Machine Learning in Materials Science: Recent Progress and Emerging Applications

Reviews in Computational Chemistry

Volumn 29, Issue , 2016, Pages 186-273

  Mueller, Tim ^a   Kusne, Aaron Gilad ^b   Ramprasad, Rampi ^c  

^a Johns Hopkins University   (United States)

^b NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

^c UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

Crystal structure predictions; Interatomic potentials; Machine learning; Materials science; Phase diagram determination; Supervised learning; Unsupervised learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CRYSTAL STRUCTURE; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATERIALS SCIENCE; PHASE DIAGRAMS; SEARCH ENGINES; SUPERVISED LEARNING; UNSUPERVISED LEARNING;

CRYSTAL STRUCTURE DETERMINATION; CRYSTAL STRUCTURE PREDICTION; DATA-DRIVEN APPROACH; EMERGING APPLICATIONS; INTERATOMIC POTENTIAL; MACHINE LEARNING APPLICATIONS; MACHINE LEARNING METHODS; SUPERVISED AND UNSUPERVISED LEARNING;

MATERIALS PROPERTIES;

EID: 85016437770     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781119148739.ch4     Document Type: Chapter

References (181)

1. R. LeSar, Stat. Anal. Data Min., 1, 372 (2009). Materials Informatics: An Emerging Technology for Materials Development.
2. K. Rajan, Mater. Today, 8, 38 (2005). Materials Informatics.
3. C. J. Long, J. Hattrick-Simpers, M. Murakami, R. C. Srivastava, I. Takeuchi, V. L. Karen, and X. Li, Rev. Sci. Instrum., 78, 072217 (2007). Rapid Structural Mapping of Ternary Metallic Alloy Systems Using the Combinatorial Approach and Cluster Analysis.
4. G. Hautier, C. C. Fischer, A. Jain, T. Mueller, and G. Ceder, Chem. Mater., 22, 3762 (2010). Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory.
5. D. Morgan, S. Curtarolo, K. Persson, J. Rodgers, and G. Ceder, Phys. Rev. Lett., 91, 135503 (2003). Predicting Crystal Structures with Data Mining of Quantum Calculations.
6. G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, Sci. Rep., 3, 1 (2013). Accelerating Materials Property Predictions Using Machine Learning.
7. K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. von Lilienfeld, A. Tkatchenko, and K.-R. Müller, J. Chem. Theory Comput., 9, 3404 (2013). Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
8. K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, J. Phys. Chem. Lett., 6, 2326 (2015). Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.
9. T. D. Huan, A. Mannodi-Kanakkithodi, and R. Ramprasad, Phys. Rev. B, 92, 014106 (2015). Accelerated Materials Property Predictions and Design Using Motif-Based Fingerprints.
10. T. Morawietz and J. Behler, J. Phys. Chem. A, 117, 7356 (2013). A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections.
11. J. Behler, Phys. Chem. Chem. Phys., 13, 17930 (2011). Neural Network Potential-Energy Surfaces in Chemistry: A Tool for Large-Scale Simulations.
12. A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Phys. Rev. Lett., 104, 136403 (2010). Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, Without the Electrons.
13. V. Botu and R. Ramprasad, Phys. Rev. B, 92, 094306 (2015). Learning Scheme to Predict Atomic Forces and Accelerate Materials Simulations.
14. V. Botu and R. Ramprasad, Int. J. Quantum Chem., 115, 1074 (2015). Adaptive Machine Learning Framework to Accelerate Ab Initio Molecular Dynamics.
15. J. C. Snyder, M. Rupp, K. Hansen, K.-R. Müller, and K. Burke, Phys. Rev. Lett., 108, 253002 (2012). Finding Density Functionals with Machine Learning.
16. A. G. Kusne, T. Gao, A. Mehta, L. Ke, M. C. Nguyen, K.-M. Ho, V. P. Antropov, C.-Z. Wang, M. J. Kramer, C. J. Long, and I. Takeuchi, Sci. Rep., 4, Article number: 6367 (2014). On-the-Fly Machine-Learning for High-Throughput Experiments: Search for Rare-Earth Free Permanent Magnets.
