Application of Machine Learning to Materials Discovery and Development
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1 Application of Machine Learning to Materials Discovery and Development Ankit Agrawal and Alok Choudhary Department of Electrical Engineering and Computer Science Northwestern University Contributors: Surya Kalidindi (GaTech), Basavarsu (TRDDC), Chris Wolverton (NU), Ahmet Cecen (GaTech), Parijat Deshpande (TRDDC), Bryce Meredig (NU) MURI 3-Year Review June 22-23, 2015
2 Integrated Computational Materials Engineering (ICME) Goal/means Performance Properties Structure Processing Cause and effect Olson, G. B. (1997). Computational design of hierarchically structured materials. Science, 277(5330),
3 Project Collaboration Goal/means Processing Structure Properties Performance Project I. Multi-objective Structure-Property Optimization Cause and effect Olson, G. B. (1997). Computational design of hierarchically structured materials. Science, 277(5330),
4 Project Collaboration Goal/means Processing Structure Properties Performance Project I. Multi-objective Structure-Property Optimization Project II. Multiscale Prediction of Localization Relationships Cause and effect Olson, G. B. (1997). Computational design of hierarchically structured materials. Science, 277(5330),
5 Project Collaboration Goal/means Processing Structure Properties Performance Cause and effect Project I. Multi-objective Structure-Property Optimization Project II. Multiscale Prediction of Localization Relationships Project III. Exploring Composition-Processing- Property Relationships Project IV. Compositionbased Discovery of Stable Compounds Olson, G. B. (1997). Computational design of hierarchically structured materials. Science, 277(5330),
6 Predicting fatigue strength of steel from composition and processing parameters Collaborative project between Agrawal (NU), Choudhary (NU), Kalidindi (GaTech), Basavarsu (TRDDC) Objective: Employ data-driven approaches to the NIMS public domain materials database for exploring composition-processingproperty relationships and constructing predictive models for fatigue strength of steels. COMPOSITION MANUFACTURING PROCESSES CORRELATES TO CORRELATES TO PROPERTIES (FATIGUE STRENGTH)
7 NIMS Database Attributes Fatigue Data Sheet Information: Chemical composition - %C, %Si, %Mn, %P, %S, %Ni, %Cr, Cu %, Mo% (all in wt. %) Upstream processing details - Ingot size, Reduction ratio, Non-metallic inclusions Heat treatment conditions Temperature, Time and other process conditions for Normalizing, Carburizing-Quenching and Tempering processes Mechanical properties - YS, UTS, %EL (Elongation), %RA (Reduction in Area), Vickers Hardness, Charpy impact value (J/cm 2 ), Rotating bending fatigue 10 7 cycles Total data records Carbon and low alloy steels observations, Carburizing steels - 48 observations and Spring steels -18 observations Reference : 6
8 Steel Fatigue Strength Prediction Framework 7
9 Data Mining Modeling Classification/Regression Learning a predictive model based on supervised (labeled) training data, which can then be used to classify unseen data E.g. Decision trees, Neural Networks, Support Vector Machines, etc. Model evaluation Test-train split Split the labeled data into training and testing sets Cross-validation Test every instance in the dataset using a model that has not seen that instance Types k-fold cross validation Leave-one-out cross-validation (LOOCV) with k=n Training split Testing split 8
10 Cluster Visualization 9
11 Information Gain Based Feature Ranking 10
12 Evaluation Metrics Compare vectors of actual and predicted values Coefficient of correlation (R) Coefficient of determination (R 2 ) Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) Standard Deviation of Error (SDE) Mean Absolute Error Fraction (MAE) Root Mean Squared Error Fraction (RMSE) Standard Deviation of Error Fraction (SDE)
13 Results Comparison 12
14 13
15 Results Comparison A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters, Integrating Materials 14 and Manufacturing Innovation, 3 (8): 1 19, 2014.
