论文标题
机器学习催化表面反应的量子通量通量相关功能
Machine learning the quantum flux-flux correlation function for catalytic surface reactions
论文作者
论文摘要
为有机异质催化表面反应构建了完全量子通量升级相关函数和反应速率常数的数据集。高斯工艺回归变量已成功拟合到训练数据中，以预测先前看不见的测试集反应速率恒定产物和通量 - 升华相关函数的cauchy拟合。对于测试集反应速率恒定产物的最佳回归预测，最佳回归预测的绝对百分比为0.5％，测试集磁通量相关函数为1.0％。当查看新温度和以前看不见的反应的反应性时，高斯工艺回归器都是准确的，并且对通量 - 升线相关函数的计算要求的时间传播提供了显着的加速。
A dataset of fully quantum flux-flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to predict previously unseen test set reaction rate constant products and Cauchy fits of the flux-flux correlation function. The optimal regressor prediction mean absolute percent errors were on the order of 0.5% for test set reaction rate constant products and 1.0% for test set flux-flux correlation functions. The Gaussian process regressors were accurate both when looking at kinetics at new temperatures and reactivity of previously unseen reactions and provide a significant speedup respect to the computationally demanding time propagation of the flux-flux correlation function.
