Chemically Accurate Force Fields (FF) for Molecule-Surface Interactions

• Applicant: Saientan Bag, Ph.D.
• Subject Area: Theoretical Chemistry: Molecules, Materials, Surfaces
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 520493690

Adsorption and desorption of molecules are key processes in extraction and purification of biomolecules, engineering of drug carriers, designing of surface-specific coatings. To understand the adsorption process on the atomic scale, state-of-the-art quantum mechanical and classical simulation methodologies are widely used. However, studying adsorption using a full quantum mechanical treatment is limited to picoseconds simulation timescales, while classical molecular dynamics simulations are limited by the accuracy of existing force fields (FF). To overcome these challenges, I propose to develop a new generic approach to generate functional based machine learning FF for molecule-surface interactions based on symbolic regression. This approach promises significant advances over the existing FF both in terms of the accuracy and computational inexpensiveness (order of magnitude faster implementation than the existing FF). I will first apply the proposed bottom-up approach to develop a FF for Amino-Acid (and Nucleobases) adsorption to homogeneous surfaces like gold and graphene. Later, I will extend the proposed method to include the inhomogeneous surfaces (e.g. silica) under the same framework and provide a WORKFLOW tool to the community to generate FF for arbitrary molecule-surface combinations. Keeping in mind the relevance of molecule-surface interaction in applications like drug delivery, chromatographic separation etc., this proposal aims to deliver key technological advances with the ultimate ambition to provide innovative solutions to key modern societal challenges - health and well-being.

• DFG Programme: Research Grants