May 3, 2018 - 38k alloys: . > 1 mm. 945k alloys: 1 distance > 30at% .... REST API for models. 42. You can do anything I showed today with ...
Machine Learning for Materials Design Logan Ward Postdoctoral Scholar Computation Institute, University of Chicago 3 May 2018
Importance of Materials Design 2
Examples of “Aluminum” Application
Key Properties
Conductivity
Low cost Shapeable
Alloy
Al + Mg
Al + Mg,Mn
Castable Strong
???
Al + Si,Cu
???
How do we tailor materials for new technologies? How can we do this quickly?
Computational Materials Engineering 3
Goal: Accelerate design of materials Method: Replace experiments with computers
Many Established Tools: • • • •
Density Functional Theory Phase Field Finite Element Analysis Computational Thermodynamics
Emerging Field: Data-driven Models Materials Data
Machine Learning
Predictive 𝜎𝑌 = 𝑓(𝑥) Model
FEA Image: http://www.icams.de/content/research/index.html
Machine Learning: What? Why? 4
What? Why?
Algorithms that generate computer programs Create software too complex to write manually 𝑦 = 𝑓(𝑥)
General Task: Given inputs, predict output Common Algorithms: y
𝑓(𝑥) = 𝑚𝑥 + 𝑏 𝑥 0? Training Set Dataset: 3000 entries from the OQMD
Simple ML Model: Accuracy 84.1%
Game: palestrina.northwestern.edu/metal-detection/
Application to the OQMD 13
Dataset: 240000 DFT Calculations (OQMD.org) Eg
𝚫𝐇𝐟
R: 0.944 MAE: 80.5 meV/atom
R: 0.924 MAE: 0.21 eV
Ref: Ward et al. npj Comp. Mat., (2016) 28
V
R: 0.993 MAE: 0.452 Å3/atom
Predicting Glass Forming Ability 14
Application: Metallic Glasses Goal: Predict glass-forming ability Dataset: Landolt-Börnstein 6836 experimental measurements 295 ternary systems Binary property: [Can Form Glass] | [Cannot Form]
Model: Random Forest
90% accurate in 10-fold cross-validation Ref: Ward et al. npj Comp. Mat., (2016) 28
Predicting Glass-Forming Ability 15
Test: Remove Al-Ni-Zr data from training data, try to predict Measured
Predicted X No glass ● Glass
Same representation, very different material. Ref: Ward et al. npj Comp. Mat., (2016) 28
Focus #2: Crystal Structure 16
Our Approach: Voronoi-tessellation-based attributes Atomic Characteristics: 1. Element identity 2. Coordination number 3. Bond length 4. Cell size … Atomic Characteristics + Descriptive Statistics = 275 Attributes Ref: Ward et al. PRB. (2017) 024104
Learning Rate Comparison 17
Dataset: 32k DFT Δ𝐻𝑓 from the OQMD Test: Remove 1000, train on N remaining
Cross-validation is great, but does not model real use
CM: Faber et al. Int J Quantum Chemisty. 2015; PRDF: Schutt et al. PRB. (2014)
Application: The Prototype Search 18
Common Method: Prototype Search 1.
Select a crystal structure
2.
Evaluate all possibilities with DFT Select only stable ones
3.
Challenge: Computational cost Possible Solution: Guide with ML
Ref: Ward et al. PRB. (2017) 024104
DFT
Ranking Candidate Materials 19
Training Set: 32k entries from OQMD Test: Select top 100 entries from test set
Ranks entries better than existing methods, good choice for accelerating combinatorial searches
Ref: Ward et al. PRB. (2017) 024104
Summary 20
Collect
Process
Composition-Based Attributes
Represent
Learn
Crystal Structure Attributes
How do I use this to find a new material?
21
Part 3: Using ML To Find Materials Acknowledgments: Liquidmetal: S. O’Keeffe, J. Stevick, G. Jelbert NIST: J. Hattrick-Simpers SLAC: F. Ren, A. Mehta TRI: M. Aykol
Application: Bulk Metallic Glasses 22
Metals with Amorphous Structures ?