17. H. K. D. H. Bhadeshia, Stat. Anal. Data Min., 1, 296 (2009). Neural Networks and Information in Materials Science.
18. T. Bayes and R. Price, Philos. Trans. (1683-1775), 53, 370 (1763). An Essay Towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S.
19. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems, John Wiley & Sons, Washington, DC, 1977.
20. R. E. Kass and L. Wasserman, J. Am. Stat. Assoc., 91, 1343 (1996). The Selection of Prior Distributions by Formal Rules.
21. E. T. Jaynes, Phys. Rev., 106, 620 (1957). Information Theory and Statistical Mechanics.
22. G. Schwarz, Ann. Stat., 6, 461 (1978). Estimating the Dimension of a Model.
23. H. Akaike, IEEE Trans. Autom. Control, 19, 716 (1974). A New Look at the Statistical Model Identification.
24. J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, John Wiley & Sons, Inc., Hoboken, NJ, 2005.
25. R. Horst, P. M. Pardalos, and N. V. Thoai, Introduction to Global Optimization, Kluwer Academic Publishers, Dordrecht, the Netherlands, 2000.
26. R. Fletcher, Practical Methods of Optimization, John Wiley & Sons, Ltd, Chichester, 2013.
27. I. Guyon, G. Cawley, G. Dror, and V. Lemaire, Results of the Active Learning Challenge, in JMLR: Workshop and Conference Proceedings, 2011, vol 16, pp. 19-45. Workshop on Active Learning and Experimental Design.
28. B. Settles, University of Wisconsin, Madison, (2010). Active Learning Literature Survey.
29. D. C. Montgomery, Design and Analysis of Experiments, 8th Edition, John Wiley & Sons, Inc., New York, 2012.
30. D. D. Lewis and W. A. Gale, in Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Springer-Verlag, Inc., New York, 1994. A Sequential Algorithm for Training Text Classifiers.
31. H. S. Seung, M. Opper, and H. Sompolinsky, in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM, New York, 1992. Query by Committee.
32. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, 2009.
33. H. Chernoff, Ann. Math. Stat., 24, 586 (1953). Locally Optimal Designs for Estimating Parameters.
34. A. Wald, Ann. Math. Stat., 14, 134 (1943). On the Efficient Design of Statistical Investigations.
35. J. Kiefer and J. Wolfowitz, Ann. Math. Stat., 30, 271 (1959). Optimum Designs in Regression Problems.
36. E. A. Rady, M. M. E. Abd El-Monsef, and M. M. Seyam, InterStat, 247, 1 (2009). Relationships Among Several Optimality Criteria.
37. K. Chaloner and I. Verdinelli, Stat. Sci., 10, 273 (1995). Bayesian Experimental Design:A Review.
38. A. DasGupta, in Handbook of Statistics, S. Ghosh and C. R. Rao, Editors, Elsevier, Amsterdam, 1996, pp. 1099-1147. Review of Optimal Bayes Designs.
39. A. K. Kurtz, Pers. Psychol., 1, 41 (1948). A Research Test of the Rorschach Test.
40. C. I. Mosier, Educ. Psychol. Meas., 11, 5 (1951). The Need and Means of Cross Validation. I. Problems and Designs of Cross-Validation.
41. S. Geisser, J. Am. Stat. Assoc., 70, 320 (1975). The Predictive Sample Reuse Method with Applications.
42. M. Stone, J. R. Stat. Soc. Ser. B Methodol., 36, 111 (1974). Cross-Validatory Choice and Assessment of Statistical Predictions.
43. B. Efron, Ann. Stat., 7, 1 (1979). Bootstrap Methods: Another Look at the Jackknife.
44. B. Efron and R. Tibshirani, J. Am. Stat. Assoc., 92, 548 (1997). Improvements on Cross-Validation: The .632+ Bootstrap Method.
