journal article·2021-09Open access

Rapid screening of high-throughput ground state predictions

Sayan Samanta, Axel van de Walle

Calphad· Volume 74

Abstract

High-through computational thermodynamic approaches are becoming an increasingly popular tool to uncover novel compounds. However, traditional methods tend to be limited to stability predictions of stoichiometric phases at absolute zero. Such methods thus carry the risk of identifying an excess of possible phases that do not survive to temperatures of practical relevance. We demonstrate how the Calphad formalism, informed by simple first-principles input can be simply used to overcome this problem at a low computational cost and deliver quantitatively useful phase diagram predictions at all temperatures. We illustrate the method by re-assessing prior compound formation predictions and reconcile these findings with long-standing experimental evidence to the contrary.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Machine Learning in Materials Science · Catalysis and Oxidation Reactions · Advanced Chemical Physics Studies

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