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Pushing Quantum Chemistry by Advancing Photoswitchable Catalysis

Periodic Reporting for period 2 - PushQChem (Pushing Quantum Chemistry by Advancing Photoswitchable Catalysis)

Okres sprawozdawczy: 2021-04-01 do 2022-09-30

As a central pillar of chemical sciences, a considerable amount of research effort is placed into developing and tuning catalytic processes with the aim of further expanding their utility in diverse areas ranging from pharmaceuticals, commodity chemicals to transportation fuels. Demand for new and better catalysts means developing species with higher activities, longer lifetimes, higher turnover frequencies, and increased control of selectivity (i.e. which product is preferentially formed) over an increasingly broad range of chemical reactions. Within this context, a special class of catalysts that are photoswitchable constitutes an exciting alternative to standard catalysts. Through the utilisation of a molecular switching unit, photoswitchable catalysts respond to light with a change of shape or orientation which can in principle afford enhanced control of the spatial orientation of the reagents to be chemically transformed. Yet, “smart” catalysis is an emerging field raising many technical and scientific questions associated with both the ability to control the switching between different configurational (ON/OFF, i.e. active/inactive) states and with the erosion of the activity or selectivity after incorporation of the molecular switch. Some of these challenges are ideally suited to be resolved in silico under the conditions that new computational chemistry approaches are developed.

The goal of PushQChem is to leverage these smart catalytic systems to drive modern quantum chemistry out of its comfort zone in order to tackle the complexities of characterising, understanding, controlling, and discovering smart catalytic systems.
PushQChem is carried out by a highly multidisciplinary team of scientists with expertise in quantum chemistry, molecular dynamic simulations, machine learning algorithms, data science and/or catalysis. During the period covered, we advanced state-of-the-art atomistic machine learning techniques to have access to a variety of fundamental static and dynamic properties relevant to functional organic molecules at a cost orders of magnitude smaller than with the explicit quantum chemical computations. We also built OSCAR: An Extensive and Modular Repository of Chemically and Functionally Diverse Organocatalysts, which serves as a starting point to train the developed machine learning models as well as to define the combinatorial space exploited in bottom-up and top-down catalyst design protocols. These combinations of tools are now being used to to define the rules for more active, selective and stable (photoswitchable) organocatalysts for reactions catalyzed by Lewis base or hydrogen-bonds.
PushQChem has the ambitious goal to redefine the range of applicability and capabilities of computational chemistry for advancing the field of catalysis. We are developing a new generation of atomistic machine learning frameworks that enable the predictions of highly challenging catalytic properties, which would have been inaccessible using existing computational methods. Looking into the future, we hope to demonstrate that these computational development are useful to design a class of smart catalysts with long term stability, high activity and selectivity.

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