a customized input file, and showcase a number of
features and analyses conducted automatically during a MAST-ML run. Further, we
demonstrate the utility of MAST-ML by showcasing examples of recent materials
informatics studies which used MAST-ML to formulate and evaluate various
machine learning models for an array of materials applications. Finally, we lay
out a vision of how MAST-ML, together with complementary software packages and
emerging cyberinfrastructure, can advance the rapidly growing field of
materials informatics, with a focus on producing machine learning models
easily, reproducibly, and in a manner that facilitates model evolution and
improvement in the future.
|
[{'version': 'v1', 'created': 'Mon, 14 Oct 2019 17:20:56 GMT'}]
|
2020-06-26
|
Jeffrey M. Ede
|
Deep Learning Supersampled Scanning Transmission Electron Microscopy
| null | null | null |
eess.IV cond-mat.mtrl-sci cs.CV
|
Compressed sensing can increase resolution, and decrease electron dose and
scan time of electron microscope point-scan systems with minimal information
loss. Building on a history of successful deep learning applications in
compressed sensing, we have developed a two-stage multiscale generative
adversarial network to supersample scanning transmission electron micrographs
with point-scan coverage reduced to 1/16, 1/25, ..., 1/100 px. We propose a
novel non-adversarial learning policy to train a unified generator for multiple
coverages and introduce an auxiliary network to homogenize prioritization of
training data with varied signal-to-noise ratios. This achieves root mean
square errors of 3.23% and 4.54% at 1/16 px and 1/100 px coverage,
respectively; within 1% of errors for networks trained for each coverage
individually. Detailed error distributions are presented for unified and
individual coverage generators, including errors per output pixel. In addition,
we present a baseline one-stage network for a single coverage and investigate
numerical precision for web serving. Source code, training data, and pretrained
models are publicly available at https://github.com/Jeffrey-Ede/DLSS-STEM
|
[{'version': 'v1', 'created': 'Wed, 23 Oct 2019 11:30:25 GMT'}, {'version': 'v2', 'created': 'Fri, 25 Oct 2019 09:39:05 GMT'}]
|
2019-10-28
|
Divya Kaushik, Utkarsh Singh, Upasana Sahu, Indu Sreedevi and Debanjan
Bhowmik
|
Comparing domain wall synapse with other Non Volatile Memory devices for
on-chip learning in Analog Hardware Neural Network
| null | null | null |
physics.app-ph cond-mat.mtrl-sci cs.NE
|
Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices
have been popularly used as synapses in crossbar array based analog Neural
Network (NN) circuit to achieve more energy and time efficient data
classification compared to conventional computers. Here we demonstrate the
advantages of recently proposed spin orbit torque driven Domain Wall (DW)
device as synapse compared to the RRAM and PCM devices with respect to on-chip
learning (training in hardware) in such NN. Synaptic characteristic of DW
synapse, obtained by us from micromagnetic modeling, turns out to be much more
linear and symmetric (between positive and negative update) than that of RRAM
and PCM synapse. This makes design of peripheral analog circuits for on-chip
learning much easier in DW synapse based NN compared to that for RRAM and PCM
synapses. We next incorporate the DW synapse as a Verilog-A model in the
crossbar array based NN circuit we design on SPICE circuit simulator.
Successful on-chip learning is demonstrated through SPICE simulations on the
popular Fisher's Iris dataset. Time and energy required for learning turn out
to be orders of magnitude lower for DW synapse based NN circuit compared to
that for RRAM and PCM synapse based NN circuits.
|
[{'version': 'v1', 'created': 'Mon, 28 Oct 2019 19:25:21 GMT'}]
|
2019-10-30
|
Chengqiang Lu, Qi Liu, Qiming Sun, Chang-Yu Hsieh, Shengyu Zhang,
Liang Shi, and Chee-Kong Lee
|
Deep Learning for Optoelectronic Properties of Organic Semiconductors
|
J. Phys. Chem. C 2020, 124, 13, 7048
| null | null |
physics.chem-ph cond-mat.mtrl-sci physics.comp-ph
|
Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and
its effects on the optoelectronic properties of OSCs requires a large number of
excited-state electronic-structure calculations, a computationally daunting
task for many OSC applications. In this work, we advocate the use of deep
learning to address this challenge and demonstrate that state-of-the-art deep
neural networks (DNNs) are capable of predicting the electronic properties of
OSCs at an accuracy comparable with the quantum chemistry methods used for
generating training data. We extensively investigate the performances of four
recent DNNs (deep tensor neural network, SchNet, message passing neural
network, and multilevel graph convolutional neural network) in predicting
various electronic properties of an important class of OSCs, i.e.,
oligothiophenes (OTs), including their HOMO and LUMO energies, excited-state
energies and associated transition dipole moments. We find that SchNet shows
the best performance for OTs of different sizes (from bithiophene to
sexithiophene), achieving average prediction errors in the range of 20-80meV
compared to the results from (time-dependent) density functional theory. We
show that SchNet also consistently outperforms shallow feed-forward neural
networks, especially in difficult cases with large molecules or limited
training data. We further show that SchNet could predict the transition dipole
moment accurately, a task previously known to be difficult for feed-forward
neural networks, and we ascribe the relatively large errors in transition
dipole prediction seen for some OT configurations to the charge-transfer
character of their excited states. Finally, we demonstrate the effectiveness of
SchNet by modeling the UV-Vis absorption spectra of OTs in dichloromethane and
a good agreement is observed between the calculated and experimental spectra.
|
[{'version': 'v1', 'created': 'Tue, 29 Oct 2019 21:42:02 GMT'}]
|
2021-05-10
|
Ka-Ming Tam, Nicholas Walker, Samuel Kellar, Mark Jarrell
|
Interatomic Potential in a Simple Dense Neural Network Representation
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci cond-mat.stat-mech
|
Simulations at the atomic scale provide a direct and effective way to
understand the mechanical properties of materials. In the regime of classical
mechanics, simulations for the thermodynamic properties of metals and alloys
can be done by either solving the equations of motion or performing Monte Carlo
sampling. The key component for an accurate simulation of such physical systems
to produce faithful physical quantities is the use of an appropriate potential
or a force field. In this paper, we explore the use of methods from the realm
of machine learning to overcome and bypass difficulties encountered when
fitting potentials for atomic systems. Particularly, we will show that
classical potentials can be represented by a dense neural network with good
accuracy.
|
[{'version': 'v1', 'created': 'Mon, 4 Nov 2019 17:51:13 GMT'}]
|
2019-11-05
|
Anh D. Phan, Cuong V. Nguyen, Pham T. Linh, Tran V. Huynh, Vu D. Lam,
and Anh-Tuan Le
|
Deep Learning for The Inverse Design of Mid-infrared Graphene Plasmons
| null | null | null |
physics.app-ph cond-mat.mes-hall cond-mat.mtrl-sci physics.optics
|
We theoretically investigate the plasmonic properties of mid-infrared
graphene-based metamaterials and apply deep learning of a neural network for
the inverse design. These artificial structures have square periodic arrays of
graphene plasmonic resonators deposited on dielectric thin films. Optical
spectra vary significantly with changes in structural parameters. Our numerical
results are in accordance with previous experiments. Then, the theoretical
approach is employed to generate data for training and testing deep neural
networks. By merging the pre-trained neural network with the inverse network,
we implement calculations for inverse design of the graphene-based
metameterials. We also discuss the limitation of the data-driven approach.
|
[{'version': 'v1', 'created': 'Thu, 28 Nov 2019 07:36:31 GMT'}, {'version': 'v2', 'created': 'Wed, 19 Feb 2020 06:17:32 GMT'}]
|
2020-02-20
|
So Takamoto, Satoshi Izumi, Ju Li
|
TeaNet: universal neural network interatomic potential inspired by
iterative electronic relaxations
|
Computational Materials Science 207 (2022) 111280
|
10.1016/j.commatsci.2022.111280
| null |
physics.comp-ph cond-mat.mtrl-sci cs.LG stat.ML
|
A universal interatomic potential for an arbitrary set of chemical elements
is urgently needed in computational materials science. Graph convolution neural
network (GCN) has rich expressive power, but previously was mainly employed to
transport scalars and vectors, not rank $\ge 2$ tensors. As classic interatomic
potentials were inspired by tight-binding electronic relaxation framework, we
want to represent this iterative propagation of rank $\ge 2$ tensor information
by GCN. Here we propose an architecture called the tensor embedded atom network
(TeaNet) where angular interaction is translated into graph convolution through
the incorporation of Euclidean tensors, vectors and scalars. By applying the
residual network (ResNet) architecture and training with recurrent GCN weights
initialization, a much deeper (16 layers) GCN was constructed, whose flow is
similar to an iterative electronic relaxation. Our traning dataset is generated
by density functional theory calculation of mostly chemically and structurally
randomized configurations. We demonstrate that arbitrary structures and
reactions involving the first 18 elements on the periodic table (H to Ar) can
be realized satisfactorily by TeaNet, including C-H molecular structures,
metals, amorphous SiO${}_2$, and water, showing surprisingly good performance
(energy mean absolute error 19 meV/atom) and robustness for arbitrary
chemistries involving elements from H to Ar.
