Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.

The use of data science tools to provide the emergence of non-trivial 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.

Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene.

Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene () results in the loss of N and the formation of bicyclo products (exo) and (endo) in a nonstatistical ratio, with preference for the exo product.

Catalysis and chemical mechanisms of calcite dissolution in seawater.

Near-equilibrium calcite dissolution in seawater contributes significantly to the regulation of atmospheric [Formula: see text] on 1,000-y timescales. Despite many studies on far-from-equilibrium dissolution, little is known about the detailed mechanisms responsible for calcite dissolution in seawater. In this paper, we dissolve C-labeled calcites in natural seawater.