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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Exploring the Chemical Space of Noncovalent Molecular Clusters Using Automated Cluster Building Algorithm and Neural

Sandip Giri¹, Anakuthil Anoop^1,2

¹Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur, India.

Journal of Computational Chemistry

|December 10, 2025

Summary

This summary is machine-generated.

Finding the global minima of molecular clusters is crucial. This study integrates a pretrained neural network potential (AIMNET2) with a TABU-based interface for efficient potential energy surface exploration of various molecular clusters.

Keywords:

AIMNET2 Tabu automations global minima search machine learning potentials molecular clusters

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Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Computational Chemistry
Materials Science
Chemical Physics

Background:

Determining the global minima of molecular clusters is a significant challenge in computational chemistry due to the exponential growth of local minima with cluster size.
Accurate ab initio methods are computationally expensive, creating a need for more efficient and reliable exploration techniques.
Neural network potentials offer a promising alternative, providing accurate and reliable results for molecular cluster problems.

Purpose of the Study:

To develop and demonstrate an efficient methodology for exploring the potential energy surfaces (PES) of molecular clusters.
To leverage pretrained neural network potentials for rapid and accurate determination of global minima in molecular clusters.
To advance the automation of computational molecular cluster generation for accelerated discovery.

Main Methods:

Integration of the AIMNET2 pretrained neural network potential model into a TABU-based PyAR interface.
Application of the integrated system for PES explorations of molecular clusters, including water, ammonia, hydrogen peroxide, methanol, and acetic acid.
Testing the methodology on clusters with aggregation numbers ranging from 1 to 10.

Main Results:

Demonstrated the effectiveness of the AIMNET2 integrated PyAR interface for exploring molecular cluster PES.
Successfully applied the method to various standard molecular clusters, showcasing its versatility.
The approach proved efficient for identifying low-energy isomers without requiring explicit training on the target cluster types.

Conclusions:

The integration of AIMNET2 with the PyAR interface provides an efficient and reliable solution for molecular cluster global minima identification.
This methodology represents a significant advancement toward fully automated computational molecular cluster generation.
The findings pave the way for accelerated exploration and discovery in the field of molecular clusters.