On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions

TL;DR

This paper develops and validates machine learning interatomic potentials for Al-Mg-Zr alloys, enabling accurate, efficient prediction of elastic properties across compositions, thus aiding alloy design.

Contribution

It introduces on-the-fly learning and neural network methods to create transferable MLIPs for elastic property modeling in Al-Mg-Zr alloys.

Findings

MLIPs predict elastic properties within a few GPa of experiments.

The methods significantly reduce computational costs compared to QM.

The potentials enable systematic exploration of alloy phase space.

Abstract

The development of resilient and lightweight Aluminum alloys is central to advancing structural materials for energy-efficient engineering applications. To address this challenge, in this study, we explore the elastic properties of Al-Mg-Zr solid solutions by integrating advanced machine learning (ML) techniques with quantum-mechanical (QM) atomistic simulations. For this purpose, we develop accurate and transferable machine-learned interatomic potentials (MLIPs) using two complementary approaches: (i) an on-the-fly learning scheme combined with Bayesian linear regression during ab initio molecular dynamics simulations, and (ii) the equivariant neural network architecture MACE. Both MLIPs facilitate the prediction of composition-dependent elastic properties while drastically reducing the computational cost compared to conventional QM methods. Comparison with ultrasonic measurements shows that the deviation between simulation and experiment remains within a few GPa across all Al-Mg-Zr systems investigated. These potentials also enable the systematic exploration of the Al-Mg-Zr solid solution phase space and provide insights into the elastic behavior as a function of alloying element concentration. Hence, our findings demonstrate the reliability and transferability of the parameterized on-the-fly MLIPs, making them valuable for accelerating the design of Al alloys with tailored physicomechanical properties in complex compositional spaces. While the present study focuses on homogeneous phases, it establishes a foundation for future multiscale simulations that include microstructural features such as precipitates and grain boundaries.