摘要
本文探讨了几种梯度近似(GGA)密度泛函及元梯度近似(metaGGA)密度泛函在描述甲烷在重构的Pt(110)-(2×1)上的解离化学吸附作用的适用性.金属的体相和表面结构、甲烷的吸附能量和解离能垒等被用来评估泛函的可靠性.另外,在从头算分子动力学计算中,采用范德瓦尔斯矫正的GGA函数(optPBE-vdW)和范德瓦尔斯矫正的meta-GGA函数(MS-PBEl-rVV10)计算粘附概率.计算结果表明,使用这两种泛函能更好地与现有的实验结果吻合,从而为发展甲烷在Pt(110)-(2×1)表面解离的可靠机器学习势能面打下重要基础.
In this work,we explore the suitability of several density functionals with the generalized gradient approximation(GGA)and beyond for describing the dissociative chemisorption of methane on the reconstructed Pt(110)-(2×1)surface.The bulk and surface structures of the metal,methane adsorption energy,and dissociation barrier are used to assess the functionals.A van der Waals corrected GGA functional(optPBE-vdW)and a metaGGA functional with van der Waals correction(MS PBEl-rVV10)are selected for ab initio molecular dynamics calculations of the sticking probability.Our results suggest that the use of these two functionals may lead to a better agreement with existing experimental results,thus serving as a good starting point for future development of reliable machine-learned potential energy surfaces for the dissociation of methane on the Pt(110)-(2×1)surface.
作者
卫奋飞
Egidius W.F.Smeets
Johannes Vossc
Geert-Jan Kroes
林森
郭华
Fenfei Wei;Egidius W.F.Smeets;Johannes Voss;Geert-Jan Kroes;Sen Lin;Hua Guo(State Key Laboratory of Photocatalysis on Energy and Environment,College of Chemistry,Fuzhou University,Fuzhou 350002,China;Leiden University,Leiden Institute of Chemistry,Einsteinweg 55,2333 CC,Leiden,The Netherlands;SUNCAT Center for Interface Science and Catalysis,SLAC National Accelerator Laboratory,2575 Sand Hill Road,Menlo Park CA 94025,USA;Department of Chemistry and Chemical Biology,University of New Mexico,Albuquerque,New Mexico 87131,USA)
基金
financial support from the National Natural Science Foundation of China(No.21973013 and No.21673040)
the National Natural Science Foundation of Fujian Province,China(No.2020J02025)
the“Chuying Program”for the Top Young Talents of Fujian Province
supported financially through a NWO/CW TOP grant(No.715.017.001)
by a grant of supercomputer time from NWO Exacte en Natuurwetenschappen(NWO-ENW,No.2019.015)
the National Science Foundation(No.CHE1951328)。
