Machine Learning for Molecular-Precision Design of Multifunctional Materials

• Applicants: Professor Dr. Thomas Bocklitz; Privatdozent Dr. Martin Presselt
• Subject Area: Physical Chemistry of Molecules, Liquids and Interfaces, Biophysical Chemistry ; Analytical Chemistry ; Methods in Artificial Intelligence and Machine Learning
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 561190461

The behavior of molecules at interfaces and within membranes is critical to many fields, from photoenergy conversion to cell membrane biochemistry and pharmacology. Although synthetic chemistry has developed sophisticated methods for molecular functionalization, predicting molecular behavior at interfaces and in complex or biological environments remains a challenge. Here, machine learning (ML) can make a significant impact, offering potential for predictive models of molecular behavior in these settings - yet it requires extensive, diverse datasets that are often challenging to generate. Our project addresses this need by investigating the behavior of functionalized molecules, both in pure form and in mixtures, within molecular monolayers at the air-water interface. These two-dimensional layers serve as ideal model systems, allowing us to integrate molecular modeling with ML to gain insights into molecular behavior in complex environments. Collaborating within SPP, we aim to develop modular ML methods that systematically analyze supramolecular structure formation, ultimately creating new ML models based on these insights. This project will generate reusable ML tools and methodologies that bridge molecular chemistry and ML. Expected outcomes include a software suite for the automated production of molecular layers and innovative molecular representations. The necessary large, independent datasets will be produced through the combined use of roll-to-roll and Langmuir-Blodgett techniques, both of which offer precise molecular layer formation at the air-water interface. A novel suite of diverse in-situ characterization techniques will provide essential spectral and microscopic data. Through this project, we will establish a new platform for the fabrication, characterization, and data-driven modeling of molecular ensembles at interfaces. The resulting data will provide critical insights into the behavior of functionalized molecules in complex, dynamic environments and support the advancement of AI models in molecular prediction.

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