fig4

Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries

Figure 4. Graph-based descriptors. (A) PLMF[66], adapted with permission[66], Copyright 2017, Springer Nature; (B) SchNet, a deep learning architecture for crystals and molecules[15]; (C) graph descriptor of CGCNN[43] reprinted with permission[43], Copyright 2018, American Physical Society; (D) MEGNet with state parameters included in crystal graph[37], adapted with permission[37], Copyright 2019, American Chemical Society. PLMF: Property-labeled materials fragments; CGCNN: crystal graph convolutional neural networks; MEGNet: MatErials graph network.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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