Microkinetic, in situ analytics, and empirical modeling approaches for developing intrinsic CO2 photoreduction kinetics are presented in this Perspective. Intrinsic kinetic models that are independent of photoreactor geometry are critical for scaling CO2 photoreduction photoreactors. Successfully scaling CO2 photoreduction is limited using the current extrinsic CO2 photoreduction kinetic models described in this Perspective, because they are dependent on the photoreactor geometry and scale used. The impact of different photoreactor geometries and light transport that lead to extrinsic kinetic models is reviewed. The impacts of temperature and pressure on surface diffusion is highlighted as additional important process parameters. The current Langmuir–Hinshelwood-based kinetic models are discussed, and their limitations are highlighted, with respect to modeling the possible deactivation of the photocatalyst. With a view on developing an intrinsic kinetic model, the challenges for developing CO2 photoreduction kinetics are highlighted and discussed with reference to the current extrinsic CO2 photoreduction kinetic model examples found in the literature. Robust analytical methods for collecting CO2 photoreduction kinetic data and for confirming the carbon source are discussed. The false positive production from adventitious carbon and organic impurities introduced during the synthesis and/or coating of photocatalysts with solvents and degradation of photoreactor components is highlighted as a challenge to collecting CO2 photoreduction kinetic data. It is shown that a wide range of the kinetic model coefficient values are possible when using a multistart, genetic algorithm, or particle swarm approach for estimating nonlinear model coefficients. Finally, an easy to test and implement approach using a mean median multistart and trust-region reflective algorithm method is presented for the estimation of nonlinear CO2 photoreduction kinetic model coefficients.

Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

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