Machine-learning accelerated structure search for ligand-protected clusters

Lincan Fang; Jarno Laakso; Patrick Rinke; Xi Chen

doi:10.1063/5.0180529

Machine-learning accelerated structure search for ligand-protected clusters

J Chem Phys. 2024 Mar 7;160(9):094106. doi: 10.1063/5.0180529.

Authors

Lincan Fang¹, Jarno Laakso¹, Patrick Rinke¹, Xi Chen^{1

2

3}

Affiliations

¹ Department of Applied Physics, Aalto University, 00076 AALTO, Espoo, Finland.
² School of Physical Science and Technology, Lanzhou University, Lanzhou, Gansu 730000, China.
³ Lanzhou Center for Theoretical Physics and Key Laboratory for Quantum Theory and Applications of the Ministry of Education, Lanzhou University, Lanzhou, Gansu 730000, China.

PMID: 38426517
DOI: 10.1063/5.0180529

Abstract

Finding low-energy structures of ligand-protected clusters is challenging due to the enormous conformational space and the high computational cost of accurate quantum chemical methods for determining the structures and energies of conformers. Here, we adopted and utilized a kernel rigid regression based machine learning method to accelerate the search for low-energy structures of ligand-protected clusters. We chose the Au25(Cys)18 (Cys: cysteine) cluster as a model system to test and demonstrate our method. We found that the low-energy structures of the cluster are characterized by a specific hydrogen bond type in the cysteine. The different configurations of the ligand layer influence the structural and electronic properties of clusters.