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Directed Electrostatics Strategy Integrated as a Graph Neural Network Approach for Accelerated Cluster Structure

Sridatri Nandy¹, 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 Theory and Computation

|January 15, 2025

Summary

We developed a graph neural network (DESIGNN) to predict stable nanocluster structures using directed electrostatics. This method accurately identifies magnesium cluster structures and generates novel isomers, accelerating materials discovery.

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Area of Science:

Computational chemistry and materials science
Application of artificial intelligence in predicting atomic structures

Background:

Predicting stable nanocluster structures is computationally challenging.
Understanding the potential energy surfaces (PESs) is crucial for identifying stable atomic cluster configurations.

Purpose of the Study:

To introduce a novel graph neural network (GNN)-based approach, DESIGNN, for predicting stable nanocluster structures.
To utilize directed electrostatics and molecular electrostatic potential (MESP) topography for guiding structure prediction.

Main Methods:

Developed the DESIGNN approach, integrating graph neural networks with directed electrostatics.
Benchmarked the model on magnesium (Mg) clusters (n < 150).
Predicted MESP topography minima and analyzed parent growth potential (GP) for cluster evolution.

Main Results:

DESIGNN accurately predicts MESP topography minima for Mg clusters (n < 70), aligning with full calculations.
Generated ground-state structures for Mg clusters (n = 4-32) that match literature findings.
Successfully generated novel symmetric isomers and large stable Mg nanoclusters (n up to 260).

Conclusions:

The DESIGNN approach effectively accelerates the search and prediction of stable nanocluster structures.
This method shows significant promise for exploring large metal clusters guided by MESP topography.
DESIGNN facilitates the discovery of new materials with specific symmetries and properties.