|
[{'version': 'v1', 'created': 'Mon, 2 Dec 2019 08:47:16 GMT'}, {'version': 'v2', 'created': 'Sun, 10 Oct 2021 18:34:11 GMT'}]
|
2022-03-17
|
Ruiyang Li, Eungkyu Lee, Tengfei Luo
|
A Unified Deep Neural Network Potential Capable of Predicting Thermal
Conductivity of Silicon in Different Phases
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Molecular dynamics simulations have been extensively used to predict thermal
properties, but simulating different phases with similar precision using a
unified force field is often difficult, due to the lack of accurate and
transferrable interatomistic potential fields. As a result, this issue has
become a major barrier to predicting the phase change of materials and their
transport properties with atomistic-level modeling techniques. Recently,
machine learning based algorithms have emerged as promising tools to develop
accurate potentials for molecular dynamics simulations. In this work, we
approach the problem of predicting the thermal conductivity of silicon in
different phases by performing molecular dynamics simulations with a deep
neural network potential. This neural network potential is trained with
ab-initio data of silicon in the crystalline, liquid and amorphous phases. The
accuracy of our potential is first validated through reproducing the atomistic
structures during the phase transition, where other empirical potentials
usually fail. The thermal conductivity of different phases is then calculated,
showing a good agreement with the experimental results and ab-initio
calculation results. Our work shows that a unified neural network-based
potential can be a promising tool for studying phase change and thermal
transport of materials with high accuracy.
|
[{'version': 'v1', 'created': 'Tue, 10 Dec 2019 23:17:49 GMT'}]
|
2019-12-12
|
James P. Horwath, Dmitri N. Zakharov, Remi Megret, Eric A. Stach
|
Understanding Important Features of Deep Learning Models for
Transmission Electron Microscopy Image Segmentation
| null | null | null |
eess.IV cond-mat.mtrl-sci cs.LG
|
Cutting edge deep learning techniques allow for image segmentation with great
speed and accuracy. However, application to problems in materials science is
often difficult since these complex models may have difficultly learning
physical parameters. In situ electron microscopy provides a clear platform for
utilizing automated image analysis. In this work we consider the case of
studying coarsening dynamics in supported nanoparticles, which is important for
understanding e.g. the degradation of industrial catalysts. By systematically
studying dataset preparation, neural network architecture, and accuracy
evaluation, we describe important considerations in applying deep learning to
physical applications, where generalizable and convincing models are required.
|
[{'version': 'v1', 'created': 'Thu, 12 Dec 2019 16:52:23 GMT'}]
|
2019-12-13
|
R. Ravinder, Karthikeya H. Sreedhara, Suresh Bishnoi, Hargun Singh
Grover, Mathieu Bauchy, Jayadeva, Hariprasad Kodamana, N. M. Anoop Krishnan
|
Deep Learning Aided Rational Design of Oxide Glasses
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn physics.data-an
|
Despite the extensive usage of oxide glasses for a few millennia, the
composition-property relationships in these materials still remain poorly
understood. While empirical and physics-based models have been used to predict
properties, these remain limited to a few select compositions or a series of
glasses. Designing new glasses requires a priori knowledge of how the
composition of a glass dictates its properties such as stiffness, density, or
processability. Thus, accelerated design of glasses for targeted applications
remain impeded due to the lack of universal composition-property models.
Herein, using deep learning, we present a methodology for the rational design
of oxide glasses. Exploiting a large dataset of glasses comprising of up to 37
oxide components and more than 100,000 glass compositions, we develop
high-fidelity deep neural networks for the prediction of eight properties that
enable the design of glasses, namely, density, Young's modulus, shear modulus,
hardness, glass transition temperature, thermal expansion coefficient, liquidus
temperature, and refractive index. These models are by far the most extensive
models developed as they cover the entire range of human-made glass
compositions. We demonstrate that the models developed here exhibit excellent
predictability, ensuring close agreement with experimental observations. Using
these models, we develop a series of new design charts, termed as glass
selection charts. These charts enable the rational design of functional glasses
for targeted applications by identifying unique compositions that satisfy two
or more constraints, on both compositions and properties, simultaneously. The
generic design approach presented herein could catalyze machine-learning
assisted materials design and discovery for a large class of materials
including metals, ceramics, and proteins.
|
[{'version': 'v1', 'created': 'Wed, 25 Dec 2019 03:04:59 GMT'}]
|
2019-12-30
|
Reza Rashetnia and Mohammad Pour-Ghaz
|
Deep learning surrogate interacting Markov chain Monte Carlo based full
wave inversion scheme for properties of materials quantification
| null | null | null |
cond-mat.mtrl-sci cs.LG stat.ML
|
Full Wave Inversion (FWI) imaging scheme has many applications in
engineering, geoscience and medical sciences. In this paper, a surrogate deep
learning FWI approach is presented to quantify properties of materials using
stress waves. Such inverse problems, in general, are ill-posed and nonconvex,
especially in cases where the solutions exhibit shocks, heterogeneity,
discontinuities, or large gradients. The proposed approach is proven efficient
to obtain global minima responses in these cases. This approach is trained
based on random sampled set of material properties and sampled trials around
local minima, therefore, it requires a forward simulation can handle high
heterogeneity, discontinuities and large gradients. High resolution
Kurganov-Tadmor (KT) central finite volume method is used as forward wave
propagation operator. Using the proposed framework, material properties of 2D
media are quantified for several different situations. The results demonstrate
the feasibility of the proposed method for estimating mechanical properties of
materials with high accuracy using deep learning approaches.
|
[{'version': 'v1', 'created': 'Mon, 16 Dec 2019 19:43:51 GMT'}]
|
2020-01-08
|
Chia-Hao Lee (1), Abid Khan (2 and 3), Di Luo (2 and 3), Tatiane P.
Santos (1), Chuqiao Shi (1), Blanka E. Janicek (1), Sangmin Kang (4), Wenjuan
Zhu (4), Nahil A. Sobh (5), Andr\'e Schleife (1, 6 and 7), Bryan K. Clark
(2), Pinshane Y. Huang (1 and 6) ((1) Department of Materials Science and
Engineering, (2) Department of Physics, (3) These authors contributed equally
to this work, (4) Department of Electrical and Computer Engineering, (5)
Beckman Institute for Advanced Science and Technology, (6) Materials Research
Laboratory, (7) National Center for Supercomputing Applications)
|
Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D
Transition Metal Dichalcogenides with Sub-picometer Precision
| null |
10.1021/acs.nanolett.0c00269
| null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
2D materials offer an ideal platform to study the strain fields induced by
individual atomic defects, yet challenges associated with radiation damage have
so-far limited electron microscopy methods to probe these atomic-scale strain
fields. Here, we demonstrate an approach to probe single-atom defects with
sub-picometer precision in a monolayer 2D transition metal dichalcogenide,
WSe$_{2-2x}$Te$_{2x}$. We utilize deep learning to mine large datasets of
aberration-corrected scanning transmission electron microscopy images to locate
and classify point defects. By combining hundreds of images of nominally
identical defects, we generate high signal-to-noise class-averages which allow
us to measure 2D atomic coordinates with up to 0.3 pm precision. Our methods
reveal that Se vacancies introduce complex, oscillating strain fields in the
WSe$_{2-2x}$Te$_{2x}$ lattice which cannot be explained by continuum elastic
theory. These results indicate the potential impact of computer vision for the
development of high-precision electron microscopy methods for beam-sensitive
materials.
|
[{'version': 'v1', 'created': 'Wed, 22 Jan 2020 19:05:53 GMT'}]
|
2020-04-29
|
Saaketh Desai, Samuel Temple Reeve, James F. Belak
|
Implementing a neural network interatomic model with performance
portability for emerging exascale architectures
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
The two main thrusts of computational science are more accurate predictions
and faster calculations; to this end, the zeitgeist in molecular dynamics (MD)
simulations is pursuing machine learned and data driven interatomic models,
e.g. neural network potentials, and novel hardware architectures, e.g. GPUs.
