25 citations · 52 across the 12 of their papers we have counts for
12 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…
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…
Interaction at a Distance: Xenon Migration in Mb
Haydar Taylan Turan, Eric Boittier, Markus Meuwly
The transport of ligands, such as NO or O, through internal cavities is essential for the function of globular proteins, including hemoglobin, myoglobin (Mb), neuroglobin, trun…
Uncertainty quantification for predictions of atomistic neural networks
Luis Itza Vazquez-Salazar, Eric D. Boittier, M. Meuwly
The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored. For this, the architecture o…
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…
Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies
Luis Itza Vazquez-Salazar, Eric Boittier, Oliver T. Unke +1
An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content…
MP2 Is Not Good Enough: Transfer Learning ML Models for Accurate VPT2 Frequencies
Silvan Käser, Eric Boittier, Meenu Upadhyay +1
The calculation of the anharmonic modes of small to medium sized molecules for assigning experimentally measured frequencies to the corresponding type of molecular motions is compu…