AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning

arXiv:2404.04810v1 Announce Type: cross
Abstract: 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 …

abstract arxiv computational cond-mat.mtrl-sci cs.lg csp deep learning discovery functional however impact material materials matrix novel prediction scale type