Structural and Electronic Descriptors of Catalytic Activity of Graphene‐Based Materials: First‐Principles Theoretical Analysis

Abstract

Characteristic features of the d-band in electronic structure of transition metals are quite effective as descriptors of their catalytic activity toward oxygen reduction reaction (ORR). With the promise of graphene-based materials to replace precious metal catalysts, descriptors of their chemical activity are much needed. Here, a site-specific electronic descriptor is proposed based on the p (π) orbital occupancy and its contribution to electronic states at the Fermi level. Simple structural descriptors are identified, and a linear predictive model is developed to precisely estimate adsorption free energies of OH (ΔG ) at various sites of doped graphene, and it is demonstrated through prediction of the most optimal site for catalysis of ORR. These structural descriptors, essentially the number of ortho, meta, and para sites of N/B-doped graphene sheet, can be extended to other doped sp hybridized systems, and greatly reduce the computational effort in estimating ΔG and site-specific catalytic activity.

Zigzag carbon as efficient and stable oxygen reduction electrocatalyst for proton exchange membrane fuel cells

“…To solve this problem, a variety of non-precious metal catalysts (NPMCs), including nitrogen-coordinated transition metals (M-N-C, M = Fe, Co, etc.) 1 – 7 , and even metal-free catalysts (mainly heteroatom-doped carbon nanomaterials) have been developed 8 – 12 . The NPMCs present satisfactory activity and stability during half-cell characterization (a three-electrode system) with both alkaline and acidic electrolytes.…”

Machine learning for heterogeneous catalyst design and discovery

“…Consequently, molecule adsorption energies on transition metal surfaces linearly correlate with the d‐band center, which can then be related to catalyst activity through linear scaling relations . Other catalyst descriptors derived by intuition exist such as the “generalized” coordination number or “orbital‐wise” coordination number, which can estimate the chemical reactivity of nanoparticle catalysts by rationally counting the atoms (or their orbital overlap) that influence the electronic structure of each catalyst site. Such descriptors are powerful but have limitations in accuracy and generalizability.…”

Molecular Dynamics and Machine Learning in Catalysts

“…Discovering the simple standard features that influence the properties in a small group of materials as descriptors is a valuable approach in properties prediction, such as catalytic activity and materials finding [141][142][143][144][145][146][147][148][149]. Several descriptors can be used, e.g., interatomic distance, nearest neighbor coordination number (CN), surface strain, the number of facets, and p-band center [150]. However, the accuracy of these simple descriptors is challenging to verify experimentally because catalysts have complex structures that change dynamically during the reaction.…”

“…A series of descriptors derive from different types of catalysts and reactions[150], Copyright 2017, Small.…”