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

Abstract

The stopping site of the muon in a muon-spin relaxation experiment (μ+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.

References in corpus (4)

• Interplay of Rare Earth and Iron magnetism in RFeAsO with R = La, Ce, Pr, and Sm: A muon spin relaxation study and symmetry analysis
• Characterizing the network topology of the energy landscapes of atomic clusters
• Quantum states of muons in fluorides
• Common effect of chemical and external pressures on the magnetic properties of RECoPO (RE = La, Pr)

Cited by in corpus (8)

• DFT+μ: Density Functional Theory for Muon Site Determination
• MuFinder: A program to determine and analyse muon stopping sites
• Muon spin rotation study of type-I superconductivity: elemental $\beta -$Sn
• Quantum effects in muon spin spectroscopy within the stochastic self-consistent harmonic approximation
• Computational Prediction of Muon Stopping Sites: a Novel Take on the Unperturbed Electrostatic Potential Method
• Comparison between Density Functional Theory and Density Functional Tight Binding approaches for finding the muon stopping site in organic molecular crystals
• Towards targeted kinetic trapping of organic-inorganic interfaces: A computational case study
• Automated computational workflows for muon spin spectroscopy