AUTOMATON: A Program That Combines a Probabilistic Cellular Automata and a Genetic Algorithm for Global Minimum...

Area of Science:

• Computational Chemistry
• Materials Science
• Chemical Physics

Background:

• Determining the global minimum energy structure of atomic clusters and molecules is crucial for understanding their properties.
• Existing methods often struggle with the complexity and vast search space of potential structures.

Purpose of the Study:

• Introduce AUTOMATON, a novel program for identifying global minimum structures of gas-phase atomic clusters and molecules.
• Demonstrate the program's effectiveness across a diverse range of chemical species.

Main Methods:

• Utilizes a simplified probabilistic cellular automaton for initial population generation, ensuring atom distribution.
• Employs genetic operations (mating and mutations) for structure evolution.
• Incorporates a chemical formula checker and gene ranking for descendant structure generation and duplicate identification algorithms (geometric and charge-based).

Main Results:

• Successfully identified the global minimum and lowest-energy isomers for all 45 tested molecules, including organic, organometallic, Zintl ion, star-shaped, and boron-based clusters.
• Validated the program's effectiveness on diverse chemical species.

Conclusions:

• AUTOMATON is a highly effective tool for identifying energetically preferred structures.
• The program's methodology ensures accurate and efficient exploration of the conformational landscape for various chemical systems.