Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa$_2$Cu$_3$O$_{7-\delta}$

TL;DR

This paper develops and benchmarks four machine-learned interatomic potentials for YBCO, enabling accurate large-scale simulations of radiation damage and defect behavior crucial for superconducting applications.

Contribution

The study introduces four new ML interatomic potentials for YBCO, trained on DFT data, with detailed benchmarking to identify the most accurate and efficient models for radiation damage simulations.

Findings

MACE achieves highest accuracy among models

ACE and tabGAP offer good efficiency-accuracy balance

All models replicate DFT-level forces across diverse environments

Abstract

High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's superconductivity depends strongly on oxygen stoichiometry and defect structure, atomistic simulations can provide crucial insight into radiation-damage mechanisms and pathways to maintain material performance. In this work, we develop and benchmark four Machine-Learned Interatomic Potentials (MLPs) for YBCO: the Atomic Cluster Expansion (ACE), the Message-Passing Atomic Cluster Expansion (MACE), the Gaussian Approximation Potential (GAP), and the Tabulated Gaussian Approximation Potential (tabGAP), trained on an extensive Density Functional Theory (DFT) database explicitly designed to include irradiation-damaged-like configurations. The resulting models achieve DFT-level accuracy across a wide range of atomic environments, faithfully capturing the interatomic forces relevant to radiation damage processes. Among the tested models, MACE delivers the highest accuracy, although at greater computational cost, while ACE and tabGAP provide an excellent balance between efficiency and fidelity. These machine-learned potentials establish a robust foundation for large-scale molecular dynamics simulations of radiation-induced defect evolution in complex superconducting materials