Abstract:
Water is often a crucial component of many molecular simulations. To be able to accurately model chemical reactions near/at water interfaces using classical force fields, it is essential to employ a water model that is both polarizable and flexible.
SWM4/Fw, our in-house flexible polarizable water model [1], will be presented. It was optimized using Gaussian process regression, a machine-learning method that can predict a model’s properties without requiring further simulation whenever the force field parameters are changed. SWM4/Fw reproduces many reference water properties and adds flexibility important for modeling chemical reactions involving bond stretching or breaking and for calculating vibrational spectra. The model is also computationally efficient thanks to the use of extended Lagrangian with Drude oscillators to represent explicit electronic polarization. The approach to parameterize the water model based on Gaussian process regression should also be useful for developing other force fields.
Reference:
[1] Wang, X.; Tse, Y.-L. S.*, Flexible Polarizable Water Model Parameterized via Gaussian Process Regression, J. Chem. Theory Comput., 2022, 18 (12), 7155–7165.
Seminar Series by the NYU-ECNU Center for Computational Chemistry at NYU Shanghai
This event is open to the NYU Shanghai, NYU, ECNU community and the computational chemistry community.