5 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…
Efficient, Equivariant Predictions of Distributed Charge Models
Eric D. Boittier, Markus Meuwly
A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accu…
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…
Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions
Eric D. Boittier, Silvan Käser, Markus Meuwly
Accurate, yet computationally efficient energy functions are essential for state-of-the art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow bas…
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…