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Issue No. 05 - Sept.-Oct. (2013 vol. 15)
ISSN: 1521-9615
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
CITATION
Srikant Srinivasan, Krishna Rajan, "Revisiting Computational Thermodynamics through Machine Learning of High-Dimensional Data", Computing in Science & Engineering, vol. 15, no. , pp. 22-31, Sept.-Oct. 2013, doi:10.1109/MCSE.2013.76
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