Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.
- DOI
- 10.3390/molecules31162814
- Published
- 2026 Aug 12
- Container
- Molecules (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/molecules31162814,
title = {Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.},
author = {He Q and Wang P and He X and Zhang J and Liu Y},
year = {2026},
journal = {Molecules (Basel, Switzerland)},
doi = {10.3390/molecules31162814},
url = {https://doi.org/10.3390/molecules31162814}
}RIS
TY - JOUR TI - Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals. AU - He Q AU - Wang P AU - He X AU - Zhang J AU - Liu Y PY - 2026 JO - Molecules (Basel, Switzerland) DO - 10.3390/molecules31162814 UR - https://doi.org/10.3390/molecules31162814 ER -
APA
Q, H., P, W., X, H., J, Z., & Y, L. (2026). Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical "Center-Environment" Features for Energetic Molecular Crystals.. Molecules (Basel, Switzerland). https://doi.org/10.3390/molecules31162814
Source records
- pubmed · retrieved 2026-09-25T01:49:41.539Z