Improving Material Property Prediction by Leveraging the Large- Scale Computational Database and Deep Learning

Chen, Pin1; Chen, Jianwen1; Yan, Hui1; Mo, Qing1; Xu, Zexin1; Liu, Jinyu1; Zhang, Wenqing2,3,4,5; Yang, Yuedong1; Lu, Yutong1

2022-08-01

10.1021/acs.jpcc.2c03051

Journal of Physical Chemistry C   影响因子和分区

1932-7447

1932-7455

Predicting physical and chemical properties of materials based on structures is critical for bottom-up material design. Many property prediction models and material training databases have been proposed, but accurately predicting properties is still challenging. Here, we report a package of "Matgen + CrystalNet " approach to improve material property prediction. We construct a large-scale material genome database (Matgen) containing 76k materials collected from an experimentally observed database and compute their properties through the density functional theory method with the Perdew-Burke- Ernzerhof (PBE) functional. Our database achieves the same computation accuracy by comparing part of our results with those from the open Material Project and Open Quantum Materials Database, all with PBE computations, and contains more diverse chemical species and big-sized structures. Based on the computed properties of our comprehensive data set, we have developed a new graph neural network (GNN) model, namely, CrystalNet, by strengthening the message passing between atoms and bonds to mimic physical and chemical interactions. The model is shown to outperform other GNN prediction models. The proof-of-concept applications, such as fine-tuning data on experimental values to improve prediction accuracy and bandgap prediction on hypothetical materials, showcase the usability and potential capacity of our package of "database + model " to improve material design.

SCI ; EI

英语

其他

National Key R&D Program of China[2020YFB0204803] ; Program for Guang-dong Introducing Innovative and Entrepreneurial Teams[2016ZT06D211] ; Guangdong Province Key Area RD Program[2019B010940001] ; Guangdong Introductive Inno-vative and Entrepreneurial Team Project[2017ZT07C062] ; Shenzhen Municipal Key-Lab program[ZDSYS20190902092905285] ; Guangdong Provincial Key-Lab program[2019B030301001]

Chemistry ; Science & Technology - Other Topics ; Materials Science

Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary

WOS:000843591200001

AMER CHEMICAL SOC

20223512668386

Computation theory ; Database systems ; Deep learning ; Density functional theory ; Graph neural networks ; Message passing ; Neural network models

Ergonomics and Human Factors Engineering:461.4 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Programming:723.1 ; Data Processing and Image Processing:723.2 ; Database Systems:723.3 ; Artificial Intelligence:723.4 ; Probability Theory:922.1 ; Atomic and Molecular Physics:931.3 ; Quantum Theory; Quantum Mechanics:931.4

Web of Science

1.Sun Yat sen Univ, Natl Supercomp Ctr Guangzhou, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
2.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Guangdong Prov Key Lab Computat Sci & Mat Design, Shenzhen 518055, Guangdong, Peoples R China
5.Southern Univ Sci & Technol, Shenzhen Key Lab Adv Quantum Funct Mat & Devices, Shenzhen 518055, Guangdong, Peoples R China

Chen, Pin,Chen, Jianwen,Yan, Hui,et al. Improving Material Property Prediction by Leveraging the Large- Scale Computational Database and Deep Learning[J]. Journal of Physical Chemistry C,2022.

Chen, Pin.,Chen, Jianwen.,Yan, Hui.,Mo, Qing.,Xu, Zexin.,...&Lu, Yutong.(2022).Improving Material Property Prediction by Leveraging the Large- Scale Computational Database and Deep Learning.Journal of Physical Chemistry C.

Chen, Pin,et al."Improving Material Property Prediction by Leveraging the Large- Scale Computational Database and Deep Learning".Journal of Physical Chemistry C (2022).