Integrating Electrochemical and Statistical Analysis Tools for Molecular Design and Mechanistic Understanding

Abstract

Conspectus Medicinal chemistry campaigns set the foundation for streamlined molecular design strategies through the development of quantitative structure–activity models. Our group’s enduring underlying interest in reaction mechanism propelled our adaption of a similar strategy to unite mechanistic interrogation and catalyst optimization by relating reaction outputs to molecular descriptors. Through collaborative opportunities, we have recently expanded these predictive statistical modeling tools to electrocatalysis and the design of redox-active organic molecules for application as electrolytes in nonaqueous redox flow batteries. Utilizing small, strategically designed data sets for a given core structure, we develop predictive statistical models that enable rapid virtual screening campaigns to identify analogues with enhanced properties. This process relates structural parameters to the output of interest, providing insight into the structural features that influence the output under study. Furthermore, the weighting of the coefficients for each parameter in the model can furnish mechanistic insight. Such a synergistic implementation of experimental and computational tools for mechanistic insight provides a means of forecasting properties of analogues without necessitating the synthesis and analysis of each molecule of interest. Through collaborative efforts, we have demonstrated the effectiveness of these tools for predicting diverse outputs such as stability, redox potential, and nonaqueous solubility. In this Account, we outline our entry into the field of organic electrochemistry and the implementation of statistical modeling tools for designing organic electrolytes. Through these projects we were exposed to the power of electrochemical techniques as a mechanistic tool, which has provided access to critical information that would otherwise be difficult to obtain. Utilizing electroanalytical techniques, we have quantified the rates of disproportionation of a variety of cobalt complexes and developed statistical models that provide critical insight into understanding of fundamental processes involved in the disproportionation of organometallic complexes. Electroanalytical tools have also been effective in elucidating the active catalyst oxidation state in different catalytic organometallic systems for C–H functionalization. Thus, our foray into electrolyte design and electrocatalysis, in which the statistical modeling tools developed for mechanistic insight were applied in a new context, came full circle to the core foundation of our group: mechanistic understanding.

Electrochemical Advances in Non‐Aqueous Redox Flow Batteries

“…Recently, however, through the pioneering work of the Sigman and Sanford groups, these relationships have been shown to be effective for the development of multivariate linear regression (MLR) models to understand and predict the behavior of electrolyte materials for NAORFBs. These techniques and the general workflow for model development have been described in a recent Accounts of Chemical Research article by the Sigman group and is shown in Figure 7 [41] …”

“…These techniques and the general workflow for model development have been described in a recent Accounts of Chemical Research article by the Sigman group and is shown in Figure 7. [41] Briefly, predictive model generation for any property of interest generally follows the following workflow: (i) strategic data set isolation and characterization, (ii) quantum mechanical calculations to isolate desired parameters, (iii) statistical model development, and (iv) model extrapolation to generate highly desirable molecules which optimize the response value of interest. To describe this workflow, (i) an appropriate data set must first be isolated in order to "train" the computational model to be developed.…”

New Redox Strategies in Organic Synthesis by Means of Electrochemistry and Photochemistry

“…Electroanalytic methods, including voltammetry, spectroelectrochemistry, hydrodynamic electrodes, and scanning electrochemical microscopy, are powerful tools for investigating reaction mechanisms involving electron transfer events. 49 Recently, these analytical techniques that are widely used in energy research have seen increasing applications in organic synthetic systems. For example, voltammetry can reveal the redox properties of reactive species and provides kinetic information for reactive intermediates that are difficult to access using chemical techniques.…”

Development and Molecular Understanding of a Pd-catalyzed Cyanation of Aryl Boronic Acids Enabled by High-Throughput Experimentation and Data Analysis

“…[25][26][27] These robotized approaches are particularly auspicious as they enable obtaining reproducible data sets that, in concert with statistical analysis tools, allow for correlating reaction outputs to specific physico-chemical properties. [28][29][30] While recently, random forest models have gained attention for being excellent for prediction, 31 such models often tend to be difficult to interpret. 32 Hence alternative, multivariate linear and polynomial regression analyses enjoy great popularity in both industry and academia, 33 owing to their ease of interpretability.…”