“Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science

Ankit Agrawal
Research Associate Professor
Department of Electrical Engineering and Computer Science,
Northwestern University
Materials Informatics and Big Data:
Realization of “Fourth Paradigm” of Science
in Materials Science
Collaborations:
Alok Choudhary (NU), BP Gautham (TRDDC), Surya Kalidindi
(GaTech), Greg Olson (NU, QuesTek), NIMS, Chris Wolverton (NU),
Logan Ward (UC), Peter Voorhees (NU), Veera Sundararaghavan
(UMich), Marc De Graef (CMU),Wei Chen (NU), Cate Brinson (Duke),
Carelyn Campbell (NIST), Kamal Choudhary (NIST), Francesca
Tavazza (NIST), Andrew Reid (NIST), Stefanos Papanikolaou (WVU)
Artificial Intelligence for Materials Science (AIMS) Workshop
NIST, Gaithersburg, MD
August 07, 2018

• 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
• Toyota Motor Corporation: The investigation of machine learning for material
development
MGI: Projects

Overview
• Materials Informatics and Big Data
★ Paradigms of Science
★ PSPP Relationships in Materials Science
★ Materials Informatics Knowledge Discovery Workflow
• Illustrative Materials Informatics
★ Forward PSPP models
★ Inverse PSPP models
★ Structure characterization
• Materials Informatics Tools

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

Data Data Everywhere…
Volume: Amount of data (size)
Velocity: Speed with which new data is
generated
Variety: Heterogeneity in the data
Variability: Inconsistency in the data
Veracity: How trustworthy the data is
Value: Knowledge hidden in big data
(needle in a haystack)
Visualization: Ability to interpret the data
and resulting insights

PSPP Relationships in Materials 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

Materials Informatics Knowledge Discovery Workflow
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

Illustrative Materials Informatics
• Forward PSPP models (property prediction)
o Steel fatigue strength prediction [IMMI 2014, CIKM 2016, IJF
2018]
o Formation energy prediction [PRB 2014, npjCM 2016, ICDM
2016, DL-KDD 2016, PRB 2017]
o Band gap and glass forming ability prediction [npjCM 2016]
o Bulk modulus prediction [RSC Adv 2016]
o Seebeck coefficient prediction [JCompChem 2018]
o Data-driven solutions to multi-scale localization relationships
[IMMI 2015, IMMI 2017, CMS 2018]
• Inverse PSPP models (optimization/discovery)
o Stable compounds [PRB 2014]
o Magnetostrictive materials (Galfenol) [Nature Scientific
Reports 2015, AIAA 2018]
o Semiconductors and metallic glasses [npjCM 2016]
o Microstructure design (GAN) [JMD 2018]
• Structure characterization
o EBSD Indexing [BigData-ASH 2016]
o Crack detection in macroscale images [CBM 2017, IJTTE 2018]

Steel Data Mining
Online Tool: http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor
Agrawal et al., IMMI 2014; Agrawal and Choudhary, CIKM 2016, IJF 2018
Significance
• Fatigue accounts for
>90% of mechanical
failures
• High cost and time of
fatigue testing
Goal
• Data-driven forward
models for fatigue
strength of steels
Experimental data
• From NIMS Japan
• 371 carbon and low-
alloy steels, 48
carburizing steels,
and 18 spring steels
Results
• R2 > 0.98 for cross-
validated models
• Online tool deploying
forward models

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
Meredig and Agrawal et al., PRB 2014; 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., under review
Online Tool: http://info.eecs.northwestern.edu/FEpredictor
Collaboration between Agrawal, Choudhary, Wolverton, Ward, NIST

CH MaD
Experimental data
• Extracted from literature
• UCSB database
• TE Design database
Methodology
• 187 attributes (composition/
crystallinity/production method)
• Random Forest models
Predictive models
• R2 up to 0.84
• Work for non-stoichiometric
(doped) materials
Outlier analysis
• Helped identify bad data
Software: ThermoEl toolkit
• Seebeck coefficient
• Bulk modulus Furmanchuk et al., RSC Adv 2016; J Comp Chem 2018
Collaboration between Agrawal, Choudhary, Olson
Thermoelectrics Data Mining
Online Tool: http://info.eecs.northwestern.edu/ThermoEl

FEM Data Mining: Homogenization and Localization
Collaboration between Agrawal, Choudhary, Kalidindi
Liu et al., IMMI 2015; Liu et al., IMMI 2017; Yang et al., CMS 2018; Yang et al., under review.

24
Galfenol Microstructure Optimization
Galfenol
• A magnetoelastic Fe-Ga alloy
Problem
• Discover microstructure with
enhanced optimal strength
and magnetostriction
Forward models known
• Theoretical models well-
established (homogenization)
Inverse models unknown
• Optimization problem
• Challenging due to high
dimensionality of
microstructure space
• Simple realization of inverse
models is prohibitively
expensive!
• Non-uniqueness of solutions
Data-driven optimization
• 80% faster, 20% better than
traditional methods
• Multiple solutions discovered
for the first time
Liu et al., Scientific Reports 2015; Paul et al., AIAA 2018
Collaboration between Agrawal, Choudhary, Sundararaghavan

EBSD Indexing Using Deep Learning
Collaboration between Agrawal, Choudhary, De Graef
Tilted
specimen
Diffraction
plane
X
Y
Z
Z’
Y’
X’
ϕ
φ1 φ2
(a)
(b)
ϕ
Objective: Fast and accurate indexing of electron backscatter diffraction
(EBSD) patterns
Solution: Deep convolutional neural networks (CNN)
with customized loss function
Prediction of First Angle
Prediction of Second Angle
Prediction of Third Angle
Benchmark: 5.7o
Our method: 2.5o
Benchmark: 5.7o
Our method: 1.8o
Benchmark: 7.7o
Our method: 4.8o
Electron
beam
Tilted
specimen
Diffraction
plane
Screen
detector
X
Y
Z
Z’
Y’
X’
ϕ
φ1 φ2
(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 56% more accurate and 86%
faster predictions compared to state-of-the-art
(1-nearest-neighbor with cosine similarity)
Liu et al., BigData ASH 2016; Jha et al., under review.

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
• 142% better optical absorption
• Scalable generator
• Transferable discriminator Yang and Li et al., JMD 2018, in press
Collaboration between Agrawal, Choudhary, Chen, Brinson
Deep Adversarial Learning for Microstructure Design

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.

Application to UAV Images for Structural Health Monitoring
Objective: Monitoring condition of civil infrastructure from
images captured by Unmanned Aerial Vehicles (UAVs) or
drones
Solution: A binary classifier trained on ImageNet pre-trained VGG-16
CNN features for pavement images
Results: Up to 90% classification accuracy and 0.9 AUC in
realistic situations without any augmentation or preprocessing
Gopalakrishnan et al., IJTTE 2018
Data: Images of civil infrastructure captured by Hexacopter-I
UAV with a 30 MP high definition Canon EOS 5D Mk IV DSLR
camera mounted on a 3-axis rotatable gimbal with live video
transmission. A total of 130 images: 80 images (cracked) + 50
images (uncracked)

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

“Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science

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