Mechanistic Inference from Statistical Models at Different Data-Size Regimes

Abstract

The chemical sciences are witnessing an influx of statistics into the catalysis literature. These developments are propelled by modern technological advancements that are leading to fast and reliable data production, mining, and management. In organic chemistry, models encoded with information-rich parameters have facilitated the formulation of mechanistic hypotheses across different data-size regimes. Herein, we aim to demonstrate through selected examples that the integration of statistical principles into homogeneous catalysis can streamline not only reaction optimization protocols but also mechanistic investigation procedures. Namely, we highlight how different aspects of molecular modeling, data set design, data visualization, and nuanced data restructuring can contribute to improving chemical reactivity and selectivity, while furthering our understanding of reaction mechanisms. By mapping out these techniques at different data set sizes, we hope to encourage the broad application of data-driven approaches for mechanistic studies regardless of the accessible amount of data.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

“…Not only do the reactions use different transition metals (i.e., Pd vs Rh), but the coordination geometries of putative reaction intermediates are also distinct. While the Heck reaction primarily involves square-planar Pd species, the hydroformylation reaction is thought to proceed through trigonal bipyramidal Rh intermediates …”

“…As showcased below, the designer maps can be applied to various downstream data science steps in multi-objective optimization campaigns. In addition to the reaction optimization applications presented (vide infra), it can be envisioned that this calculated parameter library and chemical space map could be used for many other potential applications such as training set design, novel ligand generation, and mechanistic understanding …”

Small Data Can Play a Big Role in Chemical Discovery

“…This data-size regime engenders fundamental restrictions in ML. [13] For example, many assumptions that are valid with big data, such as subtracting the mean and dividing by the standard deviation, tend to create artifacts. Familiarity with these issues defines the space of permissible statistical tools.…”

Cu Oxamate-Promoted Cross-Coupling of α-Branched Amines and Complex Aryl Halides: Investigating Ligand Function through Data Science

“…In addition, the high-quality data sets generated by HTE facilitate the use of statistical modeling techniques. − Such tools can be leveraged to interrogate which features of the ligand dictate reactivity and can thus inform the design of future ligands. Despite these advantages, it is relatively uncommon for C–N cross-coupling methods to be optimized for HTE compatibility, which may require the use of high boiling, polar solvents, and relatively dilute conditions (0.1 M or less).…”