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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

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Compact Kernel/Neural Network Representation for Accurate, Fast, and Global Reactive Molecular Potential Energy

Silvan Käser¹, Debasish Koner², Markus Meuwly¹

¹Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.

Precision Chemistry

|June 1, 2026

Summary

This summary is machine-generated.

This study introduces KerNN, a novel method for creating accurate molecular potential energy surfaces (PESs). KerNN significantly speeds up simulations and improves predictions, even beyond training data.

Keywords:

chemical reactions computational spectroscopy kernel methods maching learning molecular dynamics neural networks potential energy surfaces

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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

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Area of Science:

Computational Chemistry
Materials Science
Molecular Dynamics

Background:

Atomistic simulations require accurate potential energy surfaces (PESs) for reliable predictions.
Current neural network PESs face challenges with parameter efficiency and extrapolation.

Purpose of the Study:

To introduce KerNN, a hybrid kernel and neural network approach for molecular PES representation.
To enhance the speed, accuracy, and extrapolation capabilities of PES modeling.

Main Methods:

Developed KerNN, combining kernel methods with neural networks for PES construction.
Reduced learnable parameters compared to existing neural network PESs.
Utilized kernels as features to improve extrapolation beyond training data.

Main Results:

KerNN achieved significant reductions in training and evaluation times (orders of magnitude faster).
Maintained high prediction accuracy comparable to state-of-the-art methods.
Demonstrated superior extrapolation capabilities, addressing a key limitation of neural network PESs.

Conclusions:

KerNN offers a computationally efficient and accurate method for modeling molecular PESs.
The approach shows excellent performance in applications like spectroscopy and reaction dynamics.
KerNN provides a robust solution for simulating molecular systems with improved predictive power.