首页|Findings from Chinese Academy of Sciences Update Knowledge of Machine Learning (Structure-based Reaction Descriptors for Predicting Rate Constants By Machine Learning: Application To Hydrogen Abstraction From Alkanes By Ch3/h/o …)
Findings from Chinese Academy of Sciences Update Knowledge of Machine Learning (Structure-based Reaction Descriptors for Predicting Rate Constants By Machine Learning: Application To Hydrogen Abstraction From Alkanes By Ch3/h/o …)
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NETL
NSTL
Amer Chemical Soc
Investigators publish new report on Machine Learning. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "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." Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "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 CH3, 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 + CH3, 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 () 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."
WuhanPeople's Republic of ChinaAsiaAcyclic HydrocarbonsAlkanesCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningChinese Academy of Sciences
NETL
NSTL
Amer Chemical Soc
