Developing Machine Learning Force Fields of High Entropy Alloys with Deep Neural Networks

Computational simulation has become an invaluable tool in modern materials physics. Broadly, materials modeling falls into two categories: highly accurate but computationally intensive ab initio methods like density functional theory (DFT), and time-efficient but contextually limited methods such as classical molecular dynamics (MD). The need for a parameterized potential has long constrained the accuracy of classical MD. However, the recent rise of deep learning has opened a new path to combine quantum ab initio accuracy with classical MD efficiency. In this work, we investigate the ability to develop a DeePMD potential using DFT data to accurately model a wide range of metallic compounds—from binary alloys to emerging high-entropy alloys—under a single unified potential. The results offer insights and suggested practices for developing deep learning potentials in future studies of compositionally complex, multi-component materials.