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.
| Original language | English |
|---|---|
| Article number | 045014 |
| Journal | Machine Learning: Science and Technology |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1 2024 |
Funding
This research (AG) was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. ORNL is managed by UT-Battelle, LLC, for DOE under Contract No. DE-AC05-00OR22725. SG acknowledges funding from the National Supercomputing Mission (DST/NSM/R&D/HPC/Applications/Extension Grant/2023/18). SG acknowledges the supercomputing facility from \u2018PARAM PRAVEGA\u2019 at IISc Bangalore, Government of India. AG acknowledges Dr. Monirul Shaikh for helpful discussion.
Keywords
- causal
- fingerprints
- pathways
- perovskites
- structural modes