GAP-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys

TL;DR

This paper introduces a graph-based alchemical perturbation DFT method combined with machine learning corrections to efficiently predict binding energies in high-entropy alloys, significantly reducing computational costs.

Contribution

It presents a novel hybrid approach that integrates APDFT with a graph-based Gaussian process regression to accurately and efficiently explore HEA catalytic surfaces.

Findings

APDFT accurately predicts binding energies with minimal cost

Graph-based correction reduces errors near binding sites

Method enables rapid screening of vast configurational spaces

Abstract

High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this challenge, robust methods must be developed to efficiently explore the configurational space of HEA catalysts. Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape HEAs. Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost, significantly accelerating configurational space sampling. However, APDFT errors increase substantially when permutations occur near binding sites. To address this issue, we developed a graph-based Gaussian process regression model to correct discrepancies between APDFT and conventional density functional theory values. Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV, requiring a handful of ab initio simulations.