Simulating molecular adsorption on dielectric surfaces with classical MD, DFT and machine learning

Surfaces and the interface physics at the nano-scale play a vital role in several technological and natural processes. These interface interactions have applications in geochemistry and environmental science, biomineralisation, corrosion, etc. The interaction physics can be understood by studying the molecular adsorption interactions, at various surface coverages -- from singular molecule to bulk. Atomic force microscopy (AFM) has emerged as a potent tool to characterise such molecular interactions with the surface. These AFM images are often complemented with simulation tools to further characterise the surface phenomena. This dissertation applies simulation and machine learning tools to characterise molecular adsorption on surfaces. It targets two objectives: (a) characterisation of the 2×1 surface reconstruction of the (104) calcite surface and determination of the influence of the reconstruction on the surface chemistry through molecular adsorption, and (b) development of a machine-learning (ML) workflow to predict bulk molecular -- water in particular -- interactions over surfaces, that form the hydration layers. The design of the ML workflow is split into intermediate targets in the dissertation: (a) generation of a solid-liquid interface database (b) designing of a general descriptor of the surfaces, and (c) development and training of ML techniques to rapidly predict the hydration layers over the surface. Additionally, an out-of-distribution detection ML technique is used to gauge the accuracy of the prediction of the hydration layers.