2023 JCP-DCP Future of Chemical Physics Lecture. Talk Title: Towards a big-data ecosystem for quantum chemistry research of solvated molecular systems

Machine learning (ML) and big data play increasingly important roles in both experimental and theoretical studies of chemical physics. Although numerous critical chemical processes occur in the solution phase, datasets (computational or experimental) and machine-learning models for solution-phase molecular systems are still scarce. My research group’s objective is to overcome these challenges by building a big data ecosystem for quantum chemistry research of solvated molecular systems.



To enable the efficient generation of computational datasets of solvated molecular systems, we developed strategies to accelerate both the implicit and explicit solvent models for quantum chemistry calculations. For the implicit conductor-like polarization model (C-PCM), we developed algorithms on the graphical processing units (GPUs) to accelerate the calculation. For the explicit solvent model, we developed AutoSolvate, an open-source toolkit to streamline the QC calculation workflow of explicitly solvated molecules. To improve the accuracy of the generated datasets, we develop ML models to reduce the discrepancy between experimental measurements and computationally predicted molecular properties in both implicit and explicit solvent models. This ML correction technique has been applied to predict redox potential and absorption/fluorescence wavelength in the solution phase.

To make these tools more accessible, we are developing a web-based platform to offer automated simulations of solvated molecules on cloud computing resources and publicly disseminate the datasets to the computational molecular science communities.