Machine Learning Study of Methane Activation by Gas-Phase Species

The activation and transformation of methane have long posed significant challenges in scientific research. The quest for highly active species and a profound understanding of the mechanisms of methane activation are pivotal for the rational design of related catalysts. In this study, by assembling...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ying Xu (9172) (author)
مؤلفون آخرون:	Zi-Yu Li (1360101) (author), Qi Yang (108283) (author), Xi-Guan Zhao (17108762) (author), Qian Li (114642) (author), Sheng-Gui He (1317555) (author)
منشور في:	2025
الموضوعات:	Biochemistry Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified training data set specially designed data newly incorporated data maximum natural charge maximum electron occupancy intrinsic electronic properties feature values aiming feature selection complemented experimental rate constants data set encompassing metal atoms (< highly active species full expected range four key features oxidative addition mechanisms methane activation via machine learning study oxidative addition machine learning vde ), validated using study proved splitting strategy scientific research related catalysts rational design q </ profound understanding practical guideline phase species n </ models based methane activation manual intervention generalization ability future research energy gap e </ constructed model careful preparation absolute value
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Machine Learning Study of Methane Activation by Gas-Phase Species

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