Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization

Lincan Fang, Xiaomi Guo, Milica Todorovic, Patrick Rinke, Xi Chen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
49 Downloads (Pure)

Abstract

Finding low-energy conformers of organic molecules is a complex problem due to the flexibilities of the molecules and the high dimensionality of the search space. When such molecules are on nanoclusters, the search complexity is exacerbated by constraints imposed by the presence of the cluster and other surrounding molecules. To address this challenge, we modified our previously developed active learning molecular conformer search method based on Bayesian optimization and density functional theory. Especially, we have developed and tested strategies to avoid steric clashes between a molecule and a cluster. In this work, we chose a cysteine molecule on a well-studied gold–thiolate cluster as a model system to test and demonstrate our method. We found that cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers. However, the energy rankings and spacings between the conformers are reordered.
Original languageEnglish
Pages (from-to)745-752
Number of pages8
JournalJOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume63
Issue number3
Early online date15 Jan 2023
DOIs
Publication statusPublished - 13 Feb 2023
MoE publication typeA1 Journal article-refereed

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