An Active Learning Interatomic Potential For Defect-Engineered CoCrFeMnNi High-Entropy Alloy

TL;DR

This paper develops a machine learning-based interatomic potential for the CoCrFeMnNi high-entropy alloy, enabling accurate and efficient simulations of defect structures and properties, with active learning to improve the model during non-equilibrium conditions.

Contribution

It introduces a Moment Tensor Potential trained via active learning for a high-entropy alloy, outperforming traditional potentials in accuracy and speed.

Findings

The MTP accurately predicts physical properties of the alloy.

Active learning enhances the potential's robustness during simulations.

The potential is integrated into LAMMPS for public use.

Abstract

High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen storage, and ocean engineering. However, a large compositional space remains to be explored. Unlike conventional approaches, computational methods have shown accelerated discovery of novel alloys in a short time. However, the lack of interatomic potentials have posed a challenge in discovering new alloy compositions and property measurements. In the present work, we have developed a Moment Tensor Potential (MTP) trained by Machine Learning based approach using the BFGS unconstrained optimization algorithm for the CoCrFeMnNi High-entropy alloy. Our training set consists of various defects induced configurations such as vacancies, dislocations and stacking-faults. An active learning scheme to re-train the potential was undertaken to dynamically to add training data upon encountering extrapolative configurations during non-equilibrium simulations. A thorough investigation of the error metrics, equation of state, uniaxial tensile deformation, nano-indentation and solid-liquid interface stability for this alloy was carried out, and it is seen that the MTP potential outperforms the popular Modified Embedded Atom Method (MEAM) potential on physical properties prediction. The accuracy and high computational speed are discussed using scaling performance. The potential is prepared for public use by embedding it into the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code.