Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models.
Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models.
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DOI：
10.1021/acs.jpca.3c01430
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Dmitrij Rappoport
中科院分区：
文献类型：
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作者：
Dmitrij Rappoport
在人工管理的计算反应数据库中，选择偏差是不可避免的，但会对量子化学方法的泛化性和源自这些数据集的机器学习模型产生重大影响。在这里，我们提出准反应子图作为反应机制的离散的、基于图的表示，它具有定义良好的关联概率空间，并允许使用图核的相似函数。因此，准反应子图非常适合于构造具有代表性或多样化的反应数据集。准反应子图被定义为由反应物和生成物节点之间所有最短路径组成的正式断键和成键网络（过渡网络）的子图。然而，由于它们的纯几何结构，它们不能保证相应的反应机制在热力学和动力学上是可行的。因此，采样后必须应用可行（反应子图）和不可行的（非反应子图）的二分类。本文描述了含6个非氢原子的CHO跃迁网络的准反应子图的构造和性质，并刻画了准反应子图的统计性质。我们使用Weisfeiler-Lehman图核来研究它们的聚类。
Selection bias is inevitable in manually curated computational reaction databases but can have a significant impact on generalizability of quantum chemical methods and machine learning models derived from these data sets. Here, we propose quasireaction subgraphs as a discrete, graph-based representation of reaction mechanisms that has a well-defined associated probability space and admits a similarity function using graph kernels. Quasireaction subgraphs are thus well suited for constructing representative or diverse data sets of reactions. Quasireaction subgraphs are defined as subgraphs of a network of formal bond breaks and bond formations (transition network) composed of all shortest paths between reactant and product nodes. However, due to their purely geometric construction, they do not guarantee that the corresponding reaction mechanisms are thermodynamically and kinetically feasible. As a result, a binary classification of feasible (reaction subgraphs) and infeasible (nonreactive subgraphs) must be applied after sampling. In this paper, we describe the construction and properties of quasireaction subgraphs and characterize the statistics of quasireaction subgraphs from CHO transition networks with up to six non-hydrogen atoms. We explore their clustering using Weisfeiler-Lehman graph kernels.