oai:arXiv.org:2404.04810
Computer Science
2024
4/10/2024
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials.
However, predicting the crystal structure solely from a material's composition or formula is a promising yet challenging task, as traditional ab initio crystal structure prediction (CSP) methods rely on time-consuming global searches and first-principles free energy calculations.
Inspired by the recent success of deep learning approaches in protein structure prediction, which utilize pairwise amino acid interactions to describe 3D structures, we present AlphaCrystal-II, a novel knowledge-based solution that exploits the abundant inter-atomic interaction patterns found in existing known crystal structures.
AlphaCrystal-II predicts the atomic distance matrix of a target crystal material and employs this matrix to reconstruct its 3D crystal structure.
By leveraging the wealth of inter-atomic relationships of known crystal structures, our approach demonstrates remarkable effectiveness and reliability in structure prediction through comprehensive experiments.
This work highlights the potential of data-driven methods in accelerating the discovery and design of new materials with tailored properties.
;Comment: 16 pages
Song, Yuqi,Dong, Rongzhi,Wei, Lai,Li, Qin,Hu, Jianjun, 2024, AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning