3 citations · 5 across the 5 of their papers we have counts for
5 papers
An Extensible Agentic Framework for Expert-Level Automation of Atomistic Simulations
Yutack Park, Yeonwoo Chung, Jinmu You +3
Traditionally, atomistic simulation has been constrained by the computational scaling limits of ab initio methods and the parameterization overhead of empirical force fields. The r…
A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
Sangmin Oh, Jinmu You, Jaesun Kim +4
We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowled…
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…
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…
Disorder-dependent Li diffusion in investigated by machine learning potential
Jiho Lee, Suyeon Ju, Seungwoo Hwang +4
Solid-state electrolytes with argyrodite structures, such as , have attracted considerable attention due to their superior safety compared to liquid electrolyt…