A new approach is presented to obtain candidate structures from atomic pair distribution function (PDF) data in a highly automated way. It fetches, from web-based structural databases, all the structures meeting the experimenter's search criteria and performs structure refinements on them without human intervention. It supports both x-ray and neutron PDFs. Tests on various material systems show the effectiveness and robustness of the algorithm in finding the correct atomic crystal structure. It works on crystalline and nanocrystalline materials including complex oxide nanoparticles and nanowires, low-symmetry and locally distorted structures, and complicated doped and magnetic materials. This approach could greatly reduce the traditional structure searching work and enable the possibility of high-throughput real-time auto analysis PDF experiments in the future.
@article{arxiv.1905.02677,
title = {Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models},
author = {Long Yang and Pavol Juhás and Maxwell W. Terban and Matthew G. Tucker and Simon J. L. Billinge},
journal= {arXiv preprint arXiv:1905.02677},
year = {2020}
}