The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

中文翻译：

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利用分子动力学模拟和卷积神经网络快速预测液相酸催化的反应速率

与将生物质升级为高价值化学品有关的液相，酸催化反应的速率对溶剂组成高度敏感，而确定合适的溶剂混合物在理论和实验上均具有挑战性。我们表明，可以通过3D卷积神经网络利用经典分子动力学模拟生成的反应物-溶剂环境的复杂原子构型，从而能够准确预测模型生物质化合物的布朗斯台德酸催化反应速率。我们开发了3D卷积神经网络，我们称为SolventNet，并使用实验反应数据和相应的分子动力学模拟数据，对水-溶剂混合物中的7种生物质衍生的含氧化合物进行训练，以预测酸催化的反应速率。我们表明，SolventNet可以预测其他反应物和溶剂系统的反应速率，比以前的模拟方法快一个数量级。机器学习与分子动力学的这种结合使得可以快速，高通量筛选溶剂系统，并确定改善的生物质转化条件。