Crystal structure prediction with machine learning-based element substitution

TL;DR

This paper introduces a machine learning-based method for crystal structure prediction that uses element substitution and a metric learning classifier to efficiently identify stable structures without extensive first-principles calculations.

Contribution

The authors develop a novel CSP approach utilizing metric learning to select template structures for element substitution, reducing reliance on expensive ab initio calculations.

Findings

Achieves 96.4% accuracy in isomorphism classification.

Effectively predicts stable crystal structures across diverse systems.

Reduces computational cost compared to traditional methods.

Abstract

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4\%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substation-based CSP is demonstrated for a wide variety of crystal systems.