Bayesian Modeling of Polarizable Water: Lessons for Force Field Development
9 (2013), 452)and training on gas-phase data yields an unstable model, whereas the corresponding optimal Bayesian model gives reasonable results. A further analysis shows that observables respond in different ways to changes in force field parameters, highlighting the need to consider a large number of observables during force field training. Implications for model development as well as uncertainty quantification are discussed.
Authors
- Stefan Engblom (ORCID: https://orcid.org/0000-0002-3614-1732)
- David van der Spoel (ORCID: https://orcid.org/0000-0002-7659-8526)
- Alfred Nordman
Institutions
- Uppsala University (SE)
- Science for Life Laboratory (SE)
- Office of Advanced Scientific Computing Research (US)
- Scientific Computing Associates (United States) (US)
Publication Details
- Journal
- Journal of Chemical Theory and Computation
- Published
- 2026-05-23
- DOI
- https://doi.org/10.1021/acs.jctc.6c00260
- Primary Topic
- Nanopore and Nanochannel Transport Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Lunds Universitet
- Vetenskapsrådet
- Umeå Universitet
- Uppsala Universitet