4 papers
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…