V
Tg
No Dislocations -> High 𝜎𝑌 Glass Transition -> Net Shape Casting No Grain Boundaries -> Low Hysteresis
Applications: Surgical tools, flight control surfaces, …
What are the tradeoffs? Source: liquidmetal.com Source: Wikipedia.org
T
Problems with BMGs 23
Main Problem: Metastability of glassy phase
GFA 𝑫𝒎𝒂𝒙 𝚫𝐓𝐱
Few Alloy Systems Known to Form Glasses Properties Links Our Proposed Solution: Data analytics Goal: Optimize 𝐷𝑚𝑎𝑥 and ΔTx
Materials Informatics Workflow 24
Collect
Process
Represent
Learn
𝑋Ԧ = 𝑓(𝐶𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛)
“General Purpose” Attributes + Cluster Packing Models + DFT Δ𝐻𝑓 from OQMD GFA
6315 entries
GFA
𝑫𝒎𝒂𝒙
5916 entries
𝑫𝒎𝒂𝒙
𝚫𝐓𝐱
621 entries
𝚫𝐓𝐱
Random Forest Random Forest + Additive Regression
Task #1: Tuning Known Alloys 25
Tuned Alloys: Pareto Analysis 26
Predicted alloys have superior properties
Results: Improved design space
Task #2: Discovering New Alloys 27
Search Space: Ternary alloys of 53 elements 26 million ternary alloys 945k alloys: 𝐿1 distance > 30at%
290k alloys: 𝑃(𝐺𝑙𝑎𝑠𝑠) > 95% 57k alloys: Δ𝑇𝑥 > 66 K 38k alloys: 𝐷𝑚𝑎𝑥 > 1 mm
Run Time: ~ 2 days
Chosen System: Cu-Hf-[Mg,Ti]
Cu-Hf-Mg
Cu-Hf-Ti
28
Cu-Hf-[Ti/Mg]: Current results 29
Cu-Hf-Mg
Cu-Hf-Ti
~4 mm
Sample crystallized and/or shattered during injection molding - Cu60Hf25Ti15 could be formed with copper mold casting*
Mg ignited during arc melting Lessons Learned: • Integrate data from target process • Screen based on processing technique *Inoue et al. Acta Mat. (2001), 2645
Task #3: Sputtered Glasses 30
Goal: Find new sputterable glasses Search Space: Ternary alloys of 24 elements (~2.4 Million Alloys) Method: Combinatorial co-deposition Ding, et al. Nat. Mat. (2014) 2.4 M alloys / 1000 alloys/day -> 6 years Need: Prioritize promising experiments Ref: Ren et al. Sci Adv. (2018), eaaq1566
Step 1: Account for Processing Method 31
Design Goal: Find new sputterable glasses Step 1: Include processing information
Melt Spinning Data
ML Wikipedia
𝐺𝐹𝐴𝑀𝑆 = 𝑓(𝑥𝐻 , 𝑥𝐿𝑖 , … ) “Stacking”
Sputtering Data
ML
𝐺𝐹𝐴𝑆𝑃 = 𝑔(𝑥𝐻 , 𝑥𝐿𝑖 , … , 𝑓(… ))
Ding, et al. Nat. Mat. (2014)
Ref: Ren for et al.processing Sci Adv. (2018),method eaaq1566 by training stacked models Account
Step 1: Account for Processing Method 32
Goal: Find new glasses via sputtering Step 1: Include processing information Melt Spinning
Sputtering
Tune for Sputtering
Ref: Ren et al. Sci Adv. (2018), eaaq1566
Step 2: Adding Theories 33
Yang and Zhang. Mat. Chem. Phys. (2012)
Laws et al. Nat. Comm. (2015)
Mixing Thermo.
K. Laws (UNSW)
Size Mismatch Agreement for Zr ~ 50at%
Disagreement for high/low-Zr
Ref: Ren et al. Sci Adv. (2018), eaaq1566
Next Step: Adding Theories 34
Add Theories
Step 3: Scan 2.4M alloys (~1 hr) -> Selected Co-V-Zr
Ref: Ren et al. Sci Adv. (2018), eaaq1566
Comparison to Experiment 35
J. Hattrick-Simpers F. Ren (SLAC) (NIST)
A. Mehta T. Williams (SLAC) (USC)
Decent agreement. ML model “Zr-lean”, but close enough for success
Repeat, with Improved Model 36 Original Model
Before Co-V-Zr
HiTp Data
Updated Model
After Co-V-Zr
Improvements for Many Systems 37
Test: Leave-Ternary-Out CV Test Set: 673 alloys, 35 ternary diagrams No Sputtering Data No Sputtering Data Landolt Bornstein + All HiTp Data
HiTp Data Improves ML Accuracy for all Systems
Improvements for Many Systems 38
39
Software Infrastructure
Goal: Tight integration with design 40
Future: Adaptive design with automated experiments Problem: Disconnect between method developers and model users
What do we need to share?: • Data • Software/Methods • Models
Need #1: Access to Data Databases Datasets APIs LIMS etc.
EP
Data Discovery
Data Publication
Distributed data storage
• Mint DOIs • Associate metadata • Persist datasets
EP
• Query • Browse • Aggregate
EP
Ian Foster (PI)
Ben Blaiszik
Jonathon Gaff
Need #2: Accessible Methods 42
Language: Java Key Features: 1. Fast implementations of representations 2. Interfaces to ML libraries 3. REST API for models
Language: Python Key Features: 1. Links to materials databases 2. Compatible with Scipy stack 3. Large library of representations
You can do anything I showed today with Magpie and (soon) matminer
Need #3: Sharable ML Models (DLHub) 43 Send Model
Discover Data
Discover Data
Train Model
User 1
Send Materials Retrieve Data User 2
Call Call DLHub DLHub Model Receive Properties New science!
Library of materials ML models in Fall 2018
DLHub
Receive DOI
Summary 44
General-Purpose Representations Using Composition: DFT Δ𝐻𝑓 , 𝐸𝑔 , 𝑉; Glass-forming ability Using Crystal Structure: 2x better than existing approaches
Example applications: ✓ ✓
Improved Δ𝑇𝑥 of 2 commercial BMGs alloys Discovered many new metallic glasses
Open Databases and Software: http://materialsdatafacility.org/ http://bitbucket.org/wolverton/magpie http://github.com/hackingmaterials/matminer
Thanks to our sponsors! ALCF DF
Parsl
Globus
DLHub U.S. DEPARTMENT OF
ENERGY
IMaD
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