Issue No. 05 - Sept.-Oct. (2013 vol. 15)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCSE.2013.76
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
S. Srinivasan and K. Rajan, "Revisiting Computational Thermodynamics through Machine Learning of High-Dimensional Data," in Computing in Science & Engineering, vol. 15, no. 5, pp. 22-31, 2013.