Current implementations of neural network potentials are orders of magnitude
slower than traditional interatomic models and while looming exascale computing
offers the ability to run large, accurate simulations with these models,
achieving portable performance for MD with new and varied exascale hardware
requires rethinking traditional algorithms, using novel data structures, and
library solutions. We re-implement a neural network interatomic model in
CabanaMD, an MD proxy application, built on libraries developed for performance
portability. Our implementation shows significantly improved on-node scaling in
this complex kernel as compared to a current LAMMPS implementation, across both
strong and weak scaling. Our single-source solution results in improved
performance in many cases, with thread-scalability enabling simulations up to
21 million atoms on a single CPU node and 2 million atoms on a single GPU. We
also explore parallelism and data layout choices (using flexible data
structures called AoSoAs) and their effect on performance, seeing up to ~25%
and ~10% improvements in performance on a GPU simply by choosing the right
level of parallelism and data layout, respectively.
|
[{'version': 'v1', 'created': 'Fri, 31 Jan 2020 20:49:30 GMT'}, {'version': 'v2', 'created': 'Tue, 4 Feb 2020 19:22:38 GMT'}, {'version': 'v3', 'created': 'Fri, 21 Feb 2020 18:54:46 GMT'}]
|
2020-02-24
|
Maxim Ziatdinov, Udi Fuchs, James H.G. Owen, John N. Randall, Sergei
V. Kalinin
|
Robust multi-scale multi-feature deep learning for atomic and defect
identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface
| null | null | null |
cond-mat.mtrl-sci physics.app-ph
|
The nature of the atomic defects on the hydrogen passivated Si (100) surface
is analyzed using deep learning and scanning tunneling microscopy (STM). A
robust deep learning framework capable of identifying atomic species, defects,
in the presence of non-resolved contaminates, step edges, and noise is
developed. The automated workflow, based on the combination of several networks
for image assessment, atom-finding and defect finding, is developed to perform
the analysis at different levels of description and is deployed on an
operational STM platform. This is further extended to unsupervised
classification of the extracted defects using the mean-shift clustering
algorithm, which utilizes features automatically engineered from the combined
output of neural networks. This combined approach allows the identification of
localized and extended defects on the topographically non-uniform surfaces or
real materials. Our approach is universal in nature and can be applied to other
surfaces for building comprehensive libraries of atomic defects in quantum
materials.
|
[{'version': 'v1', 'created': 'Tue, 11 Feb 2020 22:18:28 GMT'}]
|
2020-02-19
|
Christopher M. Andolina, Philip Williamson, and Wissam A. Saidi
|
Optimization and Validation of a Deep Learning CuZr Atomistic Potential:
Robust Applications for Crystalline and Amorphous Phases with near-DFT
Accuracy
|
J. Chem. Phys. 152, 154701 (2020)
|
10.1063/5.0005347
| null |
cond-mat.mtrl-sci
|
We show that a deep-learning neural network potential (DP) based on density
functional theory (DFT) calculations can well describe Cu-Zr materials, an
example of a binary alloy system that can coexist in several ordered
intermetallics and as an amorphous phase. The complex phase diagram for Cu-Zr
makes it a challenging system for traditional atomistic force-fields that fail
to describe well the different properties and phases. Instead, we show that a
DP approach using a large database with ~300k configurations can render results
generally on par with DFT. The training set includes configurations of pristine
and bulk elementary metals and intermetallics in the liquid and solid phases in
addition to slab and amorphous configurations. The DP model was validated by
comparing bulk properties such as lattice constants, elastic constants, bulk
moduli, phonon spectra, surface energies to DFT values for identical
structures. Further, we contrast the DP results with values obtained using
well-established two embedded atom method potentials. Overall, our DP potential
provides near DFT accuracy for the different Cu-Zr phases but with a fraction
of its computational cost, thus enabling accurate computations of realistic
atomistic models especially for the amorphous phase.
|
[{'version': 'v1', 'created': 'Mon, 17 Feb 2020 04:15:31 GMT'}]
|
2020-04-29
|
Chongze Hu, Yunxing Zuo, Chi Chen, Shyue Ping Ong, Jian Luo
|
Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams:
Addressing the Challenge of Five Degrees of Freedom
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Grain boundaries (GBs) often control the processing and properties of
polycrystalline materials. Here, a potentially transformative research is
represented by constructing GB property diagrams as functions of temperature
and bulk composition, also called "complexion diagrams," as a general materials
science tool on par with phase diagrams. However, a GB has five macroscopic
(crystallographic) degrees of freedom (DOFs). It is essentially a "mission
impossible" to construct property diagrams for GBs as a function of five DOFs
by either experiments or modeling. Herein, we combine isobaric
semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid
MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN)
models to tackle this grand challenge. The DNN prediction is ~108 faster than
atomistic simulations, thereby enabling the construction of the property
diagrams for millions of distinctly different GBs of five DOFs. Notably,
excellent prediction accuracies have been achieved for not only symmetric-tilt
and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter
are more complex and much less understood, but they are ubiquitous and often
limit the performance properties of real polycrystals as the weak links. The
data-driven prediction of GB properties as function of temperature, bulk
composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new
paradigm.
|
[{'version': 'v1', 'created': 'Tue, 25 Feb 2020 02:43:36 GMT'}]
|
2020-02-26
|
Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting
Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James
Wang, Dane Morgan
|
Assessing Graph-based Deep Learning Models for Predicting Flash Point
|
Mol. Inf. 2020, 39, 1900101
|
10.1002/minf.201900101
| null |
physics.comp-ph cond-mat.mtrl-sci cs.LG
|
Flash points of organic molecules play an important role in preventing
flammability hazards and large databases of measured values exist, although
millions of compounds remain unmeasured. To rapidly extend existing data to new
compounds many researchers have used quantitative structure-property
relationship (QSPR) analysis to effectively predict flash points. In recent
years graph-based deep learning (GBDL) has emerged as a powerful alternative
method to traditional QSPR. In this paper, GBDL models were implemented in
predicting flash point for the first time. We assessed the performance of two
GBDL models, message-passing neural network (MPNN) and graph convolutional
neural network (GCNN), by comparing methods. Our result shows that MPNN both
outperforms GCNN and yields slightly worse but comparable performance with
previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of
MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable
studies. To further explore GBDL models, we collected the largest flash point
dataset to date, which contains 10575 unique molecules. The optimized MPNN
gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We
also extracted 5 datasets from our integrated dataset based on molecular types
(acids, organometallics, organogermaniums, organosilicons, and organotins) and
explore the quality of the model in these classes.against 12 previous QSPR
studies using more traditional
|
[{'version': 'v1', 'created': 'Wed, 26 Feb 2020 06:10:12 GMT'}]
|
2020-02-28
|
Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt, Aaron Kusne, Eva
Campo and Martin Green
|
Scientific AI in materials science: a path to a sustainable and scalable
paradigm
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Recently there has been an ever-increasing trend in the use of machine
learning (ML) and artificial intelligence (AI) methods by the materials
science, condensed matter physics, and chemistry communities. This perspective
article identifies key scientific, technical, and social opportunities that the
materials community must prioritize to consistently develop and leverage
Scientific AI to provide a credible path towards the advancement of current
materials-limited technologies. Here we highlight the intersections of these
opportunities with a series of proposed paths forward. The opportunities are
roughly sorted from scientific/technical (e.g., development of robust,
physically meaningful multiscale material representations) to social (e.g.,
promoting an AI-ready workforce). The proposed paths forward range from
developing new infrastructure and capabilities to deploying them in industry
and academia. We provide a brief introduction to AI in materials science and
engineering, followed by detailed discussions of each of the opportunities and
paths forward.
|
[{'version': 'v1', 'created': 'Wed, 18 Mar 2020 20:59:05 GMT'}]
|
2020-03-20
|
Chunyang Wang, Guanglei Ding, Yitong Liu, Huolin L. Xin
|
0.71-{\AA} resolution electron tomography enabled by deep learning aided
information recovery
| null | null | null |
cond-mat.mtrl-sci eess.IV physics.app-ph physics.ins-det
|
Electron tomography, as an important 3D imaging method, offers a powerful
method to probe the 3D structure of materials from the nano- to the
atomic-scale. However, as a grant challenge, radiation intolerance of the
nanoscale samples and the missing-wedge-induced information loss and artifacts
greatly hindered us from obtaining 3D atomic structures with high fidelity.
