Computational Prediction of Muon Stopping Sites Using Ab Initio Random Structure Searching (AIRSS)

TL;DR

This paper introduces a first-principles computational method combining ab initio calculations, random structure searching, and machine learning to accurately predict muon stopping sites in crystalline materials, eliminating reliance on experimental data.

Contribution

The work presents a novel, purely theoretical approach for predicting muon stopping sites using ab initio methods, random structure searching, and machine learning, implemented in the Soprano Python library.

Findings

Successfully predicted muonium stopping sites in Si, Diamond, and Ge.

Accurately identified muonium stopping site in LiF.

Method does not require experimental input for predictions.

Abstract

The stopping site of the muon in a muon-spin relaxation experiment ({\mu}+SR) is in general unknown. There are some techniques that can be used to guess the muon stopping site, but they often rely on approximations and are not generally applicable to all cases. In this work, we propose a purely theoretical method to predict muon stopping sites in crystalline materials from first principles. The method is based on a combination of ab initio calculations, random structure searching and machine learning, and it has successfully predicted the MuT and MuBC stopping sites of muonium in Si, Diamond and Ge, as well as the muonium stopping site in LiF, without any recourse to experimental results. The method makes use of Soprano, a Python library developed to aid ab-initio computational crystallography, that was publicly released and contains all the software tools necessary to reproduce our analysis.