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Machine learning using structural representations for discovery of high temperature superconductors

Physics

Machine learning using structural representations for discovery of high temperature superconductors

L. Novakovic, A. Salamat, et al.

This research conducted by Lazar Novakovic, Ashkan Salamat, and Keith V Lawler delves into the innovative application of machine learning to uncover high-temperature superconductors. Utilizing advanced structural representations to navigate the vast compositional phase space, the study highlights how pressure influences polymorphisms critical to superconductivity, achieving impressive accuracy in predicting transition temperatures.

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Playback language: English
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
Tags
machine learning
high-temperature superconductors
structural representations
polymorphisms
superconducting transition temperatures
computational efficiency
phase space

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