Schwerpunktprogramm (SPP 2363) “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning
From Fundamentals to Application and Beyond
Gefördert von der Deutschen Forschungsgemeinschaft e. V. (DFG)
Laufzeit: 2022 bis 2024
The interaction of transition metal ions and organic molecules in solution will be investigated using a machine-learning approach. So far, openly reported systematic massive data on these systems are sparse, preventing from an efficient use of machine-learning approaches. Within this project, we address this challenge by generating high throughput data, both experimentally, employing modern robot-based approaches, and theoretically, by utilizing DFT calculation on fast GPU-based DFT programs.
Within this project, we will not only generate large amount of data (experimentally and theoretically), which can be individually utilized employing methods of machine-learning to identify correlations but, moreover, to also cross-correlate theoretical and experimentally obtained data.
The aim of this systematic study is to predict the interaction of an organic molecule and a metal ion by just using the chemical structure of the molecule and the sort of metal ion. These results could therefore be highly interesting for the development of new drugs, catalysts or energy conversion moieties.