On generating Special Quasirandom Structures: Optimization for the DFT computational efficiency

TL;DR

This paper introduces an evolutionary algorithm for generating Special Quasirandom Structures that significantly reduces DFT computational costs by optimizing supercell efficiency, with minimal loss in disorder accuracy.

Contribution

The paper presents a novel evolutionary algorithm that filters and optimizes SQS for DFT efficiency, outperforming standard methods in reducing computational complexity.

Findings

Achieves about five times fewer displacements for phonon calculations.

Maintains comparable correlation errors with standard tools.

Reduces computational cost with negligible impact on disorder accuracy.

Abstract

We present our novel evolutionary algorithm for generating Special Quasirandom Structures (SQS) designed to optimize the computational efficiency of Density Functional Theory (DFT) computations. Operating on the premise that symmetry proxies non-randomness, we rigorously filter out 1.P1 candidate structures prior to evaluating correlation functions. Our extinction-based workflow includes the seeding, filtration, evaluation, extinction, and repopulation phases to produce efficient supercells with maximal local environmental distinctness. We compare our results against those generated by established software packages, on the example of the W\textsubscript{70}Cr\textsubscript{30} alloy. Although standard tools achieve (marginally) lower correlation errors, our best-performing structures require approximately five times fewer unique displacements for phonon calculations. This approach sacrifices negligible quantitative disorder accuracy to significantly reduce the computational cost of modeling thermal properties.