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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Systematically improved potential energy surfaces via sinNN models and sparse grid sampling.

Antoine Aerts¹

¹Université libre de Bruxelles, Spectroscopy, Quantum Chemistry and Atmospheric Remote Sensing (SQUARES), 50, Av. F. Roosevelt CP 160/09, 1050 Brussels, Belgium.

The Journal of Chemical Physics

|March 25, 2026

Summary

This summary is machine-generated.

This study presents a new method for creating accurate potential energy surfaces (PESs) for molecular simulations by combining sparse grids and neural networks. This approach improves accuracy and efficiency in quantum dynamics simulations.

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Area of Science:

Computational Chemistry
Quantum Dynamics
Theoretical Chemistry

Background:

Accurate global potential energy surfaces (PESs) in sum-of-products (SOP) form are crucial for efficient high-dimensional quantum dynamics simulations.
The multi-configuration time-dependent Hartree (MCTDH) method requires robust PESs for accurate simulations.

Purpose of the Study:

To develop a novel methodology for constructing accurate, global PESs in SOP form.
To enable efficient high-dimensional quantum dynamics simulations using the MCTDH method.
To address the curse of dimensionality in PES construction.

Main Methods:

Hierarchical sparse grid sampling for unbiased configuration space discretization.
Single-layer neural networks with sinusoidal activation functions (sinNN) for PES fitting.
Trigonometric factorization identity for maintaining compact SOP form and numerical stability.

Main Results:

The methodology successfully constructs global PESs with controlled accuracy and systematic improvement.
Refitting an analytical PES for nitrous acid (HONO) reproduced vibrational transition energies with spectroscopic precision (<2.5 cm-1).
An AIQM2-based PES for HONO reproduced experimental vibrational frequencies with a root mean square deviation of ~16 cm-1, comparable to high-level ab initio methods.

Conclusions:

The combination of sparse grid sampling and sinNN fitting is a powerful, automated tool for generating topologically sound, spectroscopic-quality PESs.
The approach demonstrates robustness on larger molecules like formic acid and carbamic acid.
This methodology significantly advances the field of quantum dynamics simulations by providing accurate and efficient PES construction.