Intermolecular Interactions – Ab initio-Generated Force Fields and Machine Learning
Consists on defining the application ranges of established electronic structure methods for confined molecular systems, on new developments in electronic structure theory to characterize them, and on Machine Learning (ML)-based representations of the involved intermolecular interactions and applications. The meeting will cover the presentation of:
1) Theoretical tools aiming to calculate interaction potentials between molecular systems and complex environments, and between themselves, through a combination of high-level ab initio theories, state-of-the-art (e.g., dispersion-corrected and dispersion-less) density functional theory methods, and semi-empirical approaches.
2) New developments in electronic structure theory and ML-based representations of intermolecular interactions and applications. The focus will be on open-shell symmetry-adapted perturbation theory and highly accurate composite schemes to be used for benchmarking purposes.
3) Systematic density-functional-theory simulations for selected confined molecular systems and enhanced by machine-learning-parametrized force fields and algorithms to screen the most relevant energy potential landscapes, also validating and refining the DFT predictions on the basis of high-level ab initio results.