Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials

Jianjun Hu; Yong Zhao; Qin Li; Yuqi Song; Rongzhi Dong; Wenhui Yang; Edirisuriya M D Siriwardane

doi:10.1021/acsomega.3c02115

Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials

ACS Omega. 2023 Jul 11;8(29):26170-26179. doi: 10.1021/acsomega.3c02115. eCollection 2023 Jul 25.

Authors

Jianjun Hu¹, Yong Zhao¹, Qin Li², Yuqi Song¹, Rongzhi Dong¹, Wenhui Yang³, Edirisuriya M D Siriwardane⁴

Affiliations

¹ Department of Computer Science and Engineering, University of South Carolna, Columbia, South Carolina 29201, United States.
² College of Big Data and Statistics, Guizhou University of Finance and Economics, Guiyang 550050, China.
³ School of Mechanical Engineering, Guizhou University, Guiyang 550050, China.
⁴ Department of Physics, University of Colombo, Colombo 3 00300, Sri Lanka.

Abstract

Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first-principles free energy calculations to predict the ground-state crystal structure of a given material composition. These ab initio algorithms are currently too slow for predicting complex material structures. Inspired by the AlphaFold algorithm for protein structure prediction, herein, we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model for predicting the atomic contact map of a target material followed by three-dimensional (3D) structure reconstruction using genetic algorithms. Extensive experiments on 20 benchmark structures showed that our AlphaCrystal algorithm can predict structures close to the ground truth structures, which can significantly speed up the crystal structure prediction and handle relatively large systems.