Thermophysical and Mechanical Properties Prediction of Rear-earth High-entropy Pyrochlore Based on Deep-learning Potential

TL;DR

This paper develops a deep-learning-based interatomic potential to accurately predict thermophysical and mechanical properties of high-entropy pyrochlore oxides, overcoming limitations of traditional methods and enabling large-scale simulations.

Contribution

It introduces a deep potential model trained on ab initio data for high-entropy pyrochlore oxides, enhancing predictive accuracy and transferability for complex disordered systems.

Findings

Deep potential model accurately reproduces ab initio results.

Model demonstrates strong transferability to medium-entropy ceramics.

Enables large-scale molecular dynamics simulations of high-entropy materials.

Abstract

High-entropy pyrochlore oxides possess ultra-low thermal conductivity and excellent high-temperature phase stability, making them promising candidate for next-generation thermal barrier coating (TBC) materials. However, reliable predictive models for such complex and disordered systems remain challenging. Ab initio methods, although accurate in describing anharmonic phonon-phonon interactions, struggle to capture the strong inherent phonon-disorder scattering in high-entropy systems. Moreover, the limited simulation cell size, hundreds of atoms, cannot fully represent the configurational complexity of high-entropy phases. On the other hand, classical molecular dynamics (MD) simulations lack accurate and transferable interatomic potentials, particularly in multi-component systems like high-entropy ceramics. In this work, we employed Deep Potential Molecular Dynamics (DPMD) to predict the thermophysical and mechanical properties of rare-earth high-entropy pyrochlore oxide system. The deep-potential (DP) model is trained on a limited dataset from ab initio molecular dynamics (AIMD) calculations, enabling large-scale molecular dynamics simulations with on-the-fly potential evaluations. This model not only achieves high accuracy in reproducing ab initio results but also demonstrates strong generalizability, making it applicable to medium-entropy ceramics containing the same constituent elements. Our study successfully develops a deep potential model for rare-earth pyrochlore systems and demonstrates that the deep-learning-based potential method offers a powerful computational approach for designing high-entropy TBC materials.