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arXiv 2004.14946physics.chem-ph
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数据增强的Hammett方程化学空间反应预测

Data Enhanced Reaction Predictions in Chemical Space With Hammett's Equation

Marco Bragato, Guido Falk von Rudorff, O. Anatole von Lilienfeld

摘要:

通过将取代基的影响与化学过程变量(如反应机理、溶剂或温度)分离,Hammett方程能够控制整个化学空间中的化学反应性。我们使用全局回归来优化Hammett参数$ρ$和$σ$在两个数据集,苄基溴与硫醇反应和铵盐分解的实验速率常数,以及由$\sim$ 1400 $S_N2$反应的计算活化能组成的合成数据集,具有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 $_3$)。原来的方法进行推广,以预测在非芳香族分子支架与多个取代基的活化能。单个取代基对分子$σ$的贡献是相加的,具有独特的回归项,该回归项量化了诱导效应。此外,取代基的位置依赖性可以由$S_N2$的距离衰减因子代替。使用Hammett方程作为$Δ$的基线模型-化学空间中活化能的机器学习模型导致小训练集大小的学习曲线大幅改善。

英文摘要:

By separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent, or temperature, the Hammett equation enables control of chemical reactivity throughout chemical space. We used global regression to optimize Hammett parameters $ρ$ and $σ$ in two datasets, experimental rate constants for benzylbromides reacting with thiols and the decomposition of ammonium salts, and a synthetic dataset consisting of computational activation energies of $\sim$ 1400 $S_N2$ reactions, with various nucleophiles and leaving groups (-H, -F, -Cl, -Br) and functional groups (-H, -NO$_2$, -CN, -NH$_3$, -CH$_3$). The original approach is generalized to predict potential energies of activation in non aromatic molecular scaffolds with multiple substituents. Individual substituents contribute additively to molecular $σ$ with a unique regression term, which quantifies the inductive effect. Moreover, the position dependence of the substituent can be replaced 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 for small training set sizes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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