Universal interatomic potential for perovskite oxides
Jing Wu, Jiyuan Yang, Yuan-Jinsheng Liu, Duo Zhang, Yudi Yang, Yuzhi, Zhang, Linfeng Zhang, Shi Liu

TL;DR
This paper introduces a deep learning-based universal interatomic potential for perovskite oxides, enabling transferable and accurate molecular dynamics simulations across diverse compositions and phases, thus advancing computational materials discovery.
Contribution
A novel deep neural network model with self-attention that creates a universal force field for various perovskite oxides, improving transferability and simulation accuracy.
Findings
Accurately predicts phase transition sequences in ferroelectric oxides.
Successfully models solid solutions with multiple metal elements.
Provides publicly available training data and testing workflows.
Abstract
With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parameterized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parameterized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials…
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Taxonomy
TopicsMachine Learning in Materials Science · Ferroelectric and Piezoelectric Materials · Multiferroics and related materials
