Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

中文翻译：

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活化能模型图很容易达到不可约误差，但可转移性有限

竞争反应的活化能表征是理解不同反应途径、产物产率和反应系统的无数其他特性的动力学相关性的一个昂贵但关键的步骤。活化能表征的标准方法历来是使用可提供的最高水平的理论进行过渡态搜索。然而，最近，几个小组推广了直接基于反应物和产物图、足够复杂的神经网络和足够广泛的数据集来预测活化能的想法。在这里，我们使用最近开发的反应图深度 1 (RGD1) 过渡状态数据集和几个新开发的图注意力架构重新审视了这项任务。所有这些新架构都在保留的反应测试集上实现了类似的最先进结果，平均绝对误差约为 4 kcal/mol，但在由具有不同机制、反应分子数或反应物大小的反应组成的外部测试集上表现不佳分配。通过一系列案例研究，其他当代图到活化能架构也具有有限的可转移性。我们的结论是，一系列标准图架构已经可以实现与可用反应数据集的不可约误差相当的结果，但分布外性能仍然很差。