Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R², (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

中文翻译：

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用有界不确定性估计预测自由基热化学的子图同构决策树

详细的化学动力学模型为工业应用提供了有价值的机械见解。自动生成可靠的动力学模型需要快速、准确的自由基热化学估计。动力学家通常更喜欢从闭壳分子到相应基团的氢键增量（HBI）校正，因为它们的可解释性、物理意义以及作为相对量的误差消除的便利性。由于数据有限，树木估算器目前依赖于专家知识和手动构建，这给维护和改进带来了挑战。在这项工作中，我们扩展了最初为速率估计开发的子图同构决策树（SIDT）算法来估计 HBI 校正。我们引入了物理感知的分裂准则，探索了有界加权不确定性估计方法，并评估了基于任意不确定性和基于模型方差减少的预剪枝方法。此外，我们根据最近发表的作品的量子化学计算，编制了涉及 C、O、N 和 H 的 2210 个自由基的热化学参数数据集。我们利用收集的数据集来训练 SIDT 模型。与现有的经验树估计器相比，SIDT 模型 (1) 提供了一种自动方法来生成和扩展热化学树估计器，(2) 具有更好的准确性和R ²，(3) 提供了更现实的不确定性估计，并且 (4 ）具有在下降速度方面更有优势的树结构。总体而言，SIDT 估计器标志着动力学建模的巨大飞跃，为自由基热化学提供了更精确、可靠和可扩展的预测。