Projects Phase 2

Project 1: Deep learning for quantum chemistry, esp. Orbital-Free Density Functional Theory

What is the kinetic energy of a given molecular ground state electron density? While the answer matters, and is known to …

Project 3: Thin-film morphology generation from flow matching

This project aims at generating thin-film morphologies for organic electronic materials using machine learning. The primary objective is to model complex, …

Project 4: Creating Transferable Reactive Machine Learning Potentials at Unrestricted Coupled Cluster

Machine learning models of atomic and molecular interactions are increasingly employed in atomistic simulations. Training these models requires large datasets derived …

Project 5: Dynamically interfacing machine learning and molecular mechanics (ML/MM) models for atomistic simulations

We plan to develop hybrid approaches combining accurate ML potentials and fast force fields to establish dynamic ML/MM simulations of …

Project 6: Development of NN/MM algorithms for excited-state dynamics, incorporating solvent effects in applications such as multi-chromophoric systems and complex excited-state processes

Machine learning models have proven highly successful in predicting ground-state potential energy surfaces (PES) of organic molecules and inorganic solids. In …