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Revisiting Computational Thermodynamics through Machine Learning of High-Dimensional Data

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2 Author(s)

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.

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Computing in Science & Engineering  (Volume:15 ,  Issue: 5 )