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Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and

W S Jesus¹, F V Prudente¹, J M C Marques²

¹Instituto de Física, Universidade Federal da Bahia, 40170-115 Salvador, BA, Brazil. wsixteen@gmail.com prudente@ufba.br.

Physical Chemistry Chemical Physics : PCCP

|January 11, 2021

Summary

This summary is machine-generated.

We developed a new method using density functional theory (DFT) and machine learning (ML) to efficiently study alkali-metal ion microsolvation clusters. This approach accurately predicts structures, enabling analysis of larger systems.

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Area of Science:

Computational Chemistry
Physical Chemistry
Materials Science

Background:

Studying microsolvation clusters of alkali-metal ions with rare-gas atoms is crucial for understanding ion-solvent interactions.
Traditional methods for characterizing these clusters can be computationally expensive and time-consuming.

Purpose of the Study:

To develop and validate a novel computational methodology for efficiently studying alkali-metal ion microsolvation clusters.
To identify an accurate density functional theory (DFT) approach for characterizing these systems.
To leverage machine learning (ML) for predicting and optimizing cluster structures.

Main Methods:

Global optimization using an analytical potential energy surface (PES) and an evolutionary algorithm (EA).
Systematic benchmark study of DFT functionals and basis sets to determine the optimal approach.
Application of ML classification algorithms to predict the mapping of low-energy PES minima to DFT minima.
Re-optimization of selected low-energy minima at the DFT level.

Main Results:

The B3LYP-D3/aug-pcseg-1 DFT approach was identified as the most suitable for Li+Krn (n = 2-14, 16) clusters.
The ML classifier accurately predicted the majority of structures requiring DFT re-optimization.
The methodology significantly enhanced computational efficiency, allowing for the study of larger clusters.

Conclusions:

The proposed methodology provides an efficient and accurate approach for studying microsolvation clusters.
Combining global optimization, DFT benchmarking, and ML prediction accelerates the characterization of complex chemical systems.
This work paves the way for investigating larger and more complex microsolvation phenomena.