Brian DeCost

National Institute of Standards and Technology

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Jason Hattrick‐Simpers

Schwartz/Reisman Emergency Medicine Institute

Kamal Choudhary

Material Measurement Laboratory

Howie Joress

Material Measurement Laboratory

A. Gilad Kusne

National Institute of Standards and Technology

Elizabeth A. Holm

Carnegie Mellon University

Kangming Li

University of Toronto

Francesca Tavazza

National Institute of Standards and Technology

Debashish Sur

University of Virginia

Mitra L. Taheri

Johns Hopkins University

Apurva Mehta

SLAC National Accelerator Laboratory

Cooperative Institutions

National Institute of Standards and Technology

Material Measurement Laboratory

Massachusetts Institute of Technology

National Institute of Standards

University of Maryland, College Park

Johns Hopkins University

Carnegie Mellon University

Brookhaven National Laboratory

Materials Science & Engineering

Northwestern University

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AtomVision: A machine vision library for atomistic images

arXiv (Cornell University) (2022)

Kamal Choudhary Ramya Gurunathan Brian DeCost Adam J. Biacchi

Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate, curate scanning tunneling microscopy (STM) and scanning transmission electron microscopy (STEM) datasets and apply machine learning techniques. To demonstrate the applicability of this library, we 1) generate and curate an atomistic image dataset of about 10000 materials, 2) develop and compare convolutional and graph neural network models to classify the Bravais lattices, 3) develop fully convolutional neural network using U-Net architecture to pixelwise classify atom vs background, 4) use generative adversarial network for super-resolution, 5) curate a natural language processing based image dataset using open-access arXiv dataset, and 6) integrate the computational framework with experimental microscopy tools. AtomVision library is available at https://github.com/usnistgov/atomvision.

10.48550/arxiv.2212.02586

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Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

arXiv (Cornell University) (2023)

Md Habibur Rahman Prince Gollapalli Panayotis Manganaris Satyesh Kumar Yadav Ghanshyam Pilania

Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98 % given the range of values within the dataset, improving significantly on the state-of-the-art. Models are tested for different defect types as well as for defect charge transition levels. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially-optimized defective structures, helping identify electronically active defects in technologically-important semiconductors.

Crystal Structure Prediction

10.48550/arxiv.2309.06423

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Leveraging Domain Adaptation for Accurate Machine Learning Predictions of New Halide Perovskites

arXiv (Cornell University) (2024)

Dipannoy Das Gupta Zachary J. L. Bare Suxuen Yew Santosh Adhikari Brian DeCost

We combine graph neural networks (GNN) with an inexpensive and reliable structure generation approach based on the bond-valence method (BVM) to train accurate machine learning models for screening 222,960 halide perovskites using statistical estimates of the DFT/PBE formation energy (Ef), and the PBE and HSE band gaps (Eg). The GNNs were fined tuned using domain adaptation (DA) from a source model, which yields a factor of 1.8 times improvement in Ef and 1.2 - 1.35 times improvement in HSE Eg compared to direct training (i.e., without DA). Using these two ML models, 48 compounds were identified out of 222,960 candidates as both stable and that have an HSE Eg that is relevant for photovoltaic applications. For this subset, only 8 have been reported to date, indicating that 40 compounds remain unexplored to the best of our knowledge and therefore offer opportunities for potential experimental examination.

Domain Adaptation

10.48550/arxiv.2401.10998

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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

arXiv (Cornell University) (2018)

Felipe Oviedo Zekun Ren Shijing Sun Charlie Settens Zhe Liu

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.

Interpretability

Pooling

10.48550/arxiv.1811.08425

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A reproducibility study of atomistic line graph neural networks for materials property prediction

Digital Discovery (2024)

Kangming Li Brian DeCost Kamal Choudhary Jason Hattrick‐Simpers

ALIGNN performance on 29 regression tasks can be generally well reproduced with minor disparity due to stochasticity.

10.1039/d4dd00064a

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High-throughput aqueous passivation behavior of thin-film vs. bulk multi-principal element alloys in sulfuric acid

Corrosion Science (2024)

William Blades Debashish Sur Howie Joress Brian DeCost Emily F. Holcombe

Multi-principal element alloys have the potential to show excellent passivation behavior. However, the detailed compositional and crystal structure design of these alloys requires a high-throughput strategy. We used combinatorial thin-film libraries of single-phase (FeCoNi)1-x-yCrxAly alloys and compared their passivation behaviors to corresponding bulk alloys. Our results demonstrate that the detailed passivation behaviors of thin-films and bulk alloys are different which is related to both nanoscale porosity within the thin-films and grain boundary dissolution. Nevertheless, we found that comparisons made among suitably designed sets of thin-film alloys can be used to determine the best corrosion performing bulk alloy composition.

Passivation

10.1016/j.corsci.2024.112261

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UHCSDB: UltraHigh Carbon Steel Micrograph DataBase

Integrating materials and manufacturing innovation (2017)

Brian DeCost Matthew D. Hecht Toby Francis Bryan A. Webler Yoosuf N. Picard

10.1007/s40192-017-0097-0

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Citations (71)

Recent advances and applications of deep learning methods in materials science

npj Computational Materials (2022)

Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

Identification

Modalities

10.1038/s41524-022-00734-6

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Scientific AI in materials science: a path to a sustainable and scalable paradigm

Machine Learning Science and Technology (2020)

Brian DeCost JR Hattrick-Simpers Zachary Trautt A. Gilad Kusne Eva M. Campo

Abstract 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 (SciAI) 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.

Paradigm shift

10.1088/2632-2153/ab9a20

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Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis

arXiv (Cornell University) (2018)

Shijing Sun Noor Titan Putri Hartono Zekun Ren Felipe Oviedo Antonio M. Buscemi

Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to novel lead-free compositions. The wider synthesis window and faster cycle of learning enables three noteworthy scientific findings: (1) we realize four inorganic layered perovskites, A3B2Br9 (A = Cs, Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report a multi-site lead-free alloy series that was not previously described in literature, Cs3(Bi1-xSbx)2(I1-xBrx)9; and (3) we reveal the effect on bandgap (reduction to <2 eV) and structure upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br. This study demonstrates that combining an accelerated experimental cycle of learning and machine-learning based diagnosis represents an important step toward realizing fully-automated laboratories for materials discovery and development.

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