Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory

Magnus Kløve, Sanna Sommer, Bo B. Iversen, Bjørk Hammer and Wilke Dononelli

Adv. Mater. 2023, 2208220

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases a reliable structural motif is needed as starting configuration for structure refinements. Here, an algorithm is introduced that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model that combines density functional theory (DFT) calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape. Due to the nature of this landscape, even metastable configurations and stacking disorder can be determined considering a single or multiple crystalline phases.

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