fig2

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

Figure 2. Explicit crystal structure descriptors and universal descriptor packages. (A) Matminer, including Magpie descriptors representing composition, valence electrons and other basic parameters of crystal materials. This figure is quoted with permission[21], Copyright 2018, Elsevier; (B) Automatminer, a tool for automatically creating complete ML pipelines for materials science, including automatic featurization with Matminer, feature reduction, and an AutoML backend. Put in a materials dataset and get out a machine that predicts materials properties. This figure is quoted with permission[61], Copyright 2020, Springer Nature. ML: Machine learning; AutoML: automated machine learning.

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