Abstracts Book - IMRC 2018

• SD2-O022 Invited Talk
HOLISTIC SCREENING OF MORE THAN 12,000 CANDIDATES FOR
SOLID LITHIUM-ION CONDUCTORS WITH STATISTICAL LEARNING
APPROACHES
Evan Reed 1
1 Stanford University, Material Science and Engineering, United States.
We double the number of known materials that exhibit high lithium conductivity
at room temperature by developing a large-scale computational screening
approach for identifying solid-state electrolytes for lithium-ion batteries that is
capable of screening all known lithium containing materials for a spectrum of
important metrics. To be useful for batteries, solid-state electrolytes must
satisfy many performance metrics at once, an optimization that is difficult to
perform experimentally or with computationally expensive ab initio techniques.
We first screened over 12,000 lithium containing materials for high structural
and chemical stability, low electronic conductivity, and low cost. We then
developed a data-driven ionic conductivity classification model using logistic
regression to identify which structures are likely to exhibit fast lithium-ion
conductivity based on experimental measurements reported in the literature.
The screening reduces the number of candidates down to 21 materials which
show promise as electrolytes and are three times more likely to be good on
conductors then randomly chosen structures.
Keywords: Machine learning, lithium-ion conductor, electrolyte
Presenting authors email: evanreed@Stanford.edu