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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Machine Learning-Assisted Local-to-Global Optimization Strategy for Accelerated Molecular Cluster Structure

Gunjan R Ramteke¹, K V Jovan Jose¹

¹Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India.

Journal of Chemical Information and Modeling

|February 24, 2026

Summary

We developed LOGOS, a machine learning strategy, to predict stable molecular cluster structures. This method accelerates the discovery of energetically favorable clusters by learning patterns from smaller structures.

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Area of Science:

Computational Chemistry
Materials Science
Chemical Physics

Background:

Predicting stable molecular cluster structures is computationally intensive.
Understanding molecular clusters is crucial for various chemical and physical processes.

Purpose of the Study:

To present a machine learning-assisted strategy, LOGOS, for efficient prediction of stable molecular cluster structures.
To accelerate the structure search and prediction of large molecular clusters.

Main Methods:

Developed a local-to-global optimization strategy (LOGOS) using deep learning.
Identified localized patterns and predicted binding sites for cluster construction.
Generated geometry-optimized daughter structures in the molecular electrostatic potential (MESP)-topography feature space.

Main Results:

Benchmarked LOGOS by building ground-state clusters of (CO2)n (n < 30).
Results were well corroborated by literature minimum-energy structures.
Demonstrated efficient prediction of daughter clusters with minimal computational cost.

Conclusions:

LOGOS accelerates the prediction of large molecular cluster structures.
Provides a practical, hierarchical solution for identifying energetically favorable clusters.
Leverages deep learning to understand complex patterns and predict structures efficiently.