45. B. Efron, J. Am. Stat. Assoc., 99, 619-642 (2004). The Estimation of Prediction Error:Covariance Penalties and Cross-Validation [With comments by P. Burman, L. Denby, J. M. Landwehr, C. L. Mallows, X. Shen, H.-C. Huang, J. Ye, and C. Zhang].
46. R. Kohavi, in IJCAI'95 Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1995, vol 2, pp. 1137-1143. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
47. M. W. Browne, J. Math. Psychol., 44, 108 (2000). Cross-Validation Methods.
48. S. Arlot and A. Celisse, Stat. Surv., 4, 40 (2010). A Survey of Cross-Validation Procedures for Model Selection.
49. B. Efron and G. Gong, Am. Stat., 37, 36 (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation.
50. S. Borra and A. Di Ciaccio, Comput. Stat. Data Anal., 54, 2976 (2010). Measuring the Prediction Error. A Comparison of Cross-Validation, Bootstrap and Covariance Penalty Methods.
51. A. E. Hoerl and R. W. Kennard, Technometrics, 12, 55 (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems.
52. R. Tibshirani, J. R. Stat. Soc. Ser. B Methodol., 58, 267 (1996). Regression Shrinkage and Selection via the Lasso.
53. D. L. Donoho, IEEE Trans. Inf. Theory, 52, 1289 (2006). Compressed Sensing.
54. T. Goldstein and S. Osher, SIAM J. Imaging Sci., 2, 323 (2009). The Split Bregman Method for L1-Regularized Problems.
55. C. Cortes and V. Vapnik, Mach. Learn., 20, 273 (1995). Support-Vector Networks.
56. C. J. C. Burges, Data Min. Knowl. Disc., 2, 121 (1998). A Tutorial on Support Vector Machines for Pattern Recognition.
57. R. G. Brereton and G. R. Lloyd, Analyst, 135, 230 (2010). Support Vector Machines for Classification and Regression.
58. P. Wolfe, Econometrica, 27, 382 (1959). The Simplex Method for Quadratic Programming.
59. M. Frank and P. Wolfe, Nav. Res. Logist. Q., 3, 95 (1956). An Algorithm for Quadratic Programming.
60. J. A. K. Suykens and J. Vandewalle, Neural Process. Lett., 9, 293 (1999). Least Squares Support Vector Machine Classifiers.
61. M. Aizerman, E. Braverman, and L. Rozoner, Autom. Remote Control, 25, 821 (1964). Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning.
62. J. Mercer, Philos. Trans. R. S. Lond. A, 209, 415 (1909). Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations.
63. N. Aronszajn, Trans. Am. Math. Soc., 68, 337 (1950). Theory of Reproducing Kernels.
64. B. Schölkopf, R. Herbrich, and A. J. Smola, in Computational Learning Theory, Springer-Verlag, Berlin, 2001, pp. 416. A generalized Representer Theorem.
65. G. S. Kimeldore and G. Wahba, Ann. Math. Stat., 41, 495 (1970). A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines.
66. C. Saunders, A. Gammerman, and V. Vovk, in (ICML-1998) Proceedings of the 15th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1998, pp. 515-521. Ridge Regression Learning Algorithm in Dual Variables.
67. W. S. McCulloch and W. Pitts, Bull. Math. Biophys., 5, 115 (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity.
68. F. Rosenblatt, Psychol. Rev., 65, 386 (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.
69. P. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Harvard University, Cambridge, MA, 1974.
70. K.-I. Funahashi, Neural Netw., 2, 183 (1989). On the Approximate Realization of Continuous Mappings by Neural Networks.
71. D. F. Specht, IEEE Trans. Neural Netw., 2, 568 (1991). A General Regression Neural Network.
72. B. Widrow and M. A. Lehr, Proc. IEEE, 78, 1415 (1990). 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation.
73. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, Upper Saddle River, NJ, 1998.
