10 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…
Towards Quantitative Reaction Dynamics of O3
Raidel Martin-Barrios, Abhirami Vijayakumar, Jingchun Wang +1
The reaction dynamics of O(3P) + O2(3Sigma_g-) collisions in the O3(1A') electronic ground state is characterized on a high-level MRCI+Q/aug-cc-pVQZ potential energy surface repres…
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…
A State-Space-View of Atom-Diatom Reactions Relevant to Rarefied Gas Flow
Abhirami Vijayakumar, Raidel Martin-Barrios, Markus Meuwly
A microscopically resolved picture of energy flow in atom-diatom collisions is essential for understanding the non-equilibrium chemistry in rarefied and hypersonic gas flow. Here,…
Structure and Spectroscopy of Criegee Intermediates in Gas- and Aqueous Environments
Cangtao Yin, Meenu Upadhyay, Markus Meuwly
The dynamics and spectroscopy of the small (HCOO) and large (CHCHOO) Criegee intermediates (CIs) in the gas phase, inside/on water droplets, on amorphous solid water (ASW)…
Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
Valerii Andreichev, Sena Aydin, Kai Töpfer +2
Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in…