Public defence in Engineering Physics, M.Sc. Fabio Priante

Title of the thesis: Discovering Ice and Water Structures with High-Resolution AFM, Atomistic Modeling, and Machine Learning

Doctoral student: Fabio Priante
Opponent: Professor Christoph Dellago, University of Vienna, Austria
Custos: Professor Adam Foster, Aalto University School of Science, Department of Applied Physics

Nanotechnology aims to engineer materials and devices down to the individual atom level. To realize this long-term objective, several techniques have been developed, among which Atomic Force Microscopy plays a crucial role.

In the right experimental conditions, Atomic Force Microscopy has been used to observe and manipulate individual atoms and molecules. In addition, when operating in liquid environments, three-dimensional Atomic Force Microscopy has allowed to investigate hydration layers, that is, how water molecules organize around solvated systems. However, while the interpretation of these measurements is relatively straightforward when dealing with flat samples, it becomes much more challenging once complex, non-flat samples are involved. This is due to the possibility of acquiring only partial structural information. This thesis combines atomistic simulations and machine learning techniques to address this limitation, focusing on systems where water molecules play a central role.

In particular, the thesis addresses the discovery of new ice nanoclusters on metal surfaces, the observation of the hydration layers forming on cellulose and chitin nanocrystals, and the identification of the structure of peptide assemblies on graphite in water. Apart from the results obtained for these specific systems, a strong focus of the thesis is on the developed computational workflows. These could be applied to future challenging scenarios, bridging the gap between Atomic Force Microscopy measurements and atomistic structure understanding.

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

Contact information:

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

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