Project Details
Projekt
Design of photocatalytic systems for CO2 reduction driven by synergistic cooperation of machine learning and automated labs
Subject Area
Organic Molecular Chemistry - Synthesis and Characterisation
Inorganic Molecular Chemistry - Synthesis and Characterisation
Methods in Artificial Intelligence and Machine Learning
Inorganic Molecular Chemistry - Synthesis and Characterisation
Methods in Artificial Intelligence and Machine Learning
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 561374426
The project "Design of photocatalytic systems for CO2 reduction driven by synergistic cooperation of machine learning and automated labs" aims to accelerate the discovery of efficient photosensitizers and catalysts for the reduction of CO2 to valuable chemicals such as carbon monoxide and methane, addressing both climate change and sustainable feedstock generation. Conventional approaches to catalyst development rely on time-consuming trial-and-error experimentation, limiting the ability to explore the vast chemical space of potential candidates and limiting the gain of knowledge from the conducted experiments. This project overcomes these limitations by combining machine learning and explainable artificial intelligence methods with the automated experimental platform ChemASAP. Specifically, the project will integrate automated synthesis and testing in a self-driving laboratory with state-of-the-art ML methods, enabling the rapid and systematic exploration of photosensitizer and catalyst candidates. The project is structured into three key objectives: (1) development of a machine learning framework to predict essential properties of photosensitizers and catalysts, such as redox potential, photostability, and selectivity, using graph neural networks (GNNs) with built-in explainability features; (2) exploration of a large chemical space to identify promising molecules, using active learning and data- and explanation-driven optimization algorithms to efficiently guide experiments; and (3) integration of the self-driving laboratory to perform closed-loop optimization of compounds and catalytic conditions, where experimental data continuously updates the ML model to refine both molecular structures and reaction conditions. The research combines the cheminformatics and chemistry expertise of Nicole Jung and Stefan Bräse and the machine learning know-how of Pascal Friederich. Two jointly supervised PhD students will contribute: One focusing on molecular templates, automated synthesis, and laboratory automation, while the other on computational tasks, including high-throughput DFT calculations, as well as development, training, and integration of explainable graph neural network models and optimization algorithms. Both PhDs will be strongly supported by the automation and software development teams established at KIT. By fully automating both experimental and computational aspects, this project will not only accelerate the discovery of catalysts for CO2 reduction but also generate broadly reusable tools and methodologies, benefiting the molecular machine learning and chemistry communities at large. The findings are expected to advance the field of AI-driven catalysis, providing insights into efficient CO2 reduction and contributing to broader efforts in carbon capture and sustainable chemical production.
DFG Programme
Priority Programmes
