Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

By Austin D. Sendek, Qian Yang, Ekin Dogus Cubuk, Karel-Alexander N. Duerloo, Yi Cui, Evan J. Reed. 2017

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Computational Screening of Cathode Coatings for Solid-State Batteries (2019, background): “Both of these factors were observed to be negatively correlated to ionic conductivity in a recent statistical learning study of Li-ion conductors.(96) The decreased oxidation limit as the Li content increases may simply be a consequence of the fact that the chemical potential of Li tends to increase”
Scaling deep learning for materials discovery (2023, result): “Similarly, following a holistic screening approach with filters such as exclusion of transition metals or by lithium fraction, we find 528 promising Li-ion conductors among GNoME discoveries, a 25 times increase compared with the original study 46 .”
Machine learning: Accelerating materials development for energy storage and conversion (2020, background): “Combining with big data,(23) ML techniques have successfully made many breakthroughs in the field of energy storage and conversion materials, such as catalysts(16,24) and battery materials.(25,26) Several early reviews have introduced the applications of ML to materials science, including materials ”