MuAIRSS - muon-spectroscopy-computational-project/pymuon-suite GitHub Wiki

Intro

The problem of identifying the stopping site of a muon is often key in the interpretation of experimental results, and also one of those that computational tools are most commonly used to solve. Roughly, one can imagine that at the atomic level, muon spectroscopy experiments go something like this:

• the polarized muon beam is fired at the sample, and the muons travel a certain depth inside the target;
• through inelastic collisions with the atoms of the lattice, muons shed their kinetic energy, which turns into heat or causes small amounts of damage to the crystal, until it becomes low enough that it is comparable to their potential energy in the electrostatic fields generated by electrons and nuclei alike;
• at that point, Coulomb forces start driving the muon, and eventually, it loses as much energy as possible and comes to a stop in a position that represents a global, or at least local, minimum for its electrostatic potential energy, where it spends the rest of its time until it decays.

Where these minima of the potential actually are isn't always obvious. In some cases it's trivial, for example in organic molecules the mechanism is often the same as for regular hydrogenation. The muon, having captured an electron and formed a muonium pseudo-atom, ends up breaking a double or triple bond and attaching itself to the molecule by using one of the unpaired electrons. However in other, crystalline structures it can be a lot harder. While an atomistic simulation that accurately predicts the potential through which the atoms interact can be used identify possible sites, this approach is not fully reliable (for example when applied to fluorides). In order to avoid such limitations, one can use a brute force approach to explore all the possible positions in which the muon may end up. AIRSS is one such method.

AIRSS

Work in Progress