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Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time‐consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on‐line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron‐demand Diels–Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer‐readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning. [ABSTRACT FROM AUTHOR]

Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time‐consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on‐line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron‐demand Diels–Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer‐readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning. [ABSTRACT FROM AUTHOR]

Dynamic combinatorial chemistry is a method widely used for generating responsive libraries of compounds, with applications ranging from chemical biology to materials science. It relies on dynamic covalent bonds that are able to form in a reversible manner in mild conditions, and therefore requires the discovery of new types of these bonds in order to progress. Amides, due to their high stability, have been scarcely used in this field and typically require an external catalyst or harsh conditions for exchange. Compounds able to undergo uncatalysed transamidation at room temperature are still rare exceptions. In this work, we describe reversible amide formation and transamidation in a class of compounds known as maleamic acids. Due to the presence of a carboxylic acid in β‐position, these compounds are in equilibrium with their anhydride and amine precursors in organic solvents at room temperature. First, we show that this equilibrium is responsive to external stimuli: by alternating the additions of a Brønsted acid and a base, we can switch between amide and anhydride several times without side‐reactions. Next, we prove that this equilibrium provides a pathway for reversible transamidation without any added catalyst, leading to thermodynamic distributions of amides at room temperature. Lastly, we use different preparation conditions and concentrations of Brønsted acid to access different library distributions, easily controlling the transition between kinetic and thermodynamic regimes. Our results show that maleamic acids can undergo transamidation in mild conditions in a reversible and tunable way, establishing them as a new addition to the toolbox of dynamic combinatorial chemistry. [ABSTRACT FROM AUTHOR]