Public defence in Engineering Physics, M.Sc. Lincan Fang

Title of the thesis:: Machine Learning for Structure Search of Ligand-protected Nanoclusters

Doctoral student: Lincan Fang
Opponent: Assoc. Prof. Olga López Acevedo, Physics Institute of Universidad de Antioquia, Colombia
Custos: Prof. Patrick Rinke, Aalto University School of Science, Department of Applied Physics

Understanding the atomic structures of ligand-protected nanoclusters is essential for their application in various fields. These structures not only determine the physical and chemical properties of ligand-protected nanoclusters but also play a crucial role in their stability and reactivity. Knowing the precise atomic structures allows us to tailor nanoclusters for specific functions. However, because of the extraordinarily high dimensionality of the search space which encompasses an exceptionally large number of all potential structures, it is difficult to use quantum mechanical methods, such as the density functional theory, to find the low-energy structures of ligand-protected nanoclusters. On this point, the structure search of ligand-protected nanoclusters could be more efficient and accurate by utilizing machine learning methods. 

In this dissertation, I developed machine learning methods to search the atomic structures of ligand-protected nanoclusters by decomposing the problem into three steps. For the first step, I developed a molecular conformer search procedure based on Bayesian optimization to search the structures of isolated molecules. Using four amino acids as examples, the results showed that the procedure is both efficient and accurate. For the second step, I modified the procedure to search the structures of a single ligand on a nanocluster and used the procedure to search structures for a cysteine molecule on a gold-thiolate cluster.I found the cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers, while the energy rankings and spacings between the conformers are reordered. In the final step, I applied a machine learning method based on kernel rigid regression (KRR) models to relax the structures of ligand-protected nanoclusters. I also used an active learning workflow to enhance the relaxation performance of the KRR models. I then applied it to search structures of Au25(Cys)18-. We found that the low-energy structures with II-type hydrogen bonds are dominant and the different configurations of the ligand layer indeed influence the properties of the clusters. 

These ML methods I developed enable the exploration of isolated molecules and complicated nanocluster systems with atomic resolution. By utilizing these methods, it becomes possible to computationally obtain the the global and local minimum structures of systems, providing valuable insights into the structure-property mechanism of these system.

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information:

Doctoral theses in the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52

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