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

Figure 1. Crystal descriptors for ML-driven development of lithium battery materials. Different descriptors, which digitally describe crystal structures, possess distinct strengths that help accelerate diverse ML tasks including property prediction, atomistic simulation, and crystal generation. The results from ML tasks can be used as new input structures and properties for further model training and prediction. ML: Machine-learning.

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