SKM 2023 – wissenschaftliches Programm
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 28: Molecular Electronics and Excited State Properties (joint session CPP/TT)
CPP 28.11: Vortrag
Mittwoch, 29. März 2023, 12:15–12:30, GÖR 226
Machine learning for molecular design of organic molecules and reaction optimization — •Julia Westermayr1, Reinhard J. Maurer2, and Detlev Belder3 — 1Artificial Intelligence in Theoretical Chemistry Group, Leipzig University, Germany — 2Computational Surface Chemistry Group, University of Warwick, UK — 3Analytical Chemistry, Leipzig University, Germany
High-throughput screening of reaction conditions and electronic properties of molecules plays a crucial role in chemical industry and can be facilitated by automated workflows and machine learning. However, the high combinatorial complexity of the various parameters affecting molecular properties leaves unguided searches in chemical space highly inefficient and the optimization of reactions to synthesize these molecules often fails as theoretical protocols are usually decoupled from experiments. In this talk, we will show how predictive and generative deep learning models can be combined to theoretically design new molecules with potential relevance to organic electronics [1]. Further, we will present how these tools can be coupled with experiments to enable automated micro-laboratories [2] for targeted chemical synthesis. [1] JW et al. Nat. Comp. Sci, in press (2023). [2] R. J. Beulig et al., Lab Chip 17, 1996 (2017).