Automated Refinement of Property-Specific Polarizable Gaussian Multipole Water Models Using Bayesian Black-Box Optimization.

ABSTRACT: The critical importance of water in sustaining life highlights the need for accurate water models in computer simulations, aiming to mimic biochemical processes experimentally. The polarizable Gaussian multipole (pGM) model, recently introduced for biomolecular simulations, improves the handling of complex biomolecular interactions. As an integral part of our initial exploration, we examined a minimalist fixed geometry three-center pGM water model using ab initio quantum mechanical calculations of water oligomers. However, our final model development was based on liquid-phase water properties, leveraging automated machine learning (AutoML) techniques for optimization. This allows the development of a framework to refine both van der Waals and electrostatic parameters of the pGM mode

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12665499 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature