Data enhanced Hammett-equation: reaction barriers in chemical space,Chemical Science

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Data enhanced Hammett-equation: reaction barriers in chemical space
Chemical Science ( IF 7.6 ) Pub Date : 2020-10-02 , DOI: 10.1039/d0sc04235h
Marco Bragato ₁ , Guido Falk von Rudorff ₁ , O Anatole von Lilienfeld _{1,

2}

Affiliation

It is intriguing how the Hammett equation enables control of chemical reactivity throughout chemical space by separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent, or temperature. We generalize Hammett's original approach to predict potential energies of activation in non aromatic molecular scaffolds with multiple substituents. We use global regression to optimize Hammett parameters ρ and σ in two experimental datasets (rate constants for benzylbromides reacting with thiols and ammonium salt decomposition), as well as in a synthetic dataset consisting of computational activation energies of ∼2400 S_N2 reactions, with various nucleophiles and leaving groups (–H, –F, –Cl, –Br) and functional groups (–H, –NO₂, –CN, –NH₃, –CH₃). Individual substituents contribute additively to molecular σ with a unique regression term, which quantifies the inductive effect. The position dependence of substituents can be modeled by a distance decaying factor for S_N2. Use of the Hammett equation as a base-line model for Δ-machine learning models of the activation energy in chemical space results in substantially improved learning curves reaching low prediction errors for small training sets.

中文翻译：

数据增强哈米特方程：化学空间中的反应势垒

有趣的是，哈米特方程如何通过将取代基的影响与化学过程变量（例如反应机理、溶剂或温度）分开来控制整个化学空间的化学反应性。我们概括了哈米特的原始方法来预测具有多个取代基的非芳香族分子支架的活化势能。我们使用全局回归来优化两个实验数据集（苄基溴与硫醇和铵盐分解反应的速率常数）以及由~2400 S _N 2 反应的计算活化能组成的合成数据集中的哈米特参数ρ和σ ，其中各种亲核试剂和离去基团（–H、–F、–Cl、–Br）和官能团（–H、–NO ₂ 、–CN、–NH ₃ 、–CH ₃ ）。各个取代基通过独特的回归项对分子σ做出附加贡献，该回归项量化了诱导效应。取代基的位置依赖性可以通过 S _N 2 的距离衰减因子进行建模。使用哈米特方程作为化学空间中活化能的 Δ 机器学习模型的基线模型，可以显着改善学习曲线，达到较低水平小训练集的预测误差。

更新日期：2020-10-13

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