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; Yang, Yuedong1,2; Lu, Yutong1,2

2022-09-29

10.1021/acs.jpcc.2c03051 16297

Journal of Physical Chemistry C   Impact Factor & Quartile

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

English

Corresponding

Chemistry ; Science & Technology - Other Topics ; Materials Science

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

WOS:000867420700001

AMER CHEMICAL SOC

Web of Science

1.Natl Supercomputer Ctr Guangzhou, Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
2.& Shenzhen Inst Quantum Sci Engn & Guangdong Prov, Southern Univ Sci & Technol, Dept Mat Sci & Engn, & Shenzhen Key Lab Adv Quantum Funct Mat & Device, 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,2022,126(38).

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,126(38).

Chen, Pin,et al."Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning".Journal of Physical Chemistry C 126.38(2022).