Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys

TL;DR

This paper introduces an automated machine-learning approach using moment tensor potentials to accurately and efficiently compute stacking fault energies in random alloys, enabling large-scale simulations with DFT-level precision.

Contribution

The authors develop a novel algorithm for training moment tensor potentials on random alloys, incorporating active learning to reduce training data and accurately reproduce DFT results.

Findings

Successfully validated on MoNbTa alloy for stacking fault energies.

Outperforms traditional methods like CPA and SQS in accounting for relaxation.

Enables large-scale, accurate simulations of complex alloy properties.

Abstract

We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ~10000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ~100 most distinct configurations - for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stacking fault energy over the entire compositional space up to a few percent. Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent systems.