This project exploits the synergy between the trending area of artificial molecular machines and cutting edge computational chemistry approaches. Specific emphasis is placed on photoswitchable catalysts, which respond to external stimuli with a conformational or configurational change. These controllable motions allow catalytic function to be turned ON/OFF in a switch type fashion by opening/hindering access of a substrate to a catalytic site. On one hand, the rich morphology and chemistry of these smart catalysts calls for computational insights and design principles that complement experiment and push the field forward. On the other hand, the inherent complexity of these highly fluxional molecules makes them perfect subjects for driving modern quantum chemistry out of its comfort zone. To benefit from this synergy, the latest innovations in quantum chemistry-based machine learning techniques will be combined with methods capable of thoroughly mapping the intricate chemistry of molecular actuators. Overall, we aim to bridge the gap between the current state-of-the-art, which has reached reasonable quantum chemical accuracy for rigid medium size organic molecules, and more challenging fluxional architectures. The proposed methodological toolbox will be applied to the field of smart catalysis where general strategies for improving the efficiencies and enhancing enantioselectivity will be formulated. Thus, this project involves exploiting a wide range of modern computational approaches to chemical tasks that are broadly relevant to flexible/switchable catalytic systems. The anticipated output will furnish the computational chemistry community with a comprehensive array of novel next-generation approaches with applicability beyond the field of molecular machines.
Field of science
- /natural sciences/chemical sciences/physical chemistry/quantum chemistry
- /natural sciences/computer and information sciences/artificial intelligence/machine learning
Call for proposal
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