Project Details
Projekt
Foundational Implicit Solvent Machine Learning Potentials for Organic Molecules
Applicant
Professorin Julija Zavadlav, Ph.D.
Subject Area
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 561190767
Machine learning (ML), particularly the development of ML potentials, has dramatically advanced molecular modeling by enabling the prediction of potential energy with the accuracy of ab initio methods at a fraction of the computational cost. However, further improvements in computational efficiency are needed to tackle large spatiotemporal scales and high-throughput screening studies. The use of implicit solvent models can provide the necessary speed-ups, especially for simulations where the solvent constitutes a significant portion of the computational domain. To date, implicit solvent ML potentials have been developed for water. This project aims to extend these models to include non-aqueous solvents, opening the doors to numerous applications in chemical engineering, as well as in various fields of physical and medicinal chemistry that were previously inaccessible. On the other hand, the proposed novel ML architectures and training strategies, incorporating concepts from foundation models, will be of great value to data-driven molecular modeling. The project's software tool development will serve the broader scientific and industrial community by lowering the entry barrier to deploying implicit solvent ML potentials in everyday molecular simulations.
DFG Programme
Priority Programmes
