Understanding the interaction of organic molecules and metal ions by robot-based high-throughput experimentation and molecular machine-learning

• Applicants: Professorin Dr. Stefanie Gräfe; Professor Dr. Ulrich S. Schubert
• Subject Area: Organic Molecular Chemistry - Synthesis and Characterisation ; Theoretical Chemistry: Molecules, Materials, Surfaces
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 497115849

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. Experimentally, a robot-based system is employed in order to test several combinations. Titration experiments are investigated by several online as well as offline techniques and the obtained data can directly be utilized for machine-learning purposes. Furthermore, DFT calculations are applied for a theoretical understanding of the interaction of organic molecules and certain metal ions. These data are compared with the experimental results and are also the basis of the machine-learning.Thus, 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.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning