Chew Alex K, Jiang Shengli, Zhang Weiqi, Zavala Victor M, Van Lehn Reid C
Department of Chemical and Biological Engineering, University of Wisconsin-Madison Madison WI 53706 USA
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison Madison WI 53706 USA.
Chem Sci. 2020 Oct 19;11(46):12464-12476. doi: 10.1039/d0sc03261a.
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.
与将生物质升级为高价值化学品相关的液相酸催化反应速率对溶剂组成高度敏感，识别合适的溶剂混合物在理论和实验上都具有挑战性。我们表明，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可被三维卷积神经网络利用，以实现对模型生物质化合物的布朗斯特酸催化反应速率的准确预测。我们开发了一种三维卷积神经网络，称为溶剂网络（SolventNet），并使用水 - 共溶剂混合物中七种生物质衍生含氧化合物的实验反应数据和相应的分子动力学模拟数据对其进行训练，以预测酸催化反应速率。我们表明，溶剂网络预测其他反应物和溶剂系统反应速率的速度比先前的模拟方法快一个数量级。这种机器学习与分子动力学的结合能够快速、高通量地筛选溶剂系统并确定改进的生物质转化条件。
