Project Details
Projekt
High Throughput Enabled Optimization of Machine Learning for Property Targeted Spiropyran Design
Subject Area
Organic Molecular Chemistry - Synthesis and Characterisation
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 497206593
This project proposes a machine learning (ML) assisted approach to systematically advance the targeted design of spiropyran photoswitches, aiming to optimize their functional properties for diverse applications. Building on promising findings in the first funding period, we will develop a fully automated high-throughput synthesis and analysis platform that facilitates the rapid generation and evaluation of a comprehensive library of spiropyran derivatives. Central to this platform is our newly developed dynamic spiropyran exchange reaction, which will allow us to create a substantially enlarged mixed library of photoswitches, thus enabling the efficient synthesis and characterization of structurally diverse photoswitches without isolating individual derivatives. This high-throughput approach will provide a rich dataset of molecular structures and their corresponding switching properties, facilitating the development of advanced ML models for precise prediction and targeted optimization in photoswitch design. We will explore known and new substitution patterns on the spiropyran scaffold, expanding into previously inaccessible regions of chemical space to create photoswitches with enhanced and possibly unique properties. To deepen theoretical insights into spiropyran and merocyanine behavior, we will employ machine learning force fields (MLFFs) to develop new computational descriptors, which offer a more cost-effective alternative to ab initio methods. This will enable detailed mapping of potential energy surfaces and precise estimation of thermal half-lives of the metastable merocyanine isomers, providing a refined theoretical foundation for the design of optimized photoswitches. In addition, we aim to extend ML models to incorporate environmental factors such as viscosity and pH, which are essential for designing molecular photoswitches tailored to complex settings, including biological environments and 3D printing matrices. By combining high-throughput experimental methods with cutting-edge ML-based theoretical modeling, this project will generate an integrated understanding of structure-property relationships, which will guide the rational design and synthesis of next-generation photoswitches specifically engineered for real-world applications across technology and science domains.
DFG Programme
Priority Programmes
