Mapping causal pathways with structural modes fingerprint for perovskite oxides

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 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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Publication Details

Journal
Machine Learning Science and Technology
Published
2024-09-19
DOI
https://doi.org/10.1088/2632-2153/ad7d5e
Citations
5
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.59

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article

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Ayana Ghosh, Saurabh Ghosh
5 citations
Machine Learning Science and Technology
Machine Learning in Materials Science
0.59
article

Mapping causal pathways with structural modes fingerprint for perovskite oxides

Ayana Ghosh, Saurabh Ghosh
article en
5 citations

Abstract

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 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.

Machine Learning Science and TechnologyVol. 5(4)
Oak Ridge National Laboratory (US), SRM Institute of Science and Technology (IN)
U.S. Department of Energy, UT-Battelle
Openalex Percentile: Top 34%
Machine Learning in Materials Science
0.59
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Mapping causal pathways with structural modes fingerprint for perovskite oxides — Ayana Ghosh, Saurabh Ghosh · Machine Learning Science and Technology (2024) | TGRS Research Map | TGRS