# Comparison between Density Functional Theory and Density Functional Tight Binding approaches for finding the muon stopping site in organic molecular crystals

## TL;DR

This study compares DFT and DFTB+ methods for locating muon stopping sites in organic crystals, demonstrating DFTB+'s efficiency and proposing a Python toolkit for standardized site-finding in muon spectroscopy.

## Contribution

It introduces a comparative analysis of DFT and DFTB+ for muon site prediction and presents a Python software suite for routine and standardized calculations.

## Key findings

- DFTB+ offers a faster alternative to DFT for muon site prediction.
- Both methods show comparable accuracy with certain limitations.
- The Python toolkit facilitates standardized and accessible computations.

## Abstract

Finding the possible stopping sites for muons inside a crystalline sample is a key problem of muon spectroscopy. In a previous work, we suggested a computational approach to this problem, using Density Functional Theory software in combination with a random structure searching approach using a Poisson sphere distribution. In this work we test this methodology further by applying it to three organic molecular crystals model systems: durene, bithiophene, and tetracyanoquinodimethane (TCNQ). Using the same sets of random structures we compare the performance of Density Functional Theory software CASTEP and the much faster lower level approximation of Density Functional Tight Binding provided by DFTB+, combined with the use of the 3ob-3-1 parameter set. We show the benefits and limitations of such an approach and we propose the use of DFTB+ as a viable alternative to more cumbersome simulations for routine site-finding in organic materials. Finally, we introduce the Muon Spectroscopy Computational Project software suite, a library of Python tools meant to make these methods standardized and easy to use.