Solid electrolytes for Li-ion batteries via machine learning

A general-purpose machine learning framework for predicting properties of inorganic materials

Logan Ward, Ankit Agrawal, Alok Choudhary et al. · npj Computational Materials · 2016 · 1.7K citations · Full text

Natural Sciences, Materials Engineering, Materials Science +16

Sulfide solid electrolytes for all-solid-state lithium batteries: Structure, conductivity, stability and application

Shaojie Chen, Dongjiu Xie, Gaozhan Liu et al. · Energy storage materials · 2018 · 665 citations

Natural Sciences, Materials Engineering, Materials Science +14

Crystal structure and ionic conductivity of Li14Zn(GeO4)4 and other new Li+ superionic conductors

Hawoong Hong · Materials Research Bulletin · 1978 · 539 citations

Natural Sciences, Chemistry, Materials Science +16

High-Throughput Screening of Solid-State Li-Ion Conductors Using Lattice-Dynamics Descriptors

Sokseiha Muy, Johannes Voss, Roman Schlem et al. · iScience · 2019 · 270 citations · Full text

Natural Sciences, Chemistry, Materials Science +17

Tuning mobility and stability of lithium ion conductors based on lattice dynamics

Sokseiha Muy, John Christopher Bachman, Livia Giordano et al. · Energy & Environmental Science · 2018 · 253 citations · Full text

Natural Sciences, Materials Engineering, Materials Science +17