Here, for the first time, by combining generative adversarial models with
state-of-the-art network architectures, we demonstrate the resolution of
electron tomography can be improved to 0.71 angstrom which is the highest
three-dimensional imaging resolution that has been reported thus far. We also
show it is possible to recover the lost information and remove artifacts in the
reconstructed tomograms by only acquiring data from -50 to +50 degrees (44%
reduction of dosage compared to -90 to +90 degrees full tilt series). In
contrast to conventional methods, the deep learning model shows outstanding
performance for both macroscopic objects and atomic features solving the
long-standing dosage and missing-wedge problems in electron tomography. Our
work provides important guidance for the application of machine learning
methods to tomographic imaging and sheds light on its applications in other 3D
imaging techniques.
|
[{'version': 'v1', 'created': 'Fri, 27 Mar 2020 07:16:30 GMT'}]
|
2020-03-30
|
Haotong Liang, Valentin Stanev, A. Gilad Kusne, Ichiro Takeuchi
|
CRYSPNet: Crystal Structure Predictions via Neural Network
|
Phys. Rev. Materials 4, 123802 (2020)
|
10.1103/PhysRevMaterials.4.123802
| null |
cond-mat.mtrl-sci stat.ML
|
Structure is the most basic and important property of crystalline solids; it
determines directly or indirectly most materials characteristics. However,
predicting crystal structure of solids remains a formidable and not fully
solved problem. Standard theoretical tools for this task are computationally
expensive and at times inaccurate. Here we present an alternative approach
utilizing machine learning for crystal structure prediction. We developed a
tool called Crystal Structure Prediction Network (CRYSPNet) that can predict
the Bravais lattice, space group, and lattice parameters of an inorganic
material based only on its chemical composition. CRYSPNet consists of a series
of neural network models, using as inputs predictors aggregating the properties
of the elements constituting the compound. It was trained and validated on more
than 100,000 entries from the Inorganic Crystal Structure Database. The tool
demonstrates robust predictive capability and outperforms alternative
strategies by a large margin. Made available to the public (at
https://github.com/AuroraLHT/cryspnet), it can be used both as an independent
prediction engine or as a method to generate candidate structures for further
computational and/or experimental validation.
|
[{'version': 'v1', 'created': 'Tue, 31 Mar 2020 16:05:18 GMT'}]
|
2021-01-04
|
Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen, Joseph B. Choi, H.S.
Udaykumar, Stephen S. Baek
|
Deep learning for synthetic microstructure generation in a
materials-by-design framework for heterogeneous energetic materials
| null |
10.1038/s41598-020-70149-0
| null |
cond-mat.mtrl-sci cs.LG
|
The sensitivity of heterogeneous energetic (HE) materials (propellants,
explosives, and pyrotechnics) is critically dependent on their microstructure.
Initiation of chemical reactions occurs at hot spots due to energy localization
at sites of porosities and other defects. Emerging multi-scale predictive
models of HE response to loads account for the physics at the meso-scale, i.e.
at the scale of statistically representative clusters of particles and other
features in the microstructure. Meso-scale physics is infused in
machine-learned closure models informed by resolved meso-scale simulations.
Since microstructures are stochastic, ensembles of meso-scale simulations are
required to quantify hot spot ignition and growth and to develop models for
microstructure-dependent energy deposition rates. We propose utilizing
generative adversarial networks (GAN) to spawn ensembles of synthetic
heterogeneous energetic material microstructures. The method generates
qualitatively and quantitatively realistic microstructures by learning from
images of HE microstructures. We show that the proposed GAN method also permits
the generation of new morphologies, where the porosity distribution can be
controlled and spatially manipulated. Such control paves the way for the design
of novel microstructures to engineer HE materials for targeted performance in a
materials-by-design framework.
|
[{'version': 'v1', 'created': 'Sun, 5 Apr 2020 16:58:31 GMT'}]
|
2023-05-12
|
Arash Rabbani, Masoud Babaei, Reza Shams, Ying Da Wang, Traiwit Chung
|
DeePore: a deep learning workflow for rapid and comprehensive
characterization of porous materials
|
Advances in Water Resources, 2020, 103787
|
10.1016/j.advwatres.2020.103787
| null |
cond-mat.mtrl-sci cs.LG
|
DeePore is a deep learning workflow for rapid estimation of a wide range of
porous material properties based on the binarized micro-tomography images. By
combining naturally occurring porous textures we generated 17700 semi-real 3-D
micro-structures of porous geo-materials with size of 256^3 voxels and 30
physical properties of each sample are calculated using physical simulations on
the corresponding pore network models. Next, a designed feed-forward
convolutional neural network (CNN) is trained based on the dataset to estimate
several morphological, hydraulic, electrical, and mechanical characteristics of
the porous material in a fraction of a second. In order to fine-tune the CNN
design, we tested 9 different training scenarios and selected the one with the
highest average coefficient of determination (R^2) equal to 0.885 for 1418
testing samples. Additionally, 3 independent synthetic images as well as 3
realistic tomography images have been tested using the proposed method and
results are compared with pore network modelling and experimental data,
respectively. Tested absolute permeabilities had around 13 % relative error
compared to the experimental data which is noticeable considering the accuracy
of the direct numerical simulation methods such as Lattice Boltzmann and Finite
Volume. The workflow is compatible with any physical size of the images due to
its dimensionless approach and can be used to characterize large-scale 3-D
images by averaging the model outputs for a sliding window that scans the whole
geometry.
|
[{'version': 'v1', 'created': 'Sun, 3 May 2020 08:46:09 GMT'}, {'version': 'v2', 'created': 'Sat, 10 Oct 2020 09:06:32 GMT'}]
|
2020-10-13
|
Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Joerg Behler,
Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David
Lacroix, Davide Donadio
|
Transferability of neural network potentials for varying stoichiometry:
phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds
|
Journal of Applied Physics 127, 244901 (2020)
|
10.1063/5.0009550
| null |
cond-mat.mtrl-sci
|
Germanium manganese compounds exhibit a variety of stable and metastable
phases with different stoichiometry. These materials entail interesting
electronic, magnetic and thermal properties both in their bulk form and as
heterostructures. Here we develop and validate a transferable machine learning
potential, based on the high-dimensional neural network formalism, to enable
the study of Mn$_x$Ge$_y$ materials over a wide range of compositions. We show
that a neural network potential fitted on a minimal training set reproduces
successfully the structural and vibrational properties and the thermal
conductivity of systems with different local chemical environments, and it can
be used to predict phononic effects in nanoscale heterostructures.
|
[{'version': 'v1', 'created': 'Tue, 19 May 2020 17:15:00 GMT'}]
|
2020-07-02
|
George S. Baggs, Paul Guerrier, Andrew Loeb, Jason C. Jones
|
Automated Copper Alloy Grain Size Evaluation Using a Deep-learning CNN
| null | null | null |
cs.CV cond-mat.mtrl-sci cs.LG stat.ML
|
Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using
a deep-learning convolutional neural network (CNN). The proof-of-concept
automated image acquisition and batch-wise image processing offers the
potential for significantly reduced labor, improved accuracy of grain
evaluation, and decreased overall turnaround times for approving Cu alloy bar
stock for use in flight critical aircraft hardware. A classification accuracy
of 91.1% on individual sub-images of the Cu alloy coupons was achieved. Process
development included minimizing the variation in acquired image color,
brightness, and resolution to create a dataset with 12300 sub-images, and then
optimizing the CNN hyperparameters on this dataset using statistical design of
experiments (DoE).
