Issue No.05 - Sept.-Oct. (2013 vol.15)
pp: 22-31
Srikant Srinivasan , Iowa State University
Krishna Rajan , Iowa State University
A new perspective on alloy thermodynamics computation uses data-driven analysis and machine learning for the design and discovery of materials. The focus is on an integrated machine-learning framework, coupling different genres of supervised and unsupervised informatics techniques, and bridging two distinct viewpoints: continuum representations based on solid solution thermodynamics and discrete high-dimensional elemental descriptions.
Informatics, Machine learning, Thermodynamics, Principal component analysis, Semiconductor materials, Atomic measurements, Computational modeling,computational thermodynamics, materials informatics, machine learning, data mining, compound semiconductors, high-dimensional model representation, bandgap engineering
Srikant Srinivasan, Krishna Rajan, "Revisiting Computational Thermodynamics through Machine Learning of High-Dimensional Data", Computing in Science & Engineering, vol.15, no. 5, pp. 22-31, Sept.-Oct. 2013, doi:10.1109/MCSE.2013.76
1. K. Rajan,“Materials Informatics,” Materials Today, vol. 8, no. 10, 2005, pp. 38-45.
2. K. Rajan,“Commentary—Materials Informatics—How Do We Go about Harnessing the ‘Big Data’ Paradigm,” Materials Today, vol. 15, no. 11, 2012, pp. 38-45.
3. P.V. Balachandran,S.R. Broderick,, and K. Rajan,“Identifying the ‘Inorganic Gene’ for High-Temperature Piezoelectric Perovskites through Statistical Learning,” Proc. Royal Soc. A: Math., Physical, and Eng. Sciences, vol. 467, no. 2132, 2011, pp. 2271-2290.
4. C. Suh and K. Rajan,“Combinatorial Design of Semiconductor Chemistry for Bandgap Engineering: ‘Virtual’ Combinatorial Experimentation,” Applied Surface Science, vol. 223, nos. 1-3, 2004, pp. 148-158.
5. J.A.D. Connolly,“Multivariable Phase Diagrams: An Algorithm Based on Generalized Thermodynamics,” Am. J. Science, vol. 290, no. 6, 1990, pp. 666-718.
6. S. Srinivasan and K. Rajan,“‘Property Phase Diagrams’ for Compound Semiconductors through Data Mining,” Materials, vol. 6, no. 1, 2013, pp. 279-290.
7. G. Li,C. Rosenthal,, and H. Rabitz,“High Dimensional Model Representations,” The J. Physical Chemistry A, vol. 105, no. 33, 2001, pp. 7765-7777.
8. A. Rajagopalan et al., “Secondary” Descriptor Development for Zeolite Framework Design: An Informatics Approach,” Applied Catalysis A: General, vol. 254, no. 1, 2003, pp. 147-160.
9. I.E. Frank and J.H. Friedman,“A Statistical View of Some Chemometrics Regression Tools,” Technometrics, vol. 35, no. 2, 1993, pp. 109-135.
10. I.M. Sobol,“Theorems and Examples on High-Dimensional Model Representation,” Reliability Engineering & System Safety, vol. 79, no. 2, 2003, pp. 187-193.
11. I.T. Joliffe and B.J. Morgan,“Principal Component Analysis and Exploratory Factor Analysis,” Statistical Methods Medical Research, vol. 1, no. 1, 1992, pp. 69-95.
12. P. Geladi and B.R. Kowalski,“Partial Least-Squares Regression—A Tutorial,” Analytica Chimica Acta, vol. 185, 1986, pp. 1-17.
13. K. Shim,H. Rabitz,, and P. Dutta,“Band Gap and Lattice Constant of ${\rm{G}}{{\rm{a}}_x}{\rm{I}}{{\rm{n}}_{1 - x}}{\rm{A}}{{\rm{s}}_y}{\rm{S}}{{\rm{b}}_{1 - y}}$,” J. Applied Physics, vol. 88, no. 12, 2000, pp. 7157-7161.
14. G.Y. Li and H. Rabitz,“General Formulation of HDMR Component Functions with Independent and Correlated Variables,” J. Mathematical Chemistry, vol. 50, no. 1, 2012, pp. 99-130.