Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes

Amir Hajibabaei

doi:10.1021/acs.jpclett.1c01605

2021-08-19

PRE

Amir Hajibabaei

Kwang S. Kim *

DOI：10.1021/acs.jpclett.1c01605

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摘要

En 中文

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.

Keyword:

LI ION DYNAMICS

SUPERIONIC CONDUCTOR

LITHIUM

1ST-PRINCIPLES

LI7P3S11

INSIGHTS

AI总结

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期刊

Journal of Physical Chemistry Letters

IF:

4.6

论文数:

2.7K

被引数:

7.5W

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