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GOAT: A Global Optimization Algorithm for Molecules and Atomic Clusters

2025/02/17 by Bernardo de Souza · 2 voices · 144 citations
Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Advanced Chemical Physics Studies #Algorithm #Chemistry #Computational chemistry #Computer science #Conformational isomerism #Energy minimization #Global optimization #Machine Learning in Materials Science #Mathematical optimization #Mathematics #Maxima and minima #Molecular dynamics #Molecule #Physics #Potential energy surface #Quantum #Quantum mechanics #Spectroscopy and Quantum Chemical Studies #Statistical physics #Work (physics)

paper · pdf · doi:10.1002/anie.202500393

published in Angewandte Chemie International Edition 64(18), e202500393 (Wiley)

openalex publication_date 2025/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

In this work, we propose a new Global Optimization Algorithm (GOAT) for molecules and clusters of atoms and show how it can find the global energy minima for both systems without resorting to molecular dynamics (MD). This avoids the potential millions of time-consuming gradient calculations required by a long MD run. Because of that, it can be used with any regular quantum chemical method, even with the costlier hybrid DFT. We showcase its accuracy by running it on various systems, from organic molecules to water clusters, metal complexes, and metal nanoparticles, comparing it with state-of-the-art methods such as the Conformer-Rotamer Ensemble Sampling Tool (CREST). We also discuss its underlying theory and mechanisms for succeeding in challenging cases. GOAT is, in general, more efficient and accurate than previous algorithms in finding global minima and succeeds in cases where others cannot due to the free choice for the Potential Energy Surface (PES).

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