Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space

TL;DR

This paper develops a neural network-based potential energy surface model for the Al-Cu-Mg alloy system, achieving first-principles accuracy and efficiency across all concentrations for atomistic simulations.

Contribution

It introduces a Deep Potential model trained with a novel generator scheme, accurately capturing the energetics and mechanical properties of the multi-component alloy system.

Findings

Model predictions align with first-principles calculations

Accurately describes binary and ternary alloy properties

Effective for atomistic simulations across full concentration range

Abstract

Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP), a neural network based representation of the PES, and DP Generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.