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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Yulian T Manchev¹, Paul L A Popelier¹

¹Department of Chemistry, The University of Manchester, Manchester M13 9PL, U.K.

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|October 11, 2024

Summary

This summary is machine-generated.

This study introduces an advanced water dimer potential using Gaussian process regression (GPR) and a custom kernel for enhanced accuracy. The new model accurately predicts dimer geometries and captures potential energy surface curvature in simulations.

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Area of Science:

Computational Chemistry
Materials Science
Quantum Mechanics

Background:

Accurate modeling of molecular interactions is crucial for understanding chemical systems.
Previous methods for water dimer potentials had limitations in capturing complex interactions.
Machine learning force fields offer a promising avenue for high-fidelity molecular simulations.

Purpose of the Study:

To develop a first-principles water dimer potential incorporating many-body interactions.
To enhance modeling capabilities by integrating Gaussian process regression (GPR) with a custom kernel and the FFLUX machine learning force field.
To create a robust and accurate model for simulating water dimer geometries and energies.

Main Methods:

Utilized Gaussian process regression (GPR) for developing the water dimer potential.
Implemented a custom kernel function via the KeOps library to enable larger GPR models.
Trained the model on a new synthetic water dimer data set (WD24).
Interfaced the GPR model with the FFLUX machine learning force field.

Main Results:

The developed model predicts 90% of water dimer geometries within chemical accuracy on a test set and during simulations.
The models successfully capture the curvature of the potential energy surface.
Achieved a geometry optimization with a low total energy error of 2.6 kJ mol⁻¹ for widely separated water molecules.
Demonstrated the first-ever dimeric modeling of a flexible, noncrystalline system using FFLUX.

Conclusions:

The novel GPR-based water dimer potential significantly advances the accuracy of molecular simulations.
The integration with FFLUX and the use of a custom kernel represent a substantial upgrade in modeling capabilities.
This work paves the way for more precise simulations of water interactions in complex systems.