Data-powered augmented volcano plots for homogeneous catalysis

Abstract

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality...

Accurate and rapid prediction of pKa of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

“…Data-driven or semiempirical quantum chemical approaches can be used to circumvent the low throughput of DFT for predicting geometries and thermochemical properties. [7][8][9][10][11] Recently a GFN2-xTB method (the latest one from the GFN(n) family), based on the density functional tight-binding approach has been introduced for the rapid prediction of geometry and thermochemical properties of TM complexes. 12 However, because of its semiempirical nature, the accuracy of the GFN2-xTB is fundamentally limited by the thermochemical span of the training set of molecules and the level of theory used in the parametrization.…”

Accurate and Rapid Prediction of pKa of Transition Metal Complexes: Semiempirical Quantum Chemistry with a DataAugmented Approach

“…Data-driven or semiempirical quantum chemical approaches can be used to circumvent the low throughput of DFT for predicting geometries and thermochemical properties. [7][8][9][10][11] Recently a GFN2-xTB method (the latest one from the GFN(n) family), based on the density functional tightbinding approach has been introduced for the rapid prediction of geometry and thermochemical properties of TM complexes. 12 However, because of its semiempirical nature, the accuracy of the GFN2-xTB is fundamentally limited by the thermochemical span of the training set of molecules and the level of theory used in the parametrization.…”

Computational Organometallic Catalysis: Where We Are, Where We Are Going

“…The total data set used consisted of 1510 catalytic cycles derived from DFT computations and 491 catalytic cycles derived from machine learned profiles. [62] Electroanalytical techniques have been combined with parameterization tools, which include DFT calculations, to uncover reaction mechanism in redox catalysis. [63]…”

“…Trends surrounding the thermodynamics of the hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands have been analyzed using a data‐driven inspired workflow (data‐powered volcano plots). The total data set used consisted of 1510 catalytic cycles derived from DFT computations and 491 catalytic cycles derived from machine learned profiles [62] . Electroanalytical techniques have been combined with parameterization tools, which include DFT calculations, to uncover reaction mechanism in redox catalysis [63] …”