AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
<|reference_start|>AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning: 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.<|reference_end|>
@article{song2024alphacrystal-ii:,
    title={AlphaCrystal-II: Distance matrix based crystal structure prediction
  using deep learning},
    author={Yuqi Song, Rongzhi Dong, Lai Wei, Qin Li, Jianjun Hu},
    journal={arXiv preprint arXiv:2404.04810},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04810},
    primaryClass={cond-mat.mtrl-sci cs.LG}
}