222 citations · 227 across the 7 of their papers we have counts for
8 papers
SevenNet-Polar for MultiTask Prediction of Energy, Forces, Stress, and Born Effective Charges: Development and Application to ZrO, LiPO, and Perovskites
Anh Khoa Augustin Lu, Shungo Arai, Yutack Park +3
Accurate prediction of the Born effective charge (BEC) tensor is crucial for modeling materials under electric fields but remains computationally expensive. To bridge this gap, we…
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…
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…
Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
Minseok Moon, Seungwoo Hwang, Jaesun Kim +3
Ovonic threshold switching (OTS) selectors play a critical role in non-volatile memory devices because of their nonlinear electrical behavior and polarity-dependent threshold volta…
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Jisu Kim, Jiho Lee, Sangmin Oh +5
Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient altern…
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…
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…
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
Yutack Park, Jaesun Kim, Seungwoo Hwang +1
Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due…