Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.

Over the past two decades, there have been major developments in transition metal-catalyzed aminations of aryl halides to form anilines, a common structure found in drug agents, natural product isolates, and fine chemicals. Many of these approaches have enabled highly efficient and selective coupling through the design of specialized ligands, which facilitate reductive elimination from a destabilized metal center. We postulated that a general and complementary method for C–N bond formation could be developed through the destabilization of a metal amido complex via photoredox catalysis, thus providing an alternative approach to the use of structurally complex ligand systems. Herein, we report the development of a distinct mechanistic paradigm for aryl amination using ligand-free nickel(II) salts, in which facile reductive elimination from the nickel metal center is induced via a photoredox-catalyzed electron-transfer event.

Ruthenium(II) polypyridyl complexes promote the efficient radical cation Diels–Alder cycloaddition of electron-rich dienophiles upon irradiation with visible light. These reactions enable facile [4+2] cycloadditions that would be electronically mismatched under thermal conditions. Key to the success of this methodology is the availability of ligand-modified ruthenium complexes that enable the rational tuning of electrochemical properties of the catalyst without significantly perturbing the overall photophysical properties of the system.

Understanding the practical limitations of chemical reactions is critically important for efficiently planning the synthesis of compounds in pharmaceutical, agrochemical, and specialty chemical research and development. However, literature reports of the scope of new reactions are often cursory and biased toward successful results, severely limiting the ability to predict reaction outcomes for untested substrates. We herein illustrate strategies for carrying out large-scale surveys of chemical reactivity by using a material-sparing nanomole-scale automated synthesis platform with greatly expanded synthetic scope combined with ultrahigh-throughput matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS).

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