Unraveling the Factors Behind the Efficiency of Hydrogen Evolution in Endohedrally Doped C60 Structures via Ab Initio Calculations and Insights from Machine Learning Models. Issue 3 (25th January 2019)

• Record Type: Journal Article
• Authors: Tahini, Hassan A.; Tan, Xin; Smith, Sean C.
• Other Names: Smith Sean C. guestEditor.
• Is Part Of: Advanced theory and simulations. Volume 2:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-25
• Subjects: DFT calculations -- hydrogen evolution reaction -- machine learning -- metallofullerenes; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201800202
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Understanding the origins of catalytic activity (or inactivity) in nanostructures allows for the rational design of cheap and durable catalysts. Here, consistent and comprehensive ab initio screening of endohedrally doped fullerenes as potential catalysts for hydrogen evolution reactions is performed. By examining variations in the electronic structure of the carbon atoms in the presence of the dopant, and by relying on machine learning algorithms, the origin of enhanced activity in fullerenes can be underpinned. The effect is attributed to the formation of free radicals by weakening the C─C double bonds. A number of electronic descriptors are discussed which can be fed into machine learning models to efficiently and reliably predict catalytic activities. This allows for a generalization of trends and a predictive ability that could be applied to other fullerene structures. Abstract : Metallofullerenes exhibits a wide range of electrocatalytic response toward hydrogen evolution . The origin of this behavior is due to the interplay between a number of electronic features. Unraveling the origins of this behavior using Density Functional Theory and machine learning models is attempted.