Work

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Year: 2024

Type: article

Abstract: 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 ABO3-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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ad7d5e

Authors: Ayana Ghosh, Saurabh Ghosh

Institutions: Oak Ridge National Laboratory, SRM Institute of Science and Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: U.S. Department of Energy, UT-Battelle