10 citations · 14 across the 6 of their papers we have counts for
6 papers · 1 filter
Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
Kham Lek Chaton, Eric D. Boittier, Mike Devereux +1
A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The Ph…
Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
JingChun Wang, Meenu Upadhyay, Eric D. Boittier +7
Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy…
Force Fields for Deep Eutectic Mixtures: Application to Structure and 2D-Infrared Spectroscopy
Kai Töpfer, Eric Boittier, Michael Devereux +3
Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN, K and acetamide is imp…
Kernel-based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations
Eric Boittier, Kai Töpfer, Mike Devereux +1
A kernel-based method (kernelized minimal distributed charge model - kMDCM) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges whose posi…
Systematic Improvement of Empirical Energy Functions in the Era of Machine Learning
Mike Devereux, Eric D. Boittier, Markus Meuwly
The impact of targeted replacement of individual terms in empirical force fields is quantitatively assessed for pure water, dichloromethane (DCM), and solvated K and Cl ion…
Molecular Dynamics with Conformationally Dependent, Distributed Charges
Eric D. Boittier, Mike Devereux, Markus Meuwly
Accounting for geometry-induced changes in the electronic distribution in molecular simulation is important for capturing effects such as charge flow, charge anisotropy and polariz…