Project Details
Projekt
Development and application of improved ligand descriptors and representations for inverse catalyst design
Subject Area
Organic Molecular Chemistry - Synthesis and Characterisation
Inorganic Molecular Chemistry - Synthesis and Characterisation
Inorganic Molecular Chemistry - Synthesis and Characterisation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 497260357
The development of new transition metal catalysts and the corresponding ligands is of crucial importance for synthetic organic chemistry and a decisive factor for the development of new reactions and improved synthesis protocols. The design of tailor-made ligands for the specific requirements of different reactions is an extremely time and cost-intensive process. The development of structure-activity relationships can accelerate the optimization of catalysts. However, the simple use of individual parameters (descriptors) to quantify steric or electronic ligand properties has proven to be too one-dimensional to reliably and quantitatively describe the complex structure-activity relationships. The use of multivariate regression analyses and the application of machine learning methods have recently enabled first advances in the prediction of improved ligands. The goal of this research project is the inverse design of homogeneous catalysts using machine learning methods. Therefore, different strategies will be developed and tested for the design of ylide-substituted phosphines (YPhos) for palladium- and gold-catalyzed reactions. In the first step, we will develop new descriptors for the improved description of ligands and their properties. These descriptors aim to explicitly quantify secondary metal-ligand interactions, thereby overcoming the distinction between monophosphines and bidentate ligands, thus enabling the screening of a broader ligand space. The new descriptors will be generated by means of quantum chemical calculations and will be verified on the basis of experimental studies in order to subsequently enable a reliable prediction of the ligand properties that are decisive for the respective reaction. To predict optimal YPhos ligands for different reactions, the next step involves creating a large-scale virtual YPhos library. Searching this library for the optimal ligands will be made possible through the development of various ligand representations. Easily interpretable descriptors based on physicochemical properties, as well as powerful deep-learning representations, will be used and compared in terms of their efficiency. Additionally, the models will be combined with a classifier trained on experimental data to predict the synthesizability of the ligands. This approach ensures realistic predictions and thus the applicability of the models for inverse ligand design in experiment. In the final step, this will be evaluated using selected reactions in gold and palladium catalysis. Based on experimental data, ideal catalysts will be predicted using machine learning methods and subsequently be synthesized and evaluated.
DFG Programme
Priority Programmes
