The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene<br>

The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene<br>

Purpose The rapid spread of SARS-CoV-2, the virus that is responsible for causing COVID-19, has presented the medical community with another example of when convalescent plasma (CP) is still used today. The ability to standardize CP at the onset of a pandemic is unlikely to exist in a reliable and uniformly reproducible way. We hypothesized that CP of unknown strength given in a serial manner will promote health and reduce mortality in those inflicted with COVID-19. Methods Participants were given up to 8 CP-units depending on their condition upon entry into the study and their response. Results 102 out of 117 participants were given CP. The earlier a participant received CP corelated with survival (p = 0.0004). The number of CP-units given, throughout all the clinical severities, was not significant with outcomes, p = 0.3947. A higher number of CP-units given to the severe/critical participants (without biological immunosuppressants or restrictive lung disease) did correlate with survival p = 0.0116 (2.8 vs. 2 units). Lower platelets on admission corelated with mortality. Platelet levels increase correlated with CP infusions p < 0.0001. Conclusion This study supports the serial use of CP of unknown strength based on clinical response for those infected with COVID-19. The use of 3–4 units of CP was found to be statistically significant for survival for severe and critical participants without restrictive lung disease and chronic biological immunosuppression. Increased platelet levels after CP infusions supports that CP is promoting overall health regardless of outcomes.