Structure Search with the Strategic Escape Algorithm

This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, levera...

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Published inJournal of chemical theory and computation Vol. 21; no. 7; pp. 3765 - 3773
Main Authors Burkhardt, Jordan, Jia, Yinglu, Li, Wan-Lu
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 08.04.2025
Subjects
Algorithms
Atomic clusters
Computational efficiency
Effectiveness
Metal clusters
Potential energy
Structure Prediction
Online AccessGet full text
ISSN1549-9618
1549-9626
1549-9626
DOI10.1021/acs.jctc.4c01746

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Abstract This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.
AbstractList This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.
This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.
This work introduces the Strategic Escape (SE) algorithm, an approach that systematically ensures effective exploration of the potential energy surface during global minimum searches for atomic clusters. The SE algorithm prioritizes the escape from local minima prior to geometry optimization, leveraging a combination of randomized direction vectors, distance-based uniqueness criteria, and covalent bonding heuristics. These principles enhance structural diversity and computational efficiency by reducing redundant geometry optimizations. Additionally, a symmetry-guided seed generation method based on an adaptive polygon is proposed to provide diverse and physically realistic initial configurations. Together, these methods achieve a 2.3-fold improvement in computational efficiency compared to conventional Basin-Hopping approaches. The effectiveness of the SE algorithm is demonstrated through its application to boron, metal clusters, and binary-composition clusters, achieving rapid convergence to global minimum structures with high reliability. These advancements establish the SE algorithm as a robust and scalable tool for exploring complex chemical systems.
Author Burkhardt, Jordan
Jia, Yinglu
Li, Wan-Lu
AuthorAffiliation Program of Materials Science and Engineering
Department of Chemistry and Biochemistry
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering
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SubjectTerms Algorithms
Atomic clusters
Computational efficiency
Effectiveness
Metal clusters
Potential energy
Structure Prediction
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Title Structure Search with the Strategic Escape Algorithm
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