Over the development of the automated Cu alloy grain size evaluation, a
degree of "explainability" in the artificial intelligence (XAI) output was
realized, based on the decomposition of the large raw images into many smaller
dataset sub-images, through the ability to explain the CNN ensemble image
output via inspection of the classification results from the individual smaller
sub-images.
|
[{'version': 'v1', 'created': 'Wed, 20 May 2020 13:13:38 GMT'}]
|
2020-05-21
|
Samad Hajinazar, Aidan Thorn, Ernesto D. Sandoval, Saba Kharabadze,
Aleksey N. Kolmogorov
|
MAISE: Construction of neural network interatomic models and
evolutionary structure optimization
| null |
10.1016/j.cpc.2020.107679
| null |
physics.comp-ph cond-mat.mtrl-sci
|
Module for ab initio structure evolution (MAISE) is an open-source package
for materials modeling and prediction. The code's main feature is an automated
generation of neural network (NN) interatomic potentials for use in global
structure searches. The systematic construction of Behler-Parrinello-type NN
models approximating ab initio energy and forces relies on two approaches
introduced in our recent studies. An evolutionary sampling scheme for
generating reference structures improves the NNs' mapping of regions visited in
unconstrained searches, while a stratified training approach enables the
creation of standardized NN models for multiple elements. A more flexible NN
architecture proposed here expands the applicability of the stratified scheme
for an arbitrary number of elements. The full workflow in the NN development is
managed with a customizable 'MAISE-NET' wrapper written in Python. The global
structure optimization capability in MAISE is based on an evolutionary
algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe
extension of the algorithm allows for an efficient simultaneous optimization of
nanoparticles in a given size range. Implemented structure analysis functions
include fingerprinting with radial distribution functions and finding space
groups with the SPGLIB tool. This work overviews MAISE's available features,
constructed models, and confirmed predictions.
|
[{'version': 'v1', 'created': 'Mon, 25 May 2020 14:15:39 GMT'}, {'version': 'v2', 'created': 'Tue, 15 Sep 2020 02:25:40 GMT'}]
|
2020-10-26
|
Tarak K Patra, Troy D. Loeffler and Subramanian K R S Sankaranarayanan
|
Accelerating Copolymer Inverse Design using AI Gaming algorithm
| null | null | null |
cond-mat.soft cond-mat.mes-hall cond-mat.mtrl-sci
|
There exists a broad class of sequencing problems, for example, in proteins
and polymers that can be formulated as a heuristic search algorithm that
involve decision making akin to a computer game. AI gaming algorithms such as
Monte Carlo tree search (MCTS) gained prominence after their exemplary
performance in the computer Go game and are decision trees aimed at identifying
the path (moves) that should be taken by the policy to reach the final winning
or optimal solution. Major challenges in inverse sequencing problems are that
the materials search space is extremely vast and property evaluation for each
sequence is computationally demanding. Reaching an optimal solution by
minimizing the total number of evaluations in a given design cycle is therefore
highly desirable. We demonstrate that one can adopt this approach for solving
the sequencing problem by developing and growing a decision tree, where each
node in the tree is a candidate sequence whose fitness is directly evaluated by
molecular simulations. We interface MCTS with MD simulations and use a
representative example of designing a copolymer compatibilizer, where the goal
is to identify sequence specific copolymers that lead to zero interfacial
energy between two immiscible homopolymers. We apply the MCTS algorithm to
polymer chain lengths varying from 10-mer to 30-mer, wherein the overall search
space varies from 210 (1024) to 230 (~1 billion). In each case, we identify a
target sequence that leads to zero interfacial energy within a few hundred
evaluations demonstrating the scalability and efficiency of MCTS in exploring
practical materials design problems with exceedingly vast chemical/material
search space. Our MCTS-MD framework can be easily extended to several other
polymer and protein inverse design problems, in particular, for cases where
sequence-property data is either unavailable and/or is resource intensive.
|
[{'version': 'v1', 'created': 'Mon, 1 Jun 2020 21:27:55 GMT'}]
|
2020-06-08
|
Troy D Loeffler, Sukriti Manna, Tarak K Patra, Henry Chan, Badri
Narayanan, and Subramanian Sankaranarayanan
|
Active Learning A Neural Network Model For Gold Clusters \& Bulk From
Sparse First Principles Training Data
| null |
10.1002/cctc.202000774
| null |
physics.comp-ph cond-mat.mtrl-sci
|
Small metal clusters are of fundamental scientific interest and of tremendous
significance in catalysis. These nanoscale clusters display diverse geometries
and structural motifs depending on the cluster size; a knowledge of this
size-dependent structural motifs and their dynamical evolution has been of
longstanding interest. Classical MD typically employ predefined functional
forms which limits their ability to capture such complex size-dependent
structural and dynamical transformation. Neural Network (NN) based potentials
represent flexible alternatives and in principle, well-trained NN potentials
can provide high level of flexibility, transferability and accuracy on-par with
the reference model used for training. A major challenge, however, is that NN
models are interpolative and requires large quantities of training data to
ensure that the model adequately samples the energy landscape both near and
far-from-equilibrium. Here, we introduce an active learning (AL) scheme that
trains a NN model on-the-fly with minimal amount of first-principles based
training data. Our AL workflow is initiated with a sparse training dataset (1
to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo
scheme that iteratively queries the energy landscape in regions of failure and
updates the training pool to improve the network performance. Using a
representative system of gold clusters, we demonstrate that our AL workflow can
train a NN with ~500 total reference calculations. Our NN predictions are
within 30 meV/atom and 40 meV/\AA of the reference DFT calculations. Moreover,
our AL-NN model also adequately captures the various size-dependent structural
and dynamical properties of gold clusters in excellent agreement with DFT
calculations and available experiments.
|
[{'version': 'v1', 'created': 'Fri, 5 Jun 2020 20:44:30 GMT'}]
|
2020-07-21
|
Henry Chan, Youssef S.G. Nashed, Saugat Kandel, Stephan Hruszkewycz,
Subramanian Sankaranarayanan, Ross J. Harder, Mathew J. Cherukara
|
Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning
| null |
10.1063/5.0031486
| null |
eess.IV cond-mat.mtrl-sci cs.LG physics.app-ph
|
Phase retrieval, the problem of recovering lost phase information from
measured intensity alone, is an inverse problem that is widely faced in various
imaging modalities ranging from astronomy to nanoscale imaging. The current
process of phase recovery is iterative in nature. As a result, the image
formation is time-consuming and computationally expensive, precluding real-time
imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to
develop a deep learning model to address this phase retrieval problem. We
introduce 3D-CDI-NN, a deep convolutional neural network and differential
programming framework trained to predict 3D structure and strain solely from
input 3D X-ray coherent scattering data. Our networks are designed to be
"physics-aware" in multiple aspects; in that the physics of x-ray scattering
process is explicitly enforced in the training of the network, and the training
data are drawn from atomistic simulations that are representative of the
physics of the material. We further refine the neural network prediction
through a physics-based optimization procedure to enable maximum accuracy at
lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction
pattern to real-space structure and strain hundreds of times faster than
traditional iterative phase retrieval methods, with negligible loss in
accuracy. Our integrated machine learning and differential programming solution
to the phase retrieval problem is broadly applicable across inverse problems in
other application areas.
|
[{'version': 'v1', 'created': 'Tue, 16 Jun 2020 18:35:32 GMT'}]
|
2024-06-12
|
Marco Eckhoff, Florian Sch\"onewald, Marcel Risch, Cynthia A. Volkert,
Peter E. Bl\"ochl, J\"org Behler
|
Closing the gap between theory and experiment for lithium manganese
oxide spinels using a high-dimensional neural network potential
|
Phys. Rev. B 102, 174102 (2020)
|
10.1103/PhysRevB.102.174102
| null |
cond-mat.mtrl-sci
|
Many positive electrode materials in lithium ion batteries include transition
metals which are difficult to describe by electronic structure methods like
density functional theory (DFT) due to the presence of multiple oxidation
states. A prominent example is the lithium manganese oxide spinel
Li$_x$Mn$_2$O$_4$ with $0\leq x\leq2$. While DFT, employing the local hybrid
functional PBE0r, provides a reliable description, the need for extended
computer simulations of large structural models remains a significant
challenge. Here, we close this gap by constructing a DFT-based high-dimensional
neural network potential (HDNNP) providing accurate energies and forces at a
fraction of the computational costs. As different oxidation states and the
resulting Jahn-Teller distortions represent a new level of complexity for
HDNNPs, the potential is carefully validated by performing X-ray diffraction
experiments. We demonstrate that the HDNNP provides atomic level details and is
able to predict a series of properties like the lattice parameters and
expansion with increasing Li content or temperature, the orthorhombic to cubic
transition, the lithium diffusion barrier, and the phonon frequencies. We show
that for understanding these properties access to large time and length scales
as enabled by the HDNNP is essential to close the gap between theory and
experiment.
|
[{'version': 'v1', 'created': 'Wed, 1 Jul 2020 08:44:44 GMT'}, {'version': 'v2', 'created': 'Fri, 2 Oct 2020 16:40:30 GMT'}]
|
2020-11-11
|
Haotian Feng and Pavana Prabhakar
|
Difference-Based Deep Learning Framework for Stress Predictions in
Heterogeneous Media
| null |
10.1016/j.compstruct.2021.113957
| null |
physics.app-ph cond-mat.mtrl-sci cs.LG
|
Stress analysis of heterogeneous media, like composite materials, using
Finite Element Analysis (FEA) has become commonplace in design and analysis.