74. C. Kingsford and S. L. Salzberg, Nat. Biotechnol., 26, 1011 (2008). What Are Decision Trees?.
75. S. K. Murthy, Data Min. Knowl. Disc., 2, 345 (1998). Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey.
76. S. R. Safavian and D. Landgrebe, IEEE Trans. Syst. Man Cybern., 21, 660 (1991). A Survey of Decision Tree Classifier Methodology.
77. W.-Y. Loh, Wiley Interdiscip. Rev. Data Min. Knowl. Disc., 1, 14 (2011). Classification and Regression Trees.
78. E. W. Bucholz, C. S. Kong, K. R. Marchman, W. G. Sawyer, S. R. Phillpot, S. B. Sinnott, and K. Rajan, Tribol. Lett., 47, 211 (2012). Data-Driven Model for Estimation of Friction Coefficient via Informatics Methods.
79. D. A. Carr, M. Lach-Hab, S. J. Yang, I. I. Vaisman, and E. Blaisten-Barojas, Microporous Mesoporous Mater., 117, 339 (2009). Machine Learning Approach for Structure-Based Zeolite Classification.
80. L. Hyafil and R. L. Rivest, Inf. Process. Lett., 5, 15 (1976). Constructing Optimal Binary Decision Trees Is NP-Complete.
81. J. Ross Quinlan, Mach. Learn., 1, 81 (1986). Induction of Decision Trees.
82. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, CRC Press, Boca Raton, FL, 1984.
83. Y. Wang and I. H. Witten, in Proceedings of the Ninth European Conference on Machine Learning, Springer-Verlag, London, 1997, pp. 128. Inducing Model Trees for Continuous Classes.
84. J. Mingers, Mach. Learn., 4, 227 (1989). An Empirical Comparison of Pruning Methods for Decision Tree Induction.
85. J. R. Quinlan, Int. J. Man Mach. Stud., 27, 221 (1987). Simplifying Decision Trees.
86. L. Breiman, Mach. Learn., 45, 5 (2001). Random Forests.
87. R. Polikar, IEEE Circuits Syst. Mag., 6, 21 (2006). Ensemble Based Systems in Decision Making.
88. L. Rokach, Artif. Intell. Rev., 33, 1 (2010). Ensemble-Based Classifiers.
89. R. Maclin and D. Opitz, arXiv preprint arXiv:1106.0257 (2011). Popular Ensemble Methods: An Empirical Study.
90. L. Breiman, Mach. Learn., 24, 123 (1996). Bagging Predictors.
91. R. E. Schapire, Mach. Learn., 5, 197 (1990). The Strength of Weak Learnability.
92. Y. Freund and R. E. Schapire, J. Comput. Syst. Sci., 55, 119 (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.
93. P. M. Long and R. A. Servedio, Mach. Learn., 78, 287 (2010). Random Classification Noise Defeats All Convex Potential Boosters.
94. N. L. Cramer, in Proceedings of the First International Conference on Genetic Algorithms, L. Erlbaum Associates Inc., Hillsdale, NJ, 1985, pp. 183. A Representation for the Adaptive Generation of Simple Sequential Programs.
95. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
96. J. R. Koza, in IJCAI'89 Proceedings of the 11th International Joint Conference on Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1989, vol 1, pp. 768. Hierarchical Genetic Algorithms Operating on Populations of Computer Programs.
97. B.-T. Zhang and H. Mühlenbein, Evol. Comput., 3, 17 (1995). Balancing Accuracy and Parsimony in Genetic Programming.
98. K. Rodriguez-Vazquez, C. M. Fonseca, and P. J. Fleming, in Late Breaking Papers at the 1997 Genetic Programming Conference, Morgan Kaufmann Publishers, San Francisco, 1997. Multiobjective Genetic Programming: a Nonlinear System Identification Application.
99. A. H. Gandomi, A. H. Alavi, and M. G. Sahab, Mater. Struct., 43, 963 (2010). New Formulation for Compressive Strength of CFRP Confined Concrete Cylinders Using Linear Genetic Programming.
