Physics guided deep learning for generative design of crystal materials with symmetry constraints

作     者：Yong Zhao Edirisuriya M.Dilanga Siriwardane Zhenyao Wu Nihang Fu Mohammed Al-Fahdi Ming Hu Jianjun Hu 

作者机构：Department of Computer Science and EngineeringUniversity of South Carolina550 Assembly St.29201 ColumbiaSCUSA Department of PhysicsUniversity of ColomboColombo 3Sri Lanka Department of Mechanical EngineeringUniversity of South Carolina301 Main Street29201 ColumbiaSCUSA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：1969-1980页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

基　　金：The research reported in this work was supported in part by National Science Foundation under the grant and 2110033 1940099 and 1905775.The views perspectives and content do not necessarily represent the official views of the NSF 

主　　题：symmetry crystal materials 

摘      要：Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventionalapproaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystalmaterial design with high structural diversity and symmetry. Our model increases the generation validity by more than 700%compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN *** Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 aresuccessfully optimized and deposited into the Carolina Materials Database ***, of which 39.6% have negativeformation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potentialsynthesizability.