Machine learning using structural representations for discovery

Index

Abstract

This research explores the use of machine learning with structural representations to discover high-temperature superconductors. The vastness of compositional phase space makes exhaustive searching computationally expensive. This study uses machine learning models that differentiate structural polymorphisms under pressure, a crucial factor in high-temperature superconductivity. The developed representation predicts superconducting transition temperatures (Tc) with an R-squared value above 0.94.

Publisher

Physical Review B

Published On

Jan 26, 2023

Authors

Lazar Novakovic, Ashkan Salamat, Keith V Lawler

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