Bayesian Learning for Accurate and Robust Biomolecular Force Fields

Vojtech Kostal, Brennon L. Shanks, Pavel Jungwirth, Hector Martinez-Seara

Open source

DOI: 10.1021/acs.jctc.5c02051
Published: 2026-02-19
Container: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.5c02051,
  title = {Bayesian Learning for Accurate and Robust Biomolecular Force Fields},
  author = {Vojtech Kostal and Brennon L. Shanks and Pavel Jungwirth and Hector Martinez-Seara},
  year = {2026},
  journal = {Journal of Chemical Theory and Computation},
  doi = {10.1021/acs.jctc.5c02051},
  url = {https://doi.org/10.1021/acs.jctc.5c02051}
}

RIS

TY  - JOUR
TI  - Bayesian Learning for Accurate and Robust Biomolecular Force Fields
AU  - Vojtech Kostal
AU  - Brennon L. Shanks
AU  - Pavel Jungwirth
AU  - Hector Martinez-Seara
PY  - 2026
JO  - Journal of Chemical Theory and Computation
DO  - 10.1021/acs.jctc.5c02051
UR  - https://doi.org/10.1021/acs.jctc.5c02051
ER  -

APA

Kostal, V., Shanks, B. L., Jungwirth, P., & Martinez-Seara, H. (2026). Bayesian Learning for Accurate and Robust Biomolecular Force Fields. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.5c02051

Source records

crossref · retrieved 2026-09-26T01:23:26.309Z