100. E. Ozbay, M. Gesoglu, and E. Guneyisi, Construct. Build Mater., 22, 1831 (2008). Empirical Modeling of Fresh and Hardened Properties of Self-Compacting Concretes by Genetic Programming.
101. A. Baykasoglu, T. Dereli, and S. Tanis, Cem. Concr. Res., 34, 2083 (2004). Prediction of Cement Strength Using Soft Computing Techniques.
102. A. H. Alavi, M. Ameri, A. H. Gandomi, and M. R. Mirzahosseini, Construct. Build Mater., 25, 1338 (2011). Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method.
103. M. Kovacic, P. Uratnik, M. Brezocnik, and R. Turk, Mater. Manuf. Processes, 22, 634 (2007). Prediction of the Bending Capability of Rolled Metal Sheet by Genetic Programming.
104. M. Brezocnik, M. Kovacic, and M. Ficko, J. Mater. Process. Technol., 157, (2004). Prediction of Surface Roughness with Genetic Programming.
105. M. Brezocnik and M. Kovacic, Mater. Manuf. Processes, 18, 475 (2003). Integrated Genetic Programming and Genetic Algorithm Approach to Predict Surface Roughness.
106. T. Mueller, E. Johlin, and J. C. Grossman, Phys. Rev. B, 89, 115 (2014). Origins of Hole Traps in Hydrogenated Nanocrystalline and Amorphous Silicon Revealed Through Machine Learning.
107. R. Poli, W. B. Langdon, N. F. McPhee, and J. R. Koza, A Field Guide to Genetic Programming, Lulu.com, 2008.
108. I. J. Good, Philos. Sci., 50, 283 (1983). The Philosophy of Exploratory Data Analysis.
109. L. A. Baumes, M. Moliner, and A. Corma, Chem. Eur. J., 15, 4258 (2009). Design of a Full-Profile-Matching Solution for High-Throughput Analysis of Multiphase Samples Through Powder X-Ray Diffraction.
110. S. Kullback, Information Theory and Statistics, Courier Dover Publications, Dover, 2012.
111. S. Kullback and R. A. Leibler, Ann. Math. Stat., 22, 79 (1951). On Information and Sufficiency.
112. H. Jeffreys, Proc. R. Soc. Lond. A Math. Phys. Sci., 186, 453(1946). An Invariant Form for the Prior Probability in Estimation Problems.
113. J. Puzicha, T. Hofmann, and J. M. Buhmann, in Computer Vision and Pattern Recognition, 1997. Proceedings, 1997 IEEE Computer Society Conference on, IEEE, New York, 1997. Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval.
114. D. Comaniciu, V. Ramesh, and P. Meer, IEEE Trans. Pattern Anal. Mach. Intell., 25, 564 (2003). Kernel-Based Object Tracking.
115. P. G. Callahan, J. P. Simmons, and M. De Graef, Model. Simul. Mater. Sci. Eng., 21, 015003 (2013). A Quantitative Description of the Morphological Aspects of Materials Structures Suitable for Quantitative Comparisons of 3D Microstructures.
116. C. W. Niblack, R. Barber, W. Equitz, M. D. Flickner, E. H. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin, in IS&T/SPIE's Symposium on Electronic Imaging:Science and Technology. International Society for Optics and Photonics, Bellingham WA, 1993. QBIC Project: Querying Images by Content, Using Color, Texture, and Shape.
117. H. Sakoe and S. Chiba, IEEE Trans. Acoust. Speech Signal Process., 26, 43 (1978). Dynamic Programming Algorithm Optimization for Spoken Word Recognition.
118. Y. Rubner, C. Tomasi, and L. J. Guibas, in Sixth International Conference on Computer Vision. IEEE, New York, 1998, pp. 59-66. A Metric for Distributions with Applications to Image Databases.
119. Y. Rubner, C. Tomasi, and L. J. Guibas, Int. J. Comput. Vis., 40, 99 (2000). The Earth Mover's Distance as a Metric for Image Retrieval.
