Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water.

Autor: Calio PB; Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States., Hocky GM; Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States.; Department of Chemistry, New York University, New York, New York 10003, United States., Voth GA; Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, United States.
Jazyk: angličtina
Zdroj: Journal of chemical theory and computation [J Chem Theory Comput] 2020 Sep 08; Vol. 16 (9), pp. 5675-5684. Date of Electronic Publication: 2020 Aug 13.
DOI: 10.1021/acs.jctc.0c00558
Abstrakt: Experiment directed simulation (EDS) is a method within a class of techniques seeking to improve molecular simulations by minimally biasing the system Hamiltonian to reproduce certain experimental observables. In a previous application of EDS to ab initio molecular dynamics (AIMD) simulation based on electronic density functional theory (DFT), the AIMD simulations of water were biased to reproduce its experimentally derived solvation structure. In particular, by solely biasing the O-O pair correlation function, other structural and dynamical properties that were not biased were improved. In this work, the hypothesis is tested that directly biasing the O-H pair correlation (and hence the H-O···H hydrogen bonding) will provide an even better improvement of DFT-based water properties in AIMD simulations. The logic behind this hypothesis is that for most electronic DFT descriptions of water the hydrogen bonding is known to be deficient due to anomalous charge transfer and over polarization in the DFT. Using recent advances to the EDS learning algorithm, we thus train a minimal bias on AIMD water that reproduces the O-H radial distribution function derived from the highly accurate MB-pol model of water. It is then confirmed that biasing the O-H pair correlation alone can lead to improved AIMD water properties, with structural and dynamical properties even closer to experiment than the previous EDS-AIMD model.
Databáze: MEDLINE