most citedApplication of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2025★ 1 cited

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Suyeon Ju, Jinmu You, Gijin Kim +3

Achieving higher operational voltages, faster charging, and broader temperature ranges for Li-ion batteries necessitates advancements in electrolyte engineering. However, the compl…

cond-mat.mtrl-sci2024

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials

Jaesun Kim, Jisu Kim, Jaehoon Kim +4

Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced…