Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics
- DOI
- 10.1021/acs.jcim.5c01180
- Published
- 2025-07-22
- Container
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jcim.5c01180,
title = {Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics},
author = {Ivan Žugec and Tin Hadži Veljković and Maite Alducin and J. Iñaki Juaristi},
year = {2025},
journal = {Journal of Chemical Information and Modeling},
doi = {10.1021/acs.jcim.5c01180},
url = {https://doi.org/10.1021/acs.jcim.5c01180}
}RIS
TY - JOUR TI - Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics AU - Ivan Žugec AU - Tin Hadži Veljković AU - Maite Alducin AU - J. Iñaki Juaristi PY - 2025 JO - Journal of Chemical Information and Modeling DO - 10.1021/acs.jcim.5c01180 UR - https://doi.org/10.1021/acs.jcim.5c01180 ER -
APA
Žugec, I., Veljković, T. H., Alducin, M., & Juaristi, J. I. (2025). Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.5c01180
Source records
- crossref · retrieved 2026-09-25T10:10:15.578Z