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Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists’ knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is found within the top-10 predictions 69.6% of the time, with top-10 accuracies for individual species reaching 80–90%. Temperature is accurately predicted within ±20 °C from the recorded temperature in 60–70% of test cases, with higher accuracy for cases with correct chemical context predictions. The utility of the model is illustrated through several examples spanning a range of common reaction classes. We also demonstrate that the model implicitly learns a continuous numerical embedding of solvent and reagent species that captures their functional similarity., Machine learning model predicts conditions for organic synthesis reactions and quantifies solvent and reagent similarity.

Kinetic Monte Carlo (KMC) has become a well-established technique for simulating the kinetics of free radical polymerization, both to generate polymer molecular weight distributions and, more recently, to track the explicit monomer sequence in every chain. However, KMC simulations require a minimal number of molecules in order to accurately describe monomer conversion and macromolecular quantities, which can render them computationally prohibitive. In this work, we propose a novel approach for accelerating KMC simulations through scaling relationships that allow the number of molecules simulated to be reduced. Using the concept of the minimal number of molecules and an explicit expression we derived for copolymerization, we propose a factor that is used to scale the reaction rate constants which results in an acceleration of KMC simulations by a factor of ∼100. Furthermore, we demonstrate the limits of this scaling approach, revealing the absolute lower bound for the number of molecules used in KMC simula...