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Revisiting Computational Thermodynamics through Machine Learning of High-Dimensional Data
Sept.-Oct. 2013 (vol. 15 no. 5)
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.
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 and Engineering, vol. 15, no. 5, pp. 22-31, Sept.-Oct. 2013, doi:10.1109/MCSE.2013.76
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