Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.
- DOI
- 10.1021/acs.jctc.6c00917
- Published
- 2026 Sep 8
- Container
- Journal of chemical theory and computation
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.6c00917,
title = {Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.},
author = {Lian X and Pasquarello A},
year = {2026},
journal = {Journal of chemical theory and computation},
doi = {10.1021/acs.jctc.6c00917},
url = {https://doi.org/10.1021/acs.jctc.6c00917}
}RIS
TY - JOUR TI - Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation. AU - Lian X AU - Pasquarello A PY - 2026 JO - Journal of chemical theory and computation DO - 10.1021/acs.jctc.6c00917 UR - https://doi.org/10.1021/acs.jctc.6c00917 ER -
APA
X, L., & A, P. (2026). Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c00917
Source records
- pubmed · retrieved 2026-09-25T06:11:51.823Z