120. G. Al-Naymat, S. Chawla, and J. Taheri, in Proceedings of the Eighth Australasian Data Mining Conference. Australian Computer Society, Inc., Sydney, 2009, vol 101, pp. 117. SparseDTW: a Novel Approach to Speed up Dynamic Time Warping.
121. S. Salvador and P. Chan, Intell. Anal., 11, 561 (2007). Toward Accurate Dynamic Time Warping in Linear Time and Space.
122. O. Pele and M. Werman, in 2009 IEEE 12th International Conference on Computer Vision, IEEE, New York, 2009, pp. 460. Fast and Robust Earth Mover's Distances.
123. D. Comaniciu and P. Meer, IEEE Trans. Pattern Anal. Mach. Intell., 24, 603 (2002). Mean Shift: A Robust Approach Toward Feature Space Analysis.
124. P. Macnaughton-Smith, W. T. Williams, M. B. Dale, and L. G. Mockett, Nature, 202, 1034 (1964). Dissimilarity Analysis: A New Technique of Hierarchical Sub-Division.
125. U. Von Luxburg, Clustering Stability, Now Publishers Inc, Hanover, MA, 2010.
126. C. Goutte, L. K. Hansen, M. G. Liptrot, and E. Rostrup, Hum. Brain Mapp., 13, 165 (2001). Feature-Space Clustering for fMRI Meta-Analysis.
127. C. A. Sugar and G. M. James, J. Am. Stat. Assoc., 98, 750 (2003). Finding the Number of Clusters in a Dataset.
128. A. Niederliński and Wydawnictwo Pracownia Komputerowa Jacka Skalmierskiego, A Quick and Gentle Guide to Constraint Logic Programming via ECLiPSe, Jacek Skalmierski Computer Studio, Gliwice, 2011.
129. Y. K. Yoo, Q. Xue, Y. S. Chu, S. Xu, U. Hangen, H.-C. Lee, W. Stein, and X.-D. Xiang, Intermetallics, 14, 241 (2006). Identification of Amorphous Phases in the Fe-Ni-Co Ternary Alloy System Using Continuous Phase Diagram Material Chips.
130. A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-Time Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.
131. J. S. Lim and A. V. Oppenheim, Advanced Topics in Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1987.
132. J. P. MacSleyne, J. P. Simmons, and M. De Graef, Acta Mater., 56, 427 (2008). On the Use of 2-D Moment Invariants for the Automated Classification of Particle Shapes.
133. M. L. Green, I. Takeuchi, and J. R. Hattrick-Simpers, J. Appl. Phys., 113, 231101 (2013). Applications of High Throughput (Combinatorial) Methodologies to Electronic, Magnetic, Optical, and Energy-Related Materials.
134. C. J. Long, D. Bunker, X. Li, V. L. Karen, and I. Takeuchi, Rev. Sci. Instrum., 80, 103902 (2009). Rapid Identification of Structural Phases in Combinatorial Thin-Film Libraries Using x-Ray Diffraction and Non-negative Matrix Factorization.
135. R. LeBras, T. Damoulas, J. M. Gregoire, A. Sabharwal, C. P. Gomes, and R. Bruce Van Dover, in Principles and Practice of Constraint Programming-CP 2011, Springer, Berlin, 2011, pp. 508-522. Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling.
136. M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, Phys. Rev. Lett., 108, 058301 (2012). Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning.
137. W. Kohn and L. J. Sham, Phys. Rev., 140, A1133 (1965). Self-Consistent Equations Including Exchange and Correlation Effects.
138. P. Hohenberg and W. Kohn, Phys. Rev., 136, 864 (1964). Inhomogeneous Electron Gas.
139. W. Sidney, J. Chem. Educ., 42, 502 (1965). Benson, III-Bond Energies.
140. J. J. P. Stewart, J. Mol. Model., 13, 1173 (2007). Optimization of Parameters for Semiempirical Methods V: Modification of NDDO Approximations and Application to 70 Elements.
