Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
复制标题
机器学习中金属表面反应的广义布伦斯特德-埃文斯-波兰尼关系
DOI：
10.1002/cctc.202201108
复制
发表时间：
2022
期刊：
影响因子：
4.5
通讯作者：
Mavrikakis, Manos
中科院分区：
文献类型：
--
作者：
Göltl, Florian;Mavrikakis, Manos
Brønsted-Evans-Polanyi (BEP) 关系，i。 即反应能和活化能之间的线性比例，是多相催化剂计算设计的核心。然而，BEP 并不通用，通常需要对每一类反应重新参数化。在这里，我们构建了广义 BEP (gBEP)，它可以预测金属表面上含 C、O、N 和 H 的分子反应的不同数据集的活化能。第一步，我们开发一组基于比例关系的描述符，可以捕获反应过程中反应物化学特性的变化。随后，我们使用反应能、这些描述符和表面结构的单个描述符来参数化基于机器学习的回归方法，以预测激活能。我们开发的最佳方法显示，训练集（数据集的 80%）的平均绝对误差 (MAE) 为 0.11 eV，测试集（数据集的 20%）为 0.23 eV。这里介绍的方法允许在典型的个人计算机上在几分之一秒内计算活化能，并且由于其通用性、准确性和应用简单性，它可能被证明在过渡金属催化剂设计中有用。
Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.