Machine Learning Study of Methane Activation by Gas-Phase Species

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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Bibliographic Details
Main Author: Ying Xu (9172) (author)
Other Authors: Zi-Yu Li (1360101) (author), Qi Yang (108283) (author), Xi-Guan Zhao (17108762) (author), Qian Li (114642) (author), Sheng-Gui He (1317555) (author)
Published: 2025
Subjects:
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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