Han, Seungwu

1. Data-efficient multi-fidelity training for high-fidelity machine\n learning interatomic potentials
   2024/09/12 by Jaesun Kim, Kim, Jaesun, Jisu Kim +10 · 29 citations
   Computer Science · #Anomaly Detection Techniques and Applications #Explainable Artificial Intelligence (XAI) #FOS: Physical sciences #Machine Learning and Data Classification #Materials Science (cond-mat.mtrl-sci)
2. Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
   2025/01/09 by Suyeon Ju, Ju, Suyeon, Jinmu You +9 · 9 citations
   Engineering · Materials Science · #Advanced Battery Technologies Research #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
3. Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
   2025/10/13 by Jaesun Kim, Kim, Jaesun, Jinmu You +28 · 1 voice · 3 citations
   Chemistry · Computer Science · Materials Science · Physics and Astronomy · #Computational Drug Discovery Methods #Inorganic Chemistry and Materials #Machine Learning in Materials Science #cond-mat.mtrl-sci
4. Disorder-dependent Li diffusion in Li₆PS₅Cl investigated by machine learning potential
   2023/10/30 by Jiho Lee, Suyeon Ju, Lee, Jiho +11 · 2 citations
   Engineering · Materials Science · #Advanced Battery Materials and Technologies #Advancements in Battery Materials #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
5. An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
   2025/06/18 by Jisu Kim, Kim, Jisu, Lee, Jiho +12 · 5 citations
   Computer Science · Neuroscience · #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications