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

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Kløve, Magnus, Sommer, Sanna, Iversen, Bo B., Hammer, Bjørk, Dononelli, Wilke
Format: Preprint
Publié: 2022
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866912474811334656
author Kløve, Magnus
Sommer, Sanna
Iversen, Bo B.
Hammer, Bjørk
Dononelli, Wilke
author_facet Kløve, Magnus
Sommer, Sanna
Iversen, Bo B.
Hammer, Bjørk
Dononelli, Wilke
contents 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, we present an algorithm 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 can be determined.
format Preprint
id arxiv_https___arxiv_org_abs_2209_01358
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory
Kløve, Magnus
Sommer, Sanna
Iversen, Bo B.
Hammer, Bjørk
Dononelli, Wilke
Materials Science
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, we present an algorithm 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 can be determined.
title Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory
topic Materials Science
url https://arxiv.org/abs/2209.01358