141. I. Torrens, Interatomic Potentials, Elsevier, Amsterdam, 2012.
142. J. Behler, J. Chem. Phys., 134, 074106 (2011). The Atom-Centered Symmetry Functions for Constructing High-Dimensional Neural Network Potentials.
143. J. Behler and M. Parrinello, Phys. Rev. Lett., 98, 146401 (2007). Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces.
144. S. M. Woodley and R. Catlow, Nat. Mater., 7, 937 (2008). Crystal Structure Prediction from First Principles.
145. J. Maddox, Nature, 335, 201 (1988). Crystals from First-Principles.
146. C. C. Fischer, K. J. Tibbetts, D. Morgan, and G. Ceder, Nat. Mater., 5, 641 (2006). Predicting Crystal Structure by Merging Data Mining with Quantum Mechanics.
147. Fiz Karlsruhe, Inorganic crystal structure database (http://www.fiz-karlsruhe.de/icsd.html, accessed October 9, 2015).
148. K. Burke, J. Chem. Phys., 136, 150901 (2012). Perspective on Density Functional Theory.
149. E. Ising, Zeitschrift für Physik, 31, 253 (1925). Beitrag zur Theorie des Ferromagnetismus.
150. J. M. Sanchez, F. Ducastelle, and D. Gratias, Physica, 128A, 334 (1984). Generalized Cluster Description of Multicomponent Systems.
151. A. van de Walle and G. Ceder, J. Phase Equilib., 23, 348 (2002). Automating First-Principles Phase Diagram Calculations.
152. R. Drautz and A. Diaz-Ortiz, Phys. Rev. B, 73, 224207 (2006). Obtaining Cluster Expansion Coefficients in Ab Initio Thermodynamics of Multicomponent Lattice-Gas Systems.
153. D. B. Laks, L. G. Ferreira, S. Froyen, and A. Zunger, Phys. Rev. B, 46, 12587 (1992). Efficient Cluster Expansion for Substitutional Systems.
154. V. Blum and A. Zunger, Phys. Rev. B, 70, 115108 (2004). Mixed-Basis Cluster Expansion for Thermodynamics of bcc Alloys.
155. T. Mueller and G. Ceder, Phys. Rev. B, 80, 024103 (2009). Bayesian Approach to Cluster Expansions.
156. T. Mueller, Phys. Rev. B, 86, 144201 (2012). Ab Initio Determination of Structure-Property Relationships in Alloy Nanoparticles.
157. L. J. Nelson, G. L. W. Hart, F. Zhou, and V. Ozolins, Phys. Rev. B, 87, 035125 (2013). Compressive Sensing as a Paradigm for Building Physics Models.
158. T. Mueller, in Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 2007, p. 199. Computational Studies of Hydrogen Storage Materials and the Development of Related Methods.
159. L. J. Nelson, V. Ozoliņš, C. Shane Reese, F. Zhou, and G. L. W. Hart, Phys. Rev. B, 88, 155105 (2013). Cluster Expansion Made Easy with Bayesian Compressive Sensing.
160. A. Zunger, S. H. Wei, L. G. Ferreira, and J. E. Bernard, Phys. Rev. Lett., 65, 353-356 (1990). Special Quasirandom Structures.
161. T. Mueller and G. Ceder, Phys. Rev. B, 82, 184107 (2010). Exact Expressions for Structure Selection in Cluster Expansions.
162. A. Seko, Y. Koyama, and I. Tanaka, Phys. Rev. B, 80, 165122 (2009). Cluster Expansion Method for Multicomponent Systems Based on Optimal Selection of Structures for Density-Functional Theory Calculations.
163. A. Seko and I. Tanaka, Phys. Rev. B, 83, 224111 (2011). Grouping of Structures for Cluster Expansion of Multicomponent Systems with Controlled Accuracy.
164. Y. C. Lin, J. Zhang, and J. Zhong, Comput. Mater. Sci., 43, 752 (2008). Application of Neural Networks to Predict the Elevated Temperature Flow Behavior of a Low Alloy Steel.