However, determining stress distributions in heterogeneous media using FEA can
be computationally expensive in situations like optimization and multi-scaling.
To address this, we utilize Deep Learning for developing a set of novel
Difference-based Neural Network (DiNN) frameworks based on engineering and
statistics knowledge to determine stress distribution in heterogeneous media,
for the first time, with special focus on discontinuous domains that manifest
high stress concentrations. The novelty of our approach is that instead of
directly using several FEA model geometries and stresses as inputs for training
a Neural Network, as typically done previously, we focus on highlighting the
differences in stress distribution between different input samples for
improving the accuracy of prediction in heterogeneous media. We evaluate the
performance of DiNN frameworks by considering different types of geometric
models that are commonly used in the analysis of composite materials, including
volume fraction and spatial randomness. Results show that the DiNN structures
significantly enhance the accuracy of stress prediction compared to existing
structures, especially for composite models with random volume fraction when
localized high stress concentrations are present.
|
[{'version': 'v1', 'created': 'Wed, 1 Jul 2020 00:18:14 GMT'}, {'version': 'v2', 'created': 'Wed, 15 Jul 2020 03:30:14 GMT'}, {'version': 'v3', 'created': 'Mon, 29 Mar 2021 12:01:43 GMT'}]
|
2021-04-22
|
Mart\'in Leandro Paleico, J\"org Behler
|
Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a
DFT-based Neural Network Potential and Genetic Algorithms
| null | null | null |
physics.chem-ph cond-mat.mtrl-sci physics.comp-ph
|
The determination of the most stable structures of metal clusters supported
at solid surfaces by computer simulations represents a formidable challenge due
to the complexity of the potential-energy surface. Here we combine a
high-dimensional neural network potential, which allows to predict the energies
and forces of a large number of structures with first-principles accuracy, with
a global optimization scheme employing genetic algorithms. This very efficient
setup is used to identify the global minima and low-energy local minima for a
series of copper clusters containing between four and ten atoms adsorbed at the
ZnO(10$\bar{1}$0) surface. A series of structures with common structural
features resembling the Cu(111) and Cu(110) surfaces at the metal-oxide
interface has been identified, and the geometries of the emerging clusters are
characterized in detail. We demonstrate that the frequently employed
approximation of a frozen substrate surface in global optimization can result
in missing the most relevant structures.
|
[{'version': 'v1', 'created': 'Mon, 13 Jul 2020 15:50:20 GMT'}]
|
2020-07-14
|
Ryan Cohn (1) and Elizabeth Holm (1) ((1) Department of Materials
Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA)
|
Unsupervised machine learning via transfer learning and k-means
clustering to classify materials image data
| null |
10.1007/s40192-021-00205-8
| null |
cond-mat.mtrl-sci cs.LG eess.IV
|
Unsupervised machine learning offers significant opportunities for extracting
knowledge from unlabeled data sets and for achieving maximum machine learning
performance. This paper demonstrates how to construct, use, and evaluate a high
performance unsupervised machine learning system for classifying images in a
popular microstructural dataset. The Northeastern University Steel Surface
Defects Database includes micrographs of six different defects observed on
hot-rolled steel in a format that is convenient for training and evaluating
models for image classification. We use the VGG16 convolutional neural network
pre-trained on the ImageNet dataset of natural images to extract feature
representations for each micrograph. After applying principal component
analysis to extract signal from the feature descriptors, we use k-means
clustering to classify the images without needing labeled training data. The
approach achieves $99.4\% \pm 0.16\%$ accuracy, and the resulting model can be
used to classify new images without retraining This approach demonstrates an
improvement in both performance and utility compared to a previous study. A
sensitivity analysis is conducted to better understand the influence of each
step on the classification performance. The results provide insight toward
applying unsupervised machine learning techniques to problems of interest in
materials science.
|
[{'version': 'v1', 'created': 'Thu, 16 Jul 2020 14:36:04 GMT'}]
|
2021-04-13
|
Joohwi Lee and Ryoji Asahi
|
Transfer learning for materials informatics using crystal graph
convolutional neural network
| null |
10.1016/j.commatsci.2021.110314
| null |
cond-mat.mtrl-sci physics.comp-ph
|
For successful applications of machine learning in materials informatics, it
is necessary to overcome the inaccuracy of predictions ascribed to insufficient
amount of data. In this study, we propose a transfer learning using a crystal
graph convolutional neural network (TL-CGCNN). Herein, TL-CGCNN is pretrained
with big data such as formation energies for crystal structures, and then used
for predicting target properties with relatively small data. We confirm that
TL-CGCNN can improve predictions of various properties such as bulk moduli,
dielectric constants, and quasiparticle band gaps, which are computationally
demanding, to construct big data for materials. Moreover, we quantitatively
observe that the prediction of properties in target models via TL-CGCNN becomes
more accurate with an increase in size of training dataset in pretrained
models. Finally, we confirm that TL-CGCNN is superior to other regression
methods in the predictions of target properties, which suffer from small amount
of data. Therefore, we conclude that TL-CGCNN is promising along with compiling
big data for materials that are easy to accumulate and relevant to the target
properties.
|
[{'version': 'v1', 'created': 'Mon, 20 Jul 2020 08:27:57 GMT'}, {'version': 'v2', 'created': 'Tue, 18 Aug 2020 01:51:07 GMT'}, {'version': 'v3', 'created': 'Tue, 10 Nov 2020 02:38:18 GMT'}, {'version': 'v4', 'created': 'Fri, 29 Jan 2021 08:05:58 GMT'}]
|
2021-02-01
|
Zachary D. McClure and Alejandro H. Strachan
|
Expanding materials selection via transfer learning for high-temperature
oxide selection
| null | null | null |
cond-mat.mtrl-sci
|
Materials with higher operating temperatures than today's state of the art
can improve system performance in several applications and enable new
technologies. Under most scenarios, a protective oxide scale with high melting
temperatures and thermodynamic stability as well as low ionic diffusivity is
required. Thus, the design of high-temperature systems would benefit from
knowledge of these properties and related ones for all known oxides. While some
properties of interest are known for many oxides (e.g. elastic constants exist
for over 1,000 oxides), melting temperature is known for a relatively small
subset. The determination of melting temperatures is time consuming and costly,
both experimentally and computationally, thus we use data science tools to
develop predictive models from the existing data. The relatively small number
of available melting temperature values precludes the use of standard tools;
therefore, we use a multi-step approach based on sequential learning where
surrogate data from first-principles calculations is leveraged to develop
models using small datasets. We use these models to predict the desired
properties for nearly 11,000 oxides and quantify uncertainties in the space.
|
[{'version': 'v1', 'created': 'Mon, 20 Jul 2020 16:45:59 GMT'}]
|
2020-07-27
|
Shreshth A. Malik, Rhys E. A. Goodall, Alpha A. Lee
|
Materials Graph Transformer predicts the outcomes of inorganic reactions
with reliable uncertainties
|
Chemistry of Materials 2021 33 (2), 616-624
|
10.1021/acs.chemmater.0c03885
| null |
physics.comp-ph cond-mat.mtrl-sci
|
A common bottleneck for materials discovery is synthesis. While recent
methodological advances have resulted in major improvements in the ability to
predicatively design novel materials, researchers often still rely on
trial-and-error approaches for determining synthesis procedures. In this work,
we develop a model that predicts the major product of solid-state reactions.
