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Issue No.05 - Sept.-Oct. (2013 vol.15)
pp: 22-31
Srikant Srinivasan , Iowa State University
Krishna Rajan , Iowa State University
ABSTRACT
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.
INDEX TERMS
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
CITATION
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
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