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Design and Optimization Strategies of a High-Performance Vented Box

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Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian

Shinyoung Bae¹, Dongjae Shin², Haechang Kim¹

¹School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.

Journal of Chemical Theory and Computation

|February 15, 2024

Summary

This study introduces a hybrid Bayesian optimization and local search method to efficiently find optimal atomic structures. This approach significantly reduces computational cost by avoiding the relaxation step, accelerating materials discovery.

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Area of Science:

Computational Materials Science
Chemical Physics
Materials Informatics

Background:

Finding the global minimum of potential energy surfaces is crucial for determining optimal atomic structures.
Existing methods often require substantial computational resources due to embedded relaxation steps.

Purpose of the Study:

To develop a more computationally efficient method for searching optimal atomic structures.
To address the high computational demand of traditional structural optimization algorithms.

Main Methods:

A hybrid approach combining Bayesian optimization (BO) and local search.
Circumventing the computationally expensive relaxation step within the optimization algorithm.
Incorporating physical constraints and structure similarity screening for enhanced efficiency.

Main Results:

The proposed hybrid method significantly expedites the structural search process.
Demonstrated efficiency in two supported metal systems.
Successfully identified optimal structures with reduced computational load.

Conclusions:

The hybrid Bayesian optimization and local search method offers a computationally efficient alternative for structural optimization.
This approach holds significant potential for accelerating materials discovery and design.
The method is particularly effective for supported metal systems.