The cardinal feature of this approach is the construction of fixed-length,
learned representations of reactions. Precursors are represented as nodes on a
`reaction graph', and message-passing operations between nodes are used to
embody the interactions between precursors in the reaction mixture. Through an
ablation study, it is shown that this framework not only outperforms less
physically-motivated baseline methods but also more reliably assesses the
uncertainty in its predictions.
|
[{'version': 'v1', 'created': 'Thu, 30 Jul 2020 21:39:58 GMT'}, {'version': 'v2', 'created': 'Thu, 3 Sep 2020 17:08:03 GMT'}]
|
2021-01-27
|
Anran Wei, Jie Xiong, Weidong Yang, Fenglin Guo
|
Identifying the elastic isotropy of architectured materials based on
deep learning method
| null |
10.1016/j.eml.2021.101173
| null |
physics.app-ph cond-mat.dis-nn cond-mat.mtrl-sci
|
With the achievement on the additive manufacturing, the mechanical properties
of architectured materials can be precisely designed by tailoring
microstructures. As one of the primary design objectives, the elastic isotropy
is of great significance for many engineering applications. However, the
prevailing experimental and numerical methods are normally too costly and
time-consuming to determine the elastic isotropy of architectured materials
with tens of thousands of possible microstructures in design space. The quick
mechanical characterization is thus desired for the advanced design of
architectured materials. Here, a deep learning-based approach is developed as a
portable and efficient tool to identify the elastic isotropy of architectured
materials directly from the images of their representative microstructures with
arbitrary component distributions. The measure of elastic isotropy for
architectured materials is derived firstly in this paper to construct a
database with associated images of microstructures. Then a convolutional neural
network is trained with the database. It is found that the convolutional neural
network shows good performance on the isotropy identification. Meanwhile, it
exhibits enough robustness to maintain the performance under fluctuated
material properties in practical fabrications. Moreover, the well-trained
convolutional neural network can be successfully transferred among different
types of architectured materials, including two-phase composites and porous
materials, which greatly enhance the efficiency of the deep learning-based
approach. This study can give new inspirations on the fast mechanical
characterization for the big-data driven design of architectured materials.
|
[{'version': 'v1', 'created': 'Sun, 2 Aug 2020 10:16:28 GMT'}, {'version': 'v2', 'created': 'Sun, 9 Aug 2020 03:15:22 GMT'}]
|
2021-04-15
|
Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi, Ushio
Matsumoto, Akihide Kuwabara, and Takafumi Ogawa
|
Facilitating {\it ab initio} configurational sampling of multicomponent
solids using an on-lattice neural network model and active learning
| null |
10.1063/5.0096645
| null |
physics.comp-ph cond-mat.mtrl-sci
|
We propose a scheme for {\it ab initio} configurational sampling in
multicomponent crystalline solids using Behler-Parinello type neural network
potentials (NNPs) in an unconventional way: the NNPs are trained to predict the
energies of relaxed structures from the perfect lattice with configurational
disorder instead of the usual way of training to predict energies as functions
of continuous atom coordinates. An active learning scheme is employed to obtain
a training set containing configurations of thermodynamic relevance. This
enables bypassing of the structural relaxation procedure which is necessary
when applying conventional NNP approaches to the lattice configuration problem.
The idea is demonstrated on the calculation of the temperature dependence of
the degree of A/B site inversion in three spinel oxides, MgAl$_2$O$_4$,
ZnAl$_2$O$_4$, and MgGa$_2$O$_4$. The present scheme may serve as an
alternative to cluster expansion for `difficult' systems, e.g., complex bulk or
interface systems with many components and sublattices that are relevant to
many technological applications today.
|
[{'version': 'v1', 'created': 'Thu, 6 Aug 2020 11:07:32 GMT'}, {'version': 'v2', 'created': 'Wed, 20 Apr 2022 12:33:53 GMT'}]
|
2024-06-19
|
Koji Shimizu, Elvis F. Arguelles, Wenwen Li, Yasunobu Ando, Emi
Minamitani, and Satoshi Watanabe
|
Phase stability of Au-Li binary systems studied using neural network
potential
|
Phys. Rev. B 103, 094112 (2021)
|
10.1103/PhysRevB.103.094112
| null |
cond-mat.mtrl-sci
|
The miscibility of Au and Li exhibits a potential application as an adhesion
layer and electrode material in secondary batteries. Here, to explore alloying
properties, we constructed a neural network potential (NNP) of Au-Li binary
systems based on density functional theory (DFT) calculations. To accelerate
construction of NNPs, we proposed an efficient and inexpensive method of
structural dataset generation. The predictions by the constructed NNP on
lattice parameters and phonon properties agree well with those obtained by DFT
calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with
fine composition grids, showing excellent agreement with DFT verifications. We
found the existence of various compositions with structures on and slightly
above the convex hull, which can explain the lack of consensus on the Au-Li
stable phases in previous studies. Moreover, we newly found
Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported
elsewhere. Finally, we examined the alloying process starting from the phase
separated structure to the complete mixing phase. We found that when multiple
adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface
started from the dissolved region. This paper demonstrates the applicability of
NNPs toward miscible phases and provides the understanding of the alloying
mechanism.
|
[{'version': 'v1', 'created': 'Wed, 12 Aug 2020 03:57:09 GMT'}]
|
2021-03-31
|
Mohammadreza Karamad, Rishikesh Magar, Yuting Shi, Samira Siahrostami,
Ian D. Gates and Amir Barati Farimani
|
Orbital Graph Convolutional Neural Network for Material Property
Prediction
|
Phys. Rev. Materials 4, 093801 (2020)
|
10.1103/PhysRevMaterials.4.093801
| null |
physics.comp-ph cond-mat.mtrl-sci cs.LG
|
Material representations that are compatible with machine learning models
play a key role in developing models that exhibit high accuracy for property
prediction. Atomic orbital interactions are one of the important factors that
govern the properties of crystalline materials, from which the local chemical
environments of atoms is inferred. Therefore, to develop robust machine
learningmodels for material properties prediction, it is imperative to include
features representing such chemical attributes. Here, we propose the Orbital
Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional
neural network framework that includes atomic orbital interaction features that
learns material properties in a robust way. In addition, we embedded an
encoder-decoder network into the OGCNN enabling it to learn important features
among basic atomic (elemental features), orbital-orbital interactions, and
topological features. We examined the performance of this model on a broad
range of crystalline material data to predict different properties. We
benchmarked the performance of the OGCNN model with that of: 1) the crystal
graph convolutional neural network (CGCNN), 2) other state-of-the-art
descriptors for material representations including Many-body Tensor
Representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), and 3)
other conventional regression machine learning algorithms where different
crystal featurization methods have been used. We find that OGCNN significantly
outperforms them. The OGCNN model with high predictive accuracy can be used to
discover new materials among the immense phase and compound spaces of materials
|
[{'version': 'v1', 'created': 'Fri, 14 Aug 2020 15:22:22 GMT'}]
|
2020-10-06
|
Kaiqi Yang, Yifan Cao, Youtian Zhang, Ming Tang, Daniel Aberg, Babak
Sadigh, Fei Zhou
|
Self-Supervised Learning and Prediction of Microstructure Evolution with
Recurrent Neural Networks
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Microstructural evolution is a key aspect of understanding and exploiting the
structure-property-performance relation of materials. Modeling microstructure
evolution usually relies on coarse-grained simulations with evolution
principles described by partial differential equations (PDEs). Here we
demonstrate that convolutional recurrent neural networks can learn the
underlying physical rules and replace PDE-based simulations in the prediction
of microstructure phenomena. Neural nets are trained by self-supervised
learning with image sequences from simulations of several common processes,
including plane wave propagation, grain growth, spinodal decomposition and
dendritic crystal growth. The trained networks can accurately predict both
short-term local dynamics and long-term statistical properties of
microstructures and is capable of extrapolating beyond the training datasets in
spatiotemporal domains and configurational and parametric spaces. Such a
data-driven approach offers significant advantages over PDE-based simulations
in time stepping efficiency and offers a useful alternative especially when the
material parameters or governing PDEs are not well determined.
|
[{'version': 'v1', 'created': 'Mon, 17 Aug 2020 22:46:12 GMT'}, {'version': 'v2', 'created': 'Sun, 30 Aug 2020 01:09:41 GMT'}]
|
2020-09-01
|
Rongzhi Dong, Yabo Dan, Xiang Li, Jianjun Hu
|
Inverse Design of Composite Metal Oxide Optical Materials based on Deep
Transfer Learning
|
Computational Materials Science (2020): 110166
|
10.1016/j.commatsci.2020.110166
| null |
cond-mat.mtrl-sci
|
Optical materials with special optical properties are widely used in a broad
span of technologies, from computer displays to solar energy utilization
leading to large dataset accumulated from years of extensive materials
synthesis and optical characterization. Previously, machine learning models
have been developed to predict the optical absorption spectrum from a materials
characterization image or vice versa. Herein we propose TLOpt, a transfer
learning based inverse optical materials design algorithm for suggesting
material compositions with a desired target light absorption spectrum. Our
approach is based on the combination of a deep neural network model and global
optimization algorithms including a genetic algorithm and Bayesian
optimization. A transfer learning strategy is employed to solve the small
dataset issue in training the neural network predictor of optical absorption
spectrum using the Magpie materials composition descriptor. Our extensive
experiments show that our algorithm can inverse design the materials
composition with stoichiometry with high accuracy.
