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20202026
most citedTraining machine-learning potentials for crystal structure prediction using disordered structures

41 citations · 45 across the 5 of their papers we have counts for

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2 papers · 1 filter

cond-mat.mtrl-sci2025★ 3 cited

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials

Jaesun Kim, Jinmu You, Yutack Park +11

Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to…

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…