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

• URL: http://arxiv.org/abs/2404.04810v1
• Date: Sun, 7 Apr 2024 05:17:43 GMT
• Title: AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
• Authors: Yuqi Song, Rongzhi Dong, Lai Wei, Qin Li, Jianjun Hu,
• Abstract summary: We present AlphaCrystal-II, a novel knowledge-based solution that exploits the abundant inter-atomic interaction patterns found in existing crystal structures. By leveraging the wealth of inter-atomic relationships of known crystal structures, our approach demonstrates remarkable effectiveness and reliability in structure prediction.
• Score: 4.437756445215657
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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