  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.
