Systematic Improvement on the Classical Molecular Model of Water

Lee-Ping Wang, Teresa Head-Gordon, Jay Ponder, Pengyu Ren, John Chodera,
Peter Eastman, Todd J. Martinez and Vijay S. Pande

Department of Chemistry, Stanford University, Stanford, CA
Department of Bioengineering, Univ. of California at Berkeley, Berkeley, CA
Department of Chemistry, Washington University at St. Louis, St. Louis, MO
Department of Biomedical Engineering, University of Texas, Austin, TX
Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, NY

Biophysical Journal, 106, A403 (2014)

Abstract:

The theoretical and computational modeling of water requires striking a
balance between a detailed description of the physical interactions and the
numerical simplicity needed for sampling the condensed phase. Here we report
the iAMOEBA classical polarizable water model, which is a simplified version
of the AMOEBA model from ten years ago. iAMOEBA uses a direct or first-order
approximation to describe electronic polarizability, which reduces the
computational cost relative to a fully polarizable model such as AMOEBA.
The model is parameterized using ForceBalance, a systematic model optimization
method which simultaneously utilizes training data from experimental
measurements and high-level ab initio calculations. We show that iAMOEBA is
a highly accurate model for water in the gas and condensed phases with the
ability to predict a comprehensive set of properties outside of the training
data. The increased accuracy of iAMOEBA over the fully polarizable AMOEBA
model indicates the validity of the direct polarization approximation in
effectively capturing polarization effects in water. Our work also
demonstrates ForceBalance as a method which allows the researcher to
systematically improve empirical models by optimally utilizing the available
reference data.