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Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science

Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science

Daniel Wheeler

July 22, 2022
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  1. Ankit Agrawal
    Research Associate Professor
    Department of Electrical Engineering and Computer Science,
    Northwestern University
    Deep Materials Informatics:
    Illustrative Applications of Deep Learning
    in Materials Science
    April 2020
    Collaborators:
    Surya Kalidindi (GaTech), Greg Olson (NU, QuesTek), Chris Wolverton (NU), Peter
    Voorhees (NU), Veera Sundararaghavan (UMich), Marc De Graef (CMU), Wei Chen (NU),
    Cate Brinson (Duke), Logan Ward (UC), Carelyn Campbell (NIST), Kamal Choudhary
    (NIST), Francesca Tavazza (NIST), Andrew Reid (NIST), Stefanos Papanikolaou (WVU)
    Team Members:
    Alok Choudhary, Wei-keng Liao, Kasthurirangan
    Gopalakrishnan, Dipendra Jha, Zijiang Yang, Arindam Paul

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  2. Research Thrusts

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  3. • NIST Center of Excellence: Center for Hierarchical Materials Design (CHiMaD)
    • AFOSR MURI: Managing the Mosaic of Microstructure
    • DARPA SIMPLEX: Data-Driven Discovery for Designed Thermoelectric Materials
    • NSF BigData Spoke: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD):
    Leverage, Innovate, & Disseminate
    • NU Data Science Initiative: Data-driven analytics for understanding processing-structure-
    property-performance relationships in steel alloys
    • DLA: Digital Innovation Design (DID)
    • Toyota Motor Corporation: The investigation of machine learning for material development
    Current and Past Projects

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  4. Overview
    • Introduction
    ★ Paradigms of Science
    ★ Deep Learning: Advantages, Challenges, Network Types
    • Illustrative Materials Informatics
    ★ Forward PSPP models
    ★ Inverse PSPP models
    ★ Structure characterization
    ★ Deep materials informatics
    • Materials Informatics Tools

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  5. Overview
    • Introduction
    ★ Paradigms of Science
    ★ Deep Learning: Advantages, Challenges, Network Types
    • Illustrative Materials Informatics
    ★ Forward PSPP models
    ★ Inverse PSPP models
    ★ Structure characterization
    ★ Deep materials informatics
    • Materials Informatics Tools

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  6. Paradigms of Science
    A. Agrawal and A. Choudhary, “Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of
    science in materials science, APL Materials, 4, 053208 (2016), doi:10.1063/1.4946894
    1st paradigm:
    Empirical
    science
    2nd paradigm:
    Model-based
    theoretical
    science
    3rd paradigm:
    Computational
    science
    (simulations)
    4th paradigm:
    (Big) data
    driven science
    2000
    1950
    1600
    Laws of
    Thermodynamics
    Density Functional
    Theory,
    Molecular Dynamics
    ∆U = Q – W
    Change in Heat Work
    internal added done
    energy to system by system
    Experiments
    Predictive analytics
    Clustering
    Relationship mining
    Anomaly detection

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  7. “Ability of machines to perform tasks that normally require human intelligence”
    [2018 DOD AI Strategy]
    Artificial Intelligence
    Artificial Intelligence
    Machine Learning
    Deep Learning
    Weak AI
    Strong AI
    Super AI

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  8. “Field of study that gives computers the ability to learn without being
    explicitly programmed”
    [Arthur Samuel, 1959]
    Machine Learning
    • Algorithms whose
    performance improves as
    they are exposed to more
    data over time
    • AI/ML has been around for
    decades, but it always has
    been hungry for big data
    and big compute
    è Deep Learning
    Artificial Intelligence
    Machine Learning
    Deep Learning

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  9. Deep Learning
    “A rediscovery of neural networks fueled by the availability of big data and big compute”

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  10. Deep Learning Success Stories

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  11. Amount of data
    Performance
    Deep
    learning
    Most
    learning
    algorithms
    Deep Learning

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  12. Types of Deep Learning Networks
    Fully connected network (MLP)
    Generative adversarial network (GAN)
    Convolutional neural network (CNN)
    Residual learning
    network (ResNet)
    Recurrent neural network (RNN)

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  13. https://thenextweb.com/artificial-intelligence/2019/02/13/thispersondoesnotexist-com-is-face-generating-ai-at-its-creepiest/

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  14. Overview
    • Introduction
    ★ Paradigms of Science
    ★ Deep Learning: Advantages, Challenges, Network Types
    • Illustrative Materials Informatics
    ★ Forward PSPP models
    ★ Inverse PSPP models
    ★ Structure characterization
    ★ Deep materials informatics
    • Materials Informatics Tools