16 Discovery of stable compounds Collaborative project between Agrawal (NU), Choudhary (NU), Wolverton (NU)
17 Discovery Framework Database Construction Thousands of DFT formation energies Empirical elemental data Predictive Modeling Model 1: established heuristic Model 2: data mining Model Evaluation Test models on unseen formation energies (a) Prediction Run combinatorial list of compositions through models Ranking Combine heuristic and data mining predictions Validation Experiments Crystal structure prediction Millions of candidate ternary compositions Models Formation energy predictions Ranked highpotential candidates Compound discovery (b)
18 Model formation energy (ev/atom)! Model Validation: Numerical DM: binaries! R 2 = 0.87! MAE = 0.27 ev/at! DM: bin. + tern.! R 2 = 0.93! MAE = 0.16 ev/at! Heuristic! R 2 = 0.95! MAE = 0.12 ev/at! DFT formation energy (ev/atom)!
19 True positive rate (sensitivity)! Model Validation: Ranking 1! 0.8! perfect classifier Classify all unstable DM: bin. +4k tern.! classify all stable Combined model outperforms either alone in regime of interest 0.6! 0.4! combined! heuristic! Classifier becomes: more conservative less conservative 0.2! random guessing! classify all unstable 0! 0! 0.2! 0.4! 0.6! 0.8! 1! False positive rate (1 - specificity)!
20 What happens when we rank all possible ternaries by their likelihood of stability?
21 Predictions for Discovery Interesting insights: Fingerprint of entire unexplored ternary composition space! Highest ranked ternary: SiYb 3 F 5 Si acts as an anion Validated with structure and DFT calculations pnictides, chalcogenides, halides Pt-X-Y Pm 12 S 19 Se a missing binary Pm 2 S 3? Average of all A-B-X ternaries
22 Validation Example of discovered stable ternary compositions whose stability was explicitly confirmed with crystal structure prediction. Our method is successful at identifying new stable compounds across a wide variety of chemistries. B. Meredig*, A. Agrawal*, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, Combinatorial screening for new materials in unconstrained composition space with machine learning, Phys. Rev. B, 89, , March
23 Summary Steel Fatigue Strength Prediction o NIMS database consisting of composition and processing parameters linked with performance (fatigue strength). o Neural networks, decision trees, multivariate polynomial regression able to achieve high R 2 values of >0.98. Stable Compound Discovery o A database of DFT calculations used to learn compositionproperty relationships, thus mimicking DFT for estimating stability. o The resulting predictive models used to scan the entire ternary composition space to discover likely stable compositions. o Many predictions explicitly confirmed with crystal structure prediction and DFT. 22
24 Future Outlook Goal/means Processing Structure Properties Performance Cause and effect Project I. Multi-objective Structure-Property Optimization Project II. Multiscale Prediction of Localization Relationships Project III. Exploring Composition-Processing- Property Relationships Project IV. Compositionbased Discovery of Stable Compounds 23
25 Publications A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters, Integrating Materials and Manufacturing Innovation, vol. 3, no. 8, pp. 1 19, B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, Combinatorial screening for new materials in unconstrained composition space with machine learning, Physical Review B, vol. 89, no , pp. 1 7, BM and AA are co-first authors. Ruoqian Liu, Abhishek Kumar, Zhengzhang Chen, Ankit Agrawal, Veera Sundararaghavan, Alok Choudhary, A predictive machine learning approach for microstructure optimization and materials design, Scientific Reports, Nature Publishing Group, 2015, in press. R. Liu, Z. Chen, T. Fast, S. Kalidindi, A. Agrawal, and A. Choudhary, Predictive Modeling in Characterizing Localization Relationships TMS Annual Meeting & Exhibition, Symposium of Data Analytics for Materials Science and Manufacturing, Feb , San Diego, CA. R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary, A Data Mining Approach in Structure-Property Optimization TMS Annual Meeting & Exhibition, Symposium of Data Analytics for Materials Science and Manufacturing, Feb , San Diego, CA. P. D. Deshpande, B. P. Gautham, A. Cecen, S. Kalidindi, A. Agrawal, and A. Choudhary, Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels, in 2nd World Congress on Integrated Computational Materials Engineering, July 7-11, 2013, Salt Lake City, Utah, 2013, pp R. Liu, Y. Yabansu, S. Kalidindi, A. Agrawal, and A. Choudhary, Predictive Modeling in Characterizing Localization Relationships. 2015, in preparation. 24
26 Thank You! Ankit Agrawal Research Associate Professor Dept. of Electrical Engineering and Computer Science Northwestern University 25
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