摘要
We report a novel deep learning(DL)method for classifying inorganic compounds using 3D electron density data.We transform Density Functional Theory(DFT)-derived CHGCAR files from the Materials Project(MP)and experimental data from the Inorganic Crystal Structure Database(ICSD)into point clouds and sparse tensors,optimized for use in DLmodels such as PointNet and Sparse 3DCNN.This approach effectively overcomes the limitations of handling the dense 3D data,a common challenge in DL.Contrasting with traditional 1D or 2D X-ray diffraction(XRD)patterns that necessitate complex reciprocal space analysis,our method utilizes 3D density data for direct interpretation in real lattice space.This shift significantly enhances classification accuracy,outperforming traditional XRD-driven DL methods.We achieve accuracies of 97.28%,90.77%,and 90.10%for crystal system,extinction group,and space group classifications,respectively.Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.
基金
supported by the Alchemist Project(20012196)funded by MOTIE,Korea
partly by the National Research Foundation of Korea(NRF)(2021R1A2C1009144 and RS-2024-00446825).