165. R. K. Jain, V. K. Jain, and P. K. Kalra, Wear, 231, 242 (1999). Modelling of Abrasive Flow Machining Process: A Neural Network Approach.
166. T. Sourmail, H. K. D. H. Bhadeshia, and D. J. C. MacKay, Mater. Sci. Technol., 18, 655 (2002). Neural Network Model of Creep Strength of Austenitic Stainless Steels.
167. M. A. Yescas, H. K. D. H. Bhadeshia, and D. J. MacKay, Mater. Sci. Eng. A, 311, 162 (2001). Estimation of the Amount of Retained Austenite in Austempered Ductile Irons Using Neural Networks.
168. E. A. Metzbower, J. J. DeLoach, S. H. Lalam, and H. K. D. H. Bhadeshia, Sci. Technol. Weld. Joining, 6, 116 (2001). Neural Network Analysis of Strength and Ductility of Welding Alloys for High Strength Low Alloy Shipbuilding Steels.
169. S. R. Kalidindi, S. R. Niezgoda, and A. A. Salem, JOM, 63, 34 (2011). Microstructure Informatics Using Higher-Order Statistics and Efficient Data-Mining Protocols.
170. V. H. C. De Albuquerque, A. R. De Alexandria, P. C. Cortez, and J. M. R. S. Tavares, NDT&E Int., 42, 644 (2009). Evaluation of Multilayer Perceptron and Self-Organizing Map Neural Network Topologies Applied on Microstructure Segmentation from Metallographic Images.
171. J. P. Simmons, P. Chuang, M. Comer, J. E. Spowart, M. D. Uchic, and M. De Graef, Model. Simul. Mater. Sci. Eng., 17, 025002 (2009). Application and Further Development of Advanced Image Processing Algorithms for Automated Analysis of Serial Section Image Data.
172. J. Waggoner, Y. Zhou, J. Simmons, M. De Graef, and S. Wang, IEEE Trans. Image Process., 22(12), 5282 (2013). 3D Materials Image Segmentation by 2D Propagation: A Graph-Cut Approach Considering Homomorphism.
173. L. Huffman, J. Simmons, M. De Graef, and I. Pollak, in Proceedings of IEEE Statistical Signal Processing Workshop, 2011, pp. 28-30. Shape Priors for MAP Segmentation of Alloy Micrographs Using Graph Cuts.
174. D. T. Fullwood, S. R. Kalidindi, S. R. Niezgoda, A. Fast, and N. Hampson, Mater. Sci. Eng. A, 494, 68 (2008). Gradient-Based Microstructure Reconstructions from Distributions Using Fast Fourier Transforms.
175. K. Rajan, Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application, Butterworth-Heinemann, Waltham, MA, 2013.
176. E. Johlin, L. K. Wagner, T. Buonassisi, and J. C. Grossman, Phys. Rev. Lett., 110, 146805 (2013). Origins of Structural Hole Traps in Hydrogenated Amorphous Silicon.
177. V. Avrutin, N. Izyumskaya, and H. Morkoç, Superlattices Microstruct., 49, 337 (2011). Semiconductor Solar Cells: Recent Progress in Terrestrial Applications.
178. D. A. Drabold, Y. Li, B. Cai, and M. Zhang, Phys. Rev. B, 83, 045201 (2011). Urbach Tails of Amorphous Silicon.
179. Y. Pan, F. Inam, M. Zhang, and D. A. Drabold, Phys. Rev. Lett., 100, 206403 (2008). Atomistic Origin of Urbach Tails in Amorphous Silicon.
180. Y. Pan, M. Zhang, and D. A. Drabold, J. Non Cryst. Solids, 354, 3480 (2008). Topological and Topological-Electronic Correlations in Amorphous Silicon.
181. P. A. Fedders, D. A. Drabold, and S. Nakhmanson, Phys. Rev. B, 58, 15624 (1998). Theoretical Study on the Nature of Band-Tail States in Amorphous Si.