Machine Learning: Science and Technology (Jan 2024)

Mapping causal pathways with structural modes fingerprint for perovskite oxides

  • Ayana Ghosh,
  • Saurabh Ghosh

DOI
https://doi.org/10.1088/2632-2153/ad7d5e
Journal volume & issue
Vol. 5, no. 4
p. 045014

Abstract

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Causality is innate to the determination of the fundamental mechanism controlling any physical phenomena. However, combining causality within the standard practices of computational modelling to understand structure-functionality connections is extremely rare. This work proposes a fingerprint based on key structural modes for ABO _3 -type perovskite oxides and its derivatives, combined with causal models, for predicting Kohn–Sham energies. Our study of causal models captures the inherent coupling between structural modes such as rotation, tilt and antiferroelectric displacements, responsible for phase transition, polarization, magnetization and metal–insulator transition, exhibited by these materials. Although developed for modelling specific functionality, this method is universally applicable to derive other functionalities and even different material classes while tracking hidden causal mechanisms via structural distortions.

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