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.

中文翻译：

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集成电化学和统计分析工具以进行分子设计和机理理解。

药物化学运动通过开发定量结构-活性模型，为简化分子设计策略奠定了基础。我们小组对反应机理的持久兴趣促使我们采用了一种相似的策略，以通过将反应输出与分子描述子相关联来统一机械询问和催化剂优化。通过合作机会，我们最近将这些预测性统计建模工具扩展到了电催化和氧化还原活性有机分子的设计，以用作非水氧化还原液流电池中的电解质。利用给定核心结构的小型，经过战略设计的数据集，我们开发了预测统计模型，使快速的虚拟筛选活动能够识别具有增强特性的类似物。此过程将结构参数与感兴趣的输出相关联，从而深入了解影响研究中输出的结构特征。此外，模型中每个参数的系数加权可以提供机械方面的见解。这种用于机械洞察力的实验和计算工具的协同实现方式提供了一种预测类似物特性的方法，而无需合成和分析每个感兴趣的分子。通过合作，我们证明了这些工具可有效预测各种输出，例如稳定性，氧化还原电势和非水溶解度。在此帐户中，我们概述了我们进入有机电化学领域以及设计有机电解质的统计建模工具的实施。通过这些项目，我们接触了电化学技术作为一种机械工具的力量，从而可以获取否则将难以获得的关键信息。利用电分析技术，我们已经量化了各种钴配合物的歧化速率，并开发了统计模型，这些模型提供了对理解有机金属配合物歧化基本过程的关键见解。电分析工具在阐明用于CH官能化的不同催化有机金属体系中的活性催化剂氧化态方面也很有效。因此，我们涉足电解质设计和电催化领域，其中为机械性见识而开发的统计建模工具被应用于新的环境中，