:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Boittier, Eric, Töpfer, Kai, Devereux, Mike, Meuwly, Markus
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics
Online Access:	https://arxiv.org/abs/2406.00513
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
by: Chaton, Kham Lek, et al.
Published: (2026)

Force Fields for Deep Eutectic Mixtures: Application to Structure and 2D-Infrared Spectroscopy
by: Töpfer, Kai, et al.
Published: (2024)

Efficient, Equivariant Predictions of Distributed Charge Models
by: Boittier, Eric D., et al.
Published: (2026)

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
by: Wang, JingChun, et al.
Published: (2025)

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions
by: Boittier, Eric D., et al.
Published: (2025)

Diffusion and Spectroscopy of H$_2$ in Myoglobin
by: Käser, Jiri, et al.
Published: (2024)

Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions
by: Töpfer, Kai, et al.
Published: (2025)

SCN as a Local Probe of Protein Structural Dynamics
by: Aydin, Sena, et al.
Published: (2024)

Energy Relaxation of N$_2$O in Gaseous, Supercritical and Liquid Xenon and SF$_6$
by: Töpfer, Kai, et al.
Published: (2024)

Systematic improvement of empirical energy functions in the era of machine learning
by: Mike Devereux, et al.
Published: (2024)

${\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
by: Töpfer, Kai, et al.
Published: (2024)

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
by: Andreichev, Valerii, et al.
Published: (2025)

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics
by: Andreichev, Valerii, et al.
Published: (2026)

CO$_2$ and NO$_2$ Formation on Amorphous Solid Water
by: Upadhyay, Meenu, et al.
Published: (2024)

The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
by: Käser, Silvan, et al.
Published: (2024)

Full Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: The Photodissociation of H$_2$COO
by: Yin, Cangtao, et al.
Published: (2026)

Machine Learning-Based Enhancements of Empirical Energy Functions: Structure, Dynamics and Spectroscopy of Modified Benzenes
by: Chaton, Kham Lek, et al.
Published: (2024)

OH-Formation Following Vibrationally Induced Reaction Dynamics of H$_2$COO
by: Song, Kaisheng, et al.
Published: (2024)

Full-Dimensional Reactive Potential Energy Surfaces for OCS$^+$ $\rightarrow$ CO+S$^+$ Dissociation: Ground and Excited States
by: Yin, Cangtao, et al.
Published: (2026)

Structure and Spectroscopy of Criegee Intermediates in Gas- and Aqueous Environments
by: Yin, Cangtao, et al.
Published: (2025)

A State-Space-View of Atom-Diatom Reactions Relevant to Rarefied Gas Flow
by: Vijayakumar, Abhirami, et al.
Published: (2025)

Cluster Models for Next‐Generation, Machine‐Learning‐Based Energy Functions for Molecular Simulations
by: JingChun Wang, et al.
Published: (2026)

Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions
by: Käser, Silvan, et al.
Published: (2024)

End-to-End Photodissociation Dynamics of Energized H$_2$COO
by: Yin, Cangtao, et al.
Published: (2025)

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions
by: Aydin, Sena, et al.
Published: (2025)

Towards Quantitative Reaction Dynamics of O3
by: Martin-Barrios, Raidel, et al.
Published: (2026)

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
by: Andreichev, Valerii, et al.
Published: (2025)

Augmenting chemical databases for atomistic machine learning by sampling conformational space
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2025)

High-Energy Reaction Dynamics of N$_{3}$
by: Wang, JingChun, et al.
Published: (2024)

Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)

Reaction Dynamics of the H + HeH$^+$ $\rightarrow$ He + H$_2^+$ System
by: Upadhyay, Meenu, et al.
Published: (2025)

High-Energy Reaction Dynamics of O$_3$
by: Wang, JingChun, et al.
Published: (2025)

Reaction Dynamics for the [NNO] System from State-Resolved and Coarse-Grained Models
by: Veliz, Juan Carlos San Vicente, et al.
Published: (2025)

A Machine-Learned Symbolic Committor for a Chemical Reaction: Retinal Isomerization
by: Töpfer, Kai, et al.
Published: (2026)

Resonant Coupling Between Electromagnetic Waves and Protein Conformational Dynamics Revealed by Molecular Dynamics Simulations
by: Chen, Jiafei, et al.
Published: (2026)

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate
by: Qu, Chen, et al.
Published: (2026)

Feshbach Resonances in Cold Collisions: Benchmarking State of the Art ab initio Potential Energy Surfaces
by: Horn, Karl P., et al.
Published: (2024)

Gradual Optimization Learning for Conformational Energy Minimization
by: Tsypin, Artem, et al.
Published: (2023)

Insights into Ionic Diffusion in C-S-H Gel Pore from Molecular Dynamics Simulations: Spatial Distributions, Energy Barriers, and Structural Descriptor
by: Chen, Weiqiang, et al.
Published: (2025)

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
by: Shteingolts, S. A., et al.
Published: (2026)