Machine learning rate constants of hydrogen abstraction reactions between ester and H atom

Yu, Jianhua; Ruan, Shanshan; Song, Hongwei; Zhang, Lidong; Yang, Ming

doi:10.1016/j.combustflame.2023.112901

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…Meanwhile, some attention were also paid to directly predict rate constants by machine learning. − Houston et al utilized Gaussian process regression to fit thermal rate constants for a collection of 13 gas-phase bimolecular reactions across a large temperature range. ,, The reactions were characterized by three parameters: Eckart tunneling, skew angle, and reactant symmetric stretch vibrational frequency. The predicted rate constants averaged over the 39 test reactions exhibited an accuracy within 80% of the precise quantum rate constants.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Zhang,

Xia,

Song

et al. 2024

J. Phys. Chem. A

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Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097−5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C−C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH 3 and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions.

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Section: Introductionmentioning

confidence: 99%

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Zhang,

Xia,

Song

et al. 2024

J. Phys. Chem. A

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“…It is important to note also that both the XGBoost and FNN models are widely used to build the Quantitative structure property relationship (QSPR) of molecular structure and properties, such as toxicity, 28,29 density/heat capacity, 30 surface tension/viscosity, 31 and reaction constants. 32 To further enhance the novelty, we construct an interpretable ML model using varied molecular descriptors, such as a molecular structure descriptor (MSD), GC, and a hybrid GC−MSD. These descriptors are integrated into both XGBoost and FNN models, allowing for a comparative analysis of their performance.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, we recognize the robustness and high performance of both RF and XGBoost as ML algorithms; each presents its own unique strengths and weaknesses. It is important to note also that both the XGBoost and FNN models are widely used to build the Quantitative structure property relationship (QSPR) of molecular structure and properties, such as toxicity, , density/heat capacity, surface tension/viscosity, and reaction constants …”

Section: Introductionmentioning

confidence: 99%

Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO₂ Solubility in Ionic Liquids

Yang,

Sun,

et al. 2024

Ind. Eng. Chem. Res.

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This study investigates the efficacy of feedforward neural network and XGBoost models in screening ionic liquid solvents for CO 2 capture. Both models were integrated with either group contribution (GC), molecular structure descriptors (MSD), or hybrid GC−MSD, to enable performance comparisons. It was demonstrated that the XGBoost models performed better over feedforward neural network models, irrespective of descriptor types. Notably, the XGBoost−GC−MSD model outperformed the artificial neural network with group contribution (ANN−GC) and structure encoding multilayer perceptron (SE-MLP) models from previous work, demonstrating an R 2 value of 0.98963, MAE of 0.01480, and RMSE of 0.02369. Even the least-performing XGBoost−GC model surpassed earlier ANN−GC and SE-MLP models, showcasing an R 2 value of 0.98891. Lastly, the Shapley additive explanation analysis identified the top five influential input features, including pressure, temperature, Chi2v, Chi0n, and BertzCT. These findings provide valuable insights into the molecular determinants affecting CO 2 solubility in ionic liquids.

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“…The average deviation of the XGB-FNN model on the prediction set was about 40%. The XGB-FNN model was also applied to predict rate constants of the H + methyl decanoate reactions over a temperature range of 300–2000 K, giving a deviation of 16.5%–84.9% from the calculated values . All of these studies have shown that machine learning models are capable of accurately predicting rate constants of combustion reactions with well-designed molecular descriptors.…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Song

Yang

2023

J. Chem. Inf. Model.

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Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure−property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH 3 , H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH 3 , H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy (Fuel 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions.

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Machine learning rate constants of hydrogen abstraction reactions between ester and H atom

Cited by 4 publications

References 60 publications

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO₂ Solubility in Ionic Liquids

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

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Machine learning rate constants of hydrogen abstraction reactions between ester and H atom

Cited by 4 publications

References 60 publications

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO2 Solubility in Ionic Liquids

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Contact Info

Product

Resources

About

Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO₂ Solubility in Ionic Liquids

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals