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Peer-reviewedLicence not statedJournal of Chemical Information and Modeling

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

  • Sangmin Oh1
  • J.H. You1
  • Jaesun Kim1
  • Jiho Lee1
  • Hyungmin An1
  • Seungwu Han2,1
  • Youngho Kang3
  1. 1Seoul National University , , ,
  2. 2Korea Institute for Advanced Study , , ,
  3. 3Incheon National University , , ,
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Cite

BibTeXRIS
BibTeX
@article{oh2026lightweight,
  title = {{A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations}},
  author = {Sangmin Oh and J.H. You and Jaesun Kim and Jiho Lee and Hyungmin An and Seungwu Han and Youngho Kang},
  journal = {Journal of Chemical Information and Modeling},
  year = {2026},
  month = jul,
  doi = {10.1021/acs.jcim.6c01103},
  url = {https://arxiv.org/abs/2604.10887},
}
RIS
TY  - JOUR
TI  - A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
AU  - Sangmin Oh
AU  - J.H. You
AU  - Jaesun Kim
AU  - Jiho Lee
AU  - Hyungmin An
AU  - Seungwu Han
AU  - Youngho Kang
PY  - 2026
DA  - 2026/07/20
JO  - Journal of Chemical Information and Modeling
DO  - 10.1021/acs.jcim.6c01103
UR  - https://arxiv.org/abs/2604.10887
SN  - 1549-9596
SN  - 1549-960X
ER  -