数据驱动方法揭示单原子掺杂金红石氧化物在甲烷活化过程中的活性位点
Uncovering the active sites of single atom-doped rutile oxides during methane activation by data-driven approach
卫奋飞 1葛冰青 1董佩佩 2万强 3胡茜茜 4林森1
作者信息
- 1. College of Chemistry,Fuzhou University,Fuzhou 350002,China
- 2. Institute(College)of Integrative Medicine,Dalian Medical University,Dalian 116044,China
- 3. College of Chemistry,Fuzhou University,Fuzhou 350002,China;Department of Chemistry and Materials Science,Zhejiang Normal University,Jinhua 321004,China
- 4. Kuang Yarning Honors School,Nanjing University,Nanjing 210023,China
- 折叠
摘要
金属氧化物通常用于甲烷的活化和转化,但总是受到过度氧化的影响.引入单原子是解决这一难题的一种有吸引力的方法,但掺杂单原子的实际作用仍存在争议.因此,开发性能描述符来预测掺杂表面上不同位点之间的反应性至关重要.在这项工作中,采用单原子(Dguest,D=Ti,V,Cr,Mn,Nb,Mo,Ru,Rh,Ta,Re,Os,Ir,Pt,Si,Ge和Sn)掺杂的金红石型金属氧化物(MO2,M=Ru,Rh,Ir,Pt,Mo)作为模型催化剂,利用密度泛函理论计算和数据驱动方法研究了甲烷在不同表面位点上的活化情况,并阐明了此类掺杂表面的实际活性位点.利用机器学习方法,从特征组合描述符的大空间中获得了多维描述符,从而可以统一预测Dguest和Mhost上活化CH4的能垒,而不受过渡态计算的影响.最后,MO2上客体位点对选择性氧化的调节作用得到了证实.我们的工作证明了掺杂剂在催化过程中的复杂作用,所开发的描述符有助于确定活化能,为基于金红石氧化物的催化剂提供潜在的选择性氧化位点.
Abstract
Metal oxides are commonly used in methane activation and conversion,but usually suffer from over-oxi-dation.The introduction of single atoms is an attractive way to overcome this challenge,but the actual role of doped single atoms remains controversial.Here,we adopted single atoms(Dguest,D=Ti,V,Cr,Mn,Nb,Mo,Ru,Rh,Ta,Re,Os,Ir,Pt,Si,Ge,and Sn)-doped rutile metal oxides(MO2,M=Ru,Rh,Ir,Pt,Mo)as model catalysts and investigate methane acti-vation at various surface sites and elucidate the actual active sites in such doped surfaces by using the density functional theory calculations and data-driven approach.Overall,we obtain derived multidimensional descriptors from a large space of feature-combined descriptors by using the machine learning approach,which allows uniform prediction of the energy barrier of CH4 activation on both Dguest and Mhost,in-dependent of the transition state calculation.The regulation of selective oxidation by guest sites on MO2 was confirmed.This work sheds light on the complicated role of dopants in catalysis,and the developed descriptors help determine the activation energy to provide potential selective oxidation sites of rutile oxide-based catalysts.
关键词
methane activation/descriptors/machine learningKey words
methane activation/descriptors/machine learning引用本文复制引用
基金项目
国家自然科学基金(22373017)
国家自然科学基金(22303085)
国家自然科学基金(21973013)
国家重点研发计划(2022YFA1503102)
National Natural Science Foundation of Fujian Province,China(2020J02025)
浙江省自然科学基金(LQ24B030014)
the"Chuying Program"for the Top Young Talents of Fujian Province()
Computations were performed at Hefei Advanced Computing Centre and Supercomputing Centre of Fujian()
出版年
2024
NETL
NSTL
万方数据