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  15. Illustrative Materials Informatics
    • Forward PSPP models (property prediction)
    o Steels [IMMI 2014, CIKM 2016, IJF 2018, DSAA 2019]
    o Crystalline stability [PRB 2014, npjCM 2016, ICDM 2016, DL-KDD
    2016, PRB 2017, SciRep 2018, KDD 2019, NatureComm 2019]
    o Band gap and glass forming ability prediction [npjCM 2016]
    o Bulk modulus prediction [RSC Adv 2016]
    o Seebeck coefficient prediction [JCompChem 2018]
    o Multi-scale localization/homogenization [IMMI 2015, IMMI 2017,
    CMS 2018, ActaMat 2019, IJCNN 2019]
    o Chemical properties prediction [NIPS MLMM 2018, IJCNN 2019,
    Molecular Informatics 2019]
    • Inverse PSPP models (optimization/discovery)
    o Stable compounds [PRB 2014]
    o Magnetostrictive materials [Scientific Reports 2015, AIAA 2018]
    o Semiconductors and metallic glasses [npjCM 2016]
    o Microstructure design (GAN) [JMD 2018]
    o Titanium aircraft panels [CMS 2019]
    • Structure characterization
    o EBSD Indexing [BigData-ASH 2016, M&M 2018]
    o Crack detection in macroscale images [CBM 2017, IJTTE 2018]
    o XRD analysis for phase detection [IJCNN 2019]
    o Plastic deformation identification [IJCNN 2019]

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  16. DFT Data Mining
    Density Functional Theory
    • Very slow simulations
    • Require crystal structure as input
    Training Data
    • Hundreds of thousands of DFT
    calculations from (OQMD)
    • JARVIS-DFT (NIST)
    Composition-based models
    • 145 attributes (stoichiometric/
    elemental/electronic/ionic)
    Structure-aware models
    • Voronoi tessellations to capture local
    environment of atoms
    Deep learning models (ElemNet)
    • Use only element fractions
    • 20% more accurate and two orders
    of magnitude faster
    • Learn chemistry of materials
    Inverse models
    • Stable compounds, metallic glasses,
    semiconductors, quaternary heuslers
    Software
    • FEpredictor, Magpie
    Agrawal et al., ICDM 2016; Ward et al., npj Comp Mat 2016; Ward et al., PRB 2017; Liu et al., DL-KDD 2016; Jha et al., SciRep 2018
    Online Tool: http://info.eecs.northwestern.edu/FEpredictor
    Collaboration between Agrawal, Choudhary, Wolverton, Ward, NIST

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  17. ElemNet: Deep Learning the Chemistry of Materials
    Jha et al., Scientific Reports 2018
    Li

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  18. Model
    Type
    Plain
    Network
    SRNet IRNet
    17-layer 0.0653 0.0551 0.0411
    24-layer 0.0719 0.0546 0.0403
    48-layer 0.1085 0.0471 0.0382
    48-layers
    Motivation
    • Deep neural networks suffer
    from the vanishing gradient
    problem as depth increases
    Proposed Solution
    • Individual residual learning
    with skip connections across
    each layer
    Datasets
    • OQMD-SC (435,582 x 271)
    • OQMD-C (341,443 x 145)
    • MP-C (83,989 x 145)
    Results
    • IRNet > SRNet > PlainNetwork
    • Up to 65% reduction in MAE
    • IRNet beats best of 10
    traditional ML approaches
    (e.g. Random Forest) on 9 out
    of 10 dataset-property
    combinations
    Formation enthalpy prediction MAE (eV/atom)
    on OQMD-SC dataset
    Deeper Learning: Individual Residual Network (IRNet)
    D. Jha, L. Ward, Z. Yang, C. Wolverton, I. Foster, W.-keng Liao, A. Choudhary, and A. Agrawal, “IRNet: A General Purpose Deep Residual Regression
    Framework for Materials Discovery,” 25th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2019, pp. 2385–2393.

