From the 1 of 6 linked papers with an AI index.
6 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
The paper introduces SevenNet-Polar, an equivariant graph neural network that simultaneously predicts energy, forces, stress, and Born effective charge tensors with high accuracy,…
A Robust 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…