|
[{'version': 'v1', 'created': 'Mon, 24 Aug 2020 18:00:14 GMT'}]
|
2020-11-26
|
Juhyeok Lee and Chaehwa Jeong and Yongsoo Yang
|
Single-atom level determination of 3-dimensional surface atomic
structure via neural network-assisted atomic electron tomography
|
Nature Communications 12, 1962 (2021)
|
10.1038/s41467-021-22204-1
| null |
cond-mat.mtrl-sci
|
Functional properties of nanomaterials strongly depend on their surface
atomic structure, but they often become largely different from their bulk
structure, exhibiting surface reconstructions and relaxations. However, most of
the surface characterization methods are either limited to 2-dimensional
measurements or not reaching to true 3D atomic-scale resolution, and
single-atom level determination of the 3D surface atomic structure for general
3D nanomaterials still remains elusive. Here we show the measurement of 3D
atomic structure of a Pt nanoparticle at 15 pm precision, aided by a deep
learning-based missing data retrieval. The surface atomic structure was
reliably measured, and we find that <100> and <111> facets contribute
differently to the surface strain, resulting in anisotropic strain distribution
as well as compressive support boundary effect. The capability of single-atom
level surface characterization will not only deepen our understanding of the
functional properties of nanomaterials but also open a new door for fine
tailoring of their performance.
|
[{'version': 'v1', 'created': 'Thu, 27 Aug 2020 10:08:28 GMT'}]
|
2021-10-01
|
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker and J\"org Behler
|
A Fourth-Generation High-Dimensional Neural Network Potential with
Accurate Electrostatics Including Non-local Charge Transfer
| null |
10.1038/s41467-020-20427-2
| null |
cond-mat.mtrl-sci physics.chem-ph physics.comp-ph
|
Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials
science. Most of the established methods, however, rely on local properties and
are thus unable to take global changes in the electronic structure into
account, which result from long-range charge transfer or different charge
states. In this work we overcome this limitation by introducing a
fourth-generation high-dimensional neural network potential that combines a
charge equilibration scheme employing environment-dependent atomic
electronegativities with accurate atomic energies. The method, which is able to
correctly describe global charge distributions in arbitrary systems, yields
much improved energies and substantially extends the applicability of modern
machine learning potentials. This is demonstrated for a series of systems
representing typical scenarios in chemistry and materials science that are
incorrectly described by current methods, while the fourth-generation neural
network potential is in excellent agreement with electronic structure
calculations.
|
[{'version': 'v1', 'created': 'Mon, 14 Sep 2020 14:43:31 GMT'}]
|
2021-03-17
|
G.P. Purja Pun, V. Yamakov, J. Hickman, E. H. Glaessgen, Y. Mishin
|
Development of a general-purpose machine-learning interatomic potential
for aluminum by the physically-informed neural network method
|
Phys. Rev. Materials 4, 113807 (2020)
|
10.1103/PhysRevMaterials.4.113807
| null |
physics.comp-ph cond-mat.mtrl-sci
|
Abstract Interatomic potentials constitute the key component of large-scale
atomistic simulations of materials. The recently proposed physically-informed
neural network (PINN) method combines a high-dimensional regression implemented
by an artificial neural network with a physics-based bond-order interatomic
potential applicable to both metals and nonmetals. In this paper, we present a
modified version of the PINN method that accelerates the potential training
process and further improves the transferability of PINN potentials to unknown
atomic environments. As an application, a modified PINN potential for Al has
been developed by training on a large database of electronic structure
calculations. The potential reproduces the reference first-principles energies
within 2.6 meV per atom and accurately predicts a wide spectrum of physical
properties of Al. Such properties include, but are not limited to, lattice
dynamics, thermal expansion, energies of point and extended defects, the
melting temperature, the structure and dynamic properties of liquid Al, the
surface tensions of the liquid surface and the solid-liquid interface, and the
nucleation and growth of a grain boundary crack. Computational efficiency of
PINN potentials is also discussed.
|
[{'version': 'v1', 'created': 'Mon, 14 Sep 2020 15:59:28 GMT'}, {'version': 'v2', 'created': 'Mon, 26 Oct 2020 17:11:19 GMT'}, {'version': 'v3', 'created': 'Mon, 9 Nov 2020 12:17:48 GMT'}]
|
2020-11-25
|
Jeffrey M. Ede
|
Review: Deep Learning in Electron Microscopy
| null |
10.1088/2632-2153/abd614
| null |
eess.IV cond-mat.mtrl-sci cs.CV cs.LG
|
Deep learning is transforming most areas of science and technology, including
electron microscopy. This review paper offers a practical perspective aimed at
developers with limited familiarity. For context, we review popular
applications of deep learning in electron microscopy. Afterwards, we discuss
hardware and software needed to get started with deep learning and interface
with electron microscopes. We then review neural network components, popular
architectures, and their optimization. Finally, we discuss future directions of
deep learning in electron microscopy.
|
[{'version': 'v1', 'created': 'Thu, 17 Sep 2020 14:23:55 GMT'}, {'version': 'v2', 'created': 'Fri, 18 Sep 2020 14:38:31 GMT'}, {'version': 'v3', 'created': 'Mon, 5 Oct 2020 07:22:38 GMT'}, {'version': 'v4', 'created': 'Tue, 3 Nov 2020 15:38:31 GMT'}, {'version': 'v5', 'created': 'Sun, 27 Dec 2020 22:14:24 GMT'}, {'version': 'v6', 'created': 'Thu, 31 Dec 2020 18:28:54 GMT'}, {'version': 'v7', 'created': 'Mon, 8 Mar 2021 10:12:04 GMT'}]
|
2021-03-09
|
Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder and
Mohammad Nasim Hasan
|
Extraction of Material Properties through Multi-fidelity Deep Learning
from Molecular Dynamics Simulation
| null |
10.1016/j.commatsci.2020.110187
| null |
physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci
|
Simulation of reasonable timescales for any long physical process using
molecular dynamics (MD) is a major challenge in computational physics. In this
study, we have implemented an approach based on multi-fidelity physics informed
neural network (MPINN) to achieve long-range MD simulation results over a large
sample space with significantly less computational cost. The fidelity of our
present multi-fidelity study is based on the integration timestep size of MD
simulations. While MD simulations with larger timestep produce results with
lower level of accuracy, it can provide enough computationally cheap training
data for MPINN to learn an accurate relationship between these low-fidelity
results and high-fidelity MD results obtained using smaller simulation
timestep. We have performed two benchmark studies, involving one and two
component LJ systems, to determine the optimum percentage of high-fidelity
training data required to achieve accurate results with high computational
saving. The results show that important system properties such as system energy
per atom, system pressure and diffusion coefficients can be determined with
high accuracy while saving 68% computational costs. Finally, as a demonstration
of the applicability of our present methodology in practical MD studies, we
have studied the viscosity of argon-copper nanofluid and its variation with
temperature and volume fraction by MD simulation using MPINN. Then we have
compared them with numerous previous studies and theoretical models. Our
results indicate that MPINN can predict accurate nanofluid viscosity at a wide
range of sample space with significantly small number of MD simulations. Our
present methodology is the first implementation of MPINN in conjunction with MD
simulation for predicting nanoscale properties. This can pave pathways to
investigate more complex engineering problems that demand long-range MD
simulations.
|
[{'version': 'v1', 'created': 'Mon, 28 Sep 2020 07:32:24 GMT'}, {'version': 'v2', 'created': 'Tue, 29 Sep 2020 03:14:41 GMT'}, {'version': 'v3', 'created': 'Sat, 14 Nov 2020 22:44:06 GMT'}]
|
2020-12-08
|
End of preview. Expand
in Data Studio
Dataset Card for "Materials-Informatics"
Dataset Name: Materials-Informatics
Dataset Owner: cs-mubashir
Language: English
Size: ~600+ entries
Last Updated: May 2025
Source: Extracted from arxiv dataset research repository
Dataset Summary
The Materials-Informatics dataset is a curated collection of research papers from arxiv repository focusing on the intersection of artificial intelligence (AI) and materials science and engineering (MSE). Each entry provides metadata and descriptive information about a research paper, including its title, authors, abstract, keywords, publication year, material types, AI techniques used, and application domains.
This dataset aims to serve as a valuable resource for researchers and practitioners working at the convergence of machine learning, deep learning, and materials discovery/design. It can be used for tasks like information retrieval, scientific NLP, trend analysis, paper classification, and LLM fine-tuning for domain-specific tasks.
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