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  19. EBSD Indexing Using Deep Learning
    Collaboration between Agrawal, Choudhary, De Graef
    Tilted
    specimen
    Diffraction
    plane
    X
    Y
    Z
    Z’
    Y’
    X’
    ϕ
    φ
    1 φ
    2
    (a)
    (b)
    ϕ
    Objective: Fast and accurate indexing of electron backscatter diffraction
    (EBSD) patterns
    Solution: Deep convolutional neural networks with
    customized loss function
    Electron
    beam
    Tilted
    specimen
    Diffraction
    plane
    Screen
    detector
    X
    Y
    Z
    Z’
    Y’
    X’
    ϕ
    φ
    1 φ
    2
    (a)
    (b)
    ϕ
    Predictor
    MAE
    (degrees)
    Training
    time
    Run time
    1-NN 5.7, 5.7, 7.7 0 375s
    Deep
    Learning
    2.5, 1.8, 4.8 7 days 50s
    Results: On average 16% more accurate and 86%
    faster predictions compared to state-of-the-art
    dictionary-based indexing (1-nearest-neighbor
    with cosine similarity)
    Liu et al., BigData ASH 2016; Jha et al., M&M 2018
    Data: 375K simulated EBSD patterns
    Model Loss Function Simulation Data Experimental Data
    Mean
    Disorientation
    Mean
    Disorientation
    Mean Symmetrically Equivalent
    Orientation Absolute Error
    (MSEAE)
    Dictionary
    based Indexing
    - - 0.652 [0.6592, 0.3534, 0.6484]
    Deep Learning
    Mean Absolute Error 0.064 0.596 [0.4039, 0.1776, 0.4426]
    Mean Squared Error 0.292 1.285 -
    Mean Disorientation 0.272 1.224 -
    MAE + Mean
    Disorientation
    0.132 0.548 [0.7155, 0.2194, 0.7066]
    MSE + Mean
    Disorientation
    0.171 0.658 -

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  20. FEM Data Mining: Deep learning for Localization Relationships
    FE DL MKS FE DL MKS
    Results: Fast approximate to FEM and much more
    accurate than existing data-driven methods.
    Challenge:
    Predict from 0/1
    to real numbers!
    Solution: 3-D CNNs
    Contrast 50
    Results:
    5.71%
    Average
    MASE
    Contrast 10
    Results:
    3.07%
    Average
    MASE
    Collaboration between Agrawal, Choudhary, Kalidindi
    Yang et al., Acta Mat 2019

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  21. Challenge
    • Identifying a low dimensional
    microstructure representation
    • Use it for materials design
    Proposed Solution
    • Deep learning
    • Generative adversarial networks
    • Bayesian optimization with
    RCWA
    Data
    • 5000 128x128 images
    synthesized using GRF method
    Results
    • 4x4 matrix (design variables)
    • Statistically similar
    microstructures
    • 17% better optical absorption
    • Scalable generator
    • Transferable discriminator Yang and Li et al., JMD 2018
    Collaboration between Agrawal, Choudhary, Chen, Brinson
    Deep Adversarial Learning for Microstructure Design

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  22. Pavement Crack Detection Using Deep Transfer Learning
    Objective: Fast and accurate crack detection from Hot-Mix
    Asphalt (HMA) and Portland Cement Concrete (PCC)
    surfaced pavement images
    Solution: A binary classifier trained on ImageNet pre-trained VGG-16
    CNN features for pavement images
    Results: Up to 90% classification accuracy and 0.87 AUC
    Gopalakrishnan et al., CBM 2017
    Data: Pavement distress images from the Federal Highway
    Administration’s (FHWA’s) Long-Term Pavement Performance
    (LTPP) program
    Challenges: Inhomogeneity of crack, diversity of surface
    texture, background complexity, presence of non-crack
    features such as joints, etc.

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  23. Motivation
    • Magnet properties prediction in a
    complex processing workflow
    Challenges
    • Experimental data
    • Small, heterogenous, noisy
    Methodology
    • Gradient boosting
    • Deep transfer learning from VGG16
    for SEM image featurization
    Results and Impact
    • <5% prediction error for Hcj and Br
    • Cost implications: only relevant
    experiments, avoid higher-end
    processing for unpromising
    candidates, reduce SEM man-hours
    è savings of millions of $$$
    • Faster magnets design: identify
    most promising regions and routes Yang et al., ICDM LMID 2019
    Industrial Materials Design
    P1 Hcj
    P1 Br
    P2 Hcj
    P2 Br
    Combination (Numerical + Image) Model Workflow
    A typical processing workflow

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  24. https://doi.org/10.1557/mrc.2019.73

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  25. Overview
    • Introduction
    ★ Paradigms of Science
    ★ Deep Learning: Advantages, Challenges, Network Types
    • Illustrative Materials Informatics
    ★ Forward PSPP models
    ★ Inverse PSPP models
    ★ Structure characterization
    ★ Deep materials informatics
    • Materials Informatics Tools

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  26. Illustrative Materials Informatics Tools
    http://info.eecs.northwestern.edu

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