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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Analyzing and Driving Cluster Formation in Atomistic Simulations.

Gareth A Tribello¹, Federico Giberti², Gabriele C Sosso³

¹Atomistic Simulation Centre, School of Mathematics and Physics, Queen's University Belfast , Belfast BT7 1NN, United Kingdom.

Journal of Chemical Theory and Computation

|January 26, 2017

Summary

This summary is machine-generated.

This study introduces computational tools using graph theory to identify system phases by analyzing atomic or molecular uniformity. The method enhances sampling of nucleation events and analyzes crystal growth, even in complex semiconducting alloys.

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Last Updated: Mar 8, 2026

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Published on: March 1, 2020

Area of Science:

Computational materials science
Chemical physics
Data science

Background:

Identifying distinct phases in atomic or molecular systems is crucial for understanding material properties and behavior.
Traditional methods may struggle with complex systems exhibiting multiple nucleation centers or intricate structures.

Purpose of the Study:

To introduce a novel computational framework for identifying phases in atomic and molecular systems.
To demonstrate the utility of this framework in enhancing the sampling of nucleation events.
To apply the method for analyzing complex simulations of crystal nucleation and growth.

Main Methods:

Utilizes graph theory, combining atom-centered symmetry functions, adjacency matrices, and clustering algorithms.
Identifies regions of uniform properties within a system.
Defines collective variables for enhanced sampling of nucleation events.

Main Results:

Successfully identifies system phases based on constituent uniformity.
Enhances sampling efficiency for nucleation events.
Analyzes simulations of molecular crystal (urea) and semiconducting alloy nucleation.
Detects grain boundaries in polycrystals formed from multiple nucleation centers.

Conclusions:

The developed computational tools provide a robust method for phase identification and analysis in diverse material systems.
The approach is effective even in challenging scenarios like polycrystal formation in semiconducting alloys.
This method offers a powerful way to analyze and understand complex nucleation and growth processes.