Rational solvent selection remains a significant challenge in process development.

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

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Comprehensive mechanistic insights into the aqueous-phase hydrogenolysis of glycerol by the ReO x –Ir catalyst were obtained by combining density functional theory (DFT) calculations with batch reaction experiments and detailed characterization of the catalysts using X-ray diffraction, X-ray photoelectron spectroscopy, and Fourier transform infrared techniques. The role and contribution of the aqueous acidic reaction medium were investigated using NMR relaxometry studies complemented with molecular dynamics and DFT calculations. At higher glycerol concentration, the enhanced competitive interaction of glycerol with the catalyst improved the conversion of glycerol. Sulfuric acid increased the concentration of glycerol within the pores of the catalyst and enhanced the propensity for dissociative adsorption of glycerol on the catalyst, explaining the promotional effect of acid during hydrogenolysis. Partially reduced and dispersed Brønsted acidic ReO x clusters on metallic Ir nanoparticles facilitated dissociative attachment of glycerol and preferential formation of the primary propoxide. The formation of the dominant product, 1,3-propanediol (1,3-PDO), results from the selective removal of the secondary hydroxyl of glycerol, with a comparatively low activation barrier of 123.3 kJ mol–1 in the solid Brønsted acid-catalyzed protonation–dehydration mechanism or 165.2 kJ mol–1 in the direct dehydroxylation mechanism. The formation of 1-propanol (1-PO) is likely to follow a successive dehydroxylation pathway in the early stages of the reaction. Although 1,3-PDO is less reactive than 1,2-propanediol (1,2-PDO), it preferentially adsorbs on the catalyst in a mixture containing glycerol to form 1-PO. The thermodynamically favorable pathway involving dehydrogenation, dehydroxylation, and hydrogenation elementary steps led to the dominant production of 1,2-PDO on pure Ir catalyst with a high C–O bond cleavage barrier of 207.4 kJ mol–1. The optimum ReO x –Ir catalyst with an Ir/Re ratio of 1 exploits the synergy of the sites of both the components. The detailed insights presented here would guide the rational selection of catalysts for the hydrogenolysis of polyols and the optimization of reaction parameters.

A modification to the mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed with the aim of identification of physical models from noisy experimental data. In the proposed formulation, a binary tree in which equations are represented as directed, acyclic graphs, is fully constructed for a pre-defined number of layers. The introduced modification results in the reduction in the number of required binary variables and removal of redundancy due to possible symmetry of the tree formulation. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the numbers of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. Future work will focus on addressing the limitations of the present formulation and solver to enable extension of target problems to larger, more complex physical models.

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Cationic copolymerizations of 4-methyl-2-methylene-1,3-dioxane, 2 (M 1 ), with 2-methylene-1,3-dioxane, 1 (M 2 ); of 4,4,6-trimethyl-2-methylene-1,3-dioxane, 3 (M 1 ), with 2-methylene-1,3-dioxane, 1 (M 2 ); of 4-methyl-2-methylene-1,3-dioxolane, 5 (M 1 ), with 2-methylene-1,3-dioxolane, 4 (M 2 ); and of 4,5-dimethyl-2-methylene-1,3-dioxolane, 6 (M 1 ), with 2-methylene-1,3-dioxolane, 4 (M 2 ) were conducted. The reactivity ratios for these four types of copolymerizations were r 1 Å 1.73 and r 2 Å 0.846; r 1 Å 2.26 and r 2 Å 0.310; r 1 Å 1.28 and r 2 Å 0.825; r 1 Å 2.23 and r 2 Å 0.515, respectively. The relative reactivities of these monomers towards cationic polymerization are: 3 ú 2 ú 1; and 6 ú 5 ú 4. With both five-and six-membered ring cyclic ketene acetals, the reactivity increased with increasing methyl substitution on the ring.