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Kim, Kwang S.
Center for Superfunctional Materials (CSM)
Research Interests
  • Theoretical/experimental nanosciences, molecular electronics spectroscopy, energy materials

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Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes

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Title
Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes
Author
Hajibabaei, AmirKim, Kwang S.
Issue Date
2021-08
Publisher
AMER CHEMICAL SOC
Citation
JOURNAL OF PHYSICAL CHEMISTRY LETTERS, v.12, no.33, pp.8115 - 8120
Abstract
We apply ab initio molecular dynamics (AIMD) with on-the-fly machine learning (ML) of interatomic potentials using the sparse Gaussian process regression (SGPR) algorithm for a survey of Li diffusivity in hundreds of ternary crystals as potential electrolytes for all-solid-state batteries. We show that models generated for these crystals can be easily combined for creating more general and transferable models which can potentially be used for simulating new materials without further training. As examples, universal potentials are created for Li-P-S and Li-Sb-S systems by combining the expert models of the crystals which contained the same set of elements. We also show that combinatorial models of different ternary crystals can be directly applied for modeling composite quaternary ones (e.g., Li-Ge-P-S). This hierarchical approach paves the way for modeling large-scale complexity by a combinatorial approach.
URI
https://scholarworks.unist.ac.kr/handle/201301/54001
URL
https://pubs.acs.org/doi/10.1021/acs.jpclett.1c01605
DOI
10.1021/acs.jpclett.1c01605
ISSN
1948-7185
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CHM_Journal Papers
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