Work

Bayesian Modeling of Polarizable Water: Lessons for Force Field Development

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.6c00260

Authors: Alfred T. Nordman, Stefan Engblom, David van der Spoel

Institutions: Uppsala University, Science for Life Laboratory

Language: English

Topic: Advanced Physical and Chemical Molecular Interactions

Subfield: Physical and Theoretical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: Lunds Universitet, Vetenskapsrådet, Umeå Universitet, Uppsala Universitet