A generalized method for refining and selecting random crystal structures using graph theory

Random generation of crystal structures is a key to the success of predicting unknown crystals. In this work, we introduce a general method for refining and selecting random structures that relies on minimal prior information. The method establishes a quotient graph from the random structure using a...

Full description

Saved in:
Bibliographic Details
Published inThe Journal of chemical physics Vol. 163; no. 9
Main Authors Yu, Shaobo, Wang, Junjie, Han, Yu, Jia, Qiuhan, Liang, Zhixin, Yang, Ziyang, Pan, Yujian, Gao, Hao, Sun, Jian
Format Journal Article
LanguageEnglish
Published United States 07.09.2025
Online AccessGet more information
ISSN1089-7690
DOI10.1063/5.0278803

Cover

More Information
Summary:Random generation of crystal structures is a key to the success of predicting unknown crystals. In this work, we introduce a general method for refining and selecting random structures that relies on minimal prior information. The method establishes a quotient graph from the random structure using a near-neighbor finding algorithm, which subsequently guides the refinement of the initial structure. To validate this approach, we apply it to nine distinct systems, and the outcomes indicate that it effectively yields a great number of low-energy structures. This technique could be integrated into most structure prediction algorithms to generate more sound initial structures, thereby expediting the search for ground state structures.
ISSN:1089-7690
DOI:10.1063/5.0278803