Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C₂N by DFT and machine learning

Abstract

Designing high-performance bifunctional oxygen evolution/reduction reaction (OER/ORR) catalysts is a newly emerged topic with wide applications in metal-air batteries and fuel cells. Herein, we report a group of (27) single-atom...

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

“…[15,16] For example, ML can predict the OER/ORR and hydrogen evolution reaction performance directly from the atomic scale properties of graphene-supported SACs; [17,18] identify active sites for CO 2 reduction on alloyed gold and describe the CO 2 reduction performance of SACs with the help of the atomic-scale properties of the metal. [19,20] Although the ML approach has been adopted in studying SACs and DACs on N-doped carbon materials previously [21][22][23][24] these existing studies often use the atomic properties of the metals (e.g., electronegativity, d-orbital electron count, metal-carbon…”

Understanding the Electronic Interactions, Vertical Excitation Analysis, and the Photovoltaic Properties of 5-(2-ethylhexyl)-1,3-di(furan-2-yl)-4H-thieno[3,4-c]pyrrole-4,6-dione

“…From the lowest unoccupied molecular orbital (LUMO) isosurface, one can infer that LUMO spreads all over the donor and acceptor backbone including the S atom in the acceptor unit, but N. Visuals from the plots of LUMO+1, LUMO+2 and HOMO-1 show that they are also distributed around the donor and acceptor groups, but HOMO-2 is virtually distributed all over the FTPF monomer. Negative energy terms was obtained for both HOMO and LUMO (-5.7509 eV and -2.3274 eV, respectively) using DFT method at CAM-B3LYP/6-31+G(d,p) levels employed; an indication that all calculation converged appropriately at this level [21,52,[60][61][62]. The calculated HOMO-LUMO energy gap is 3.4256 eV, a relatively small energy gap which shows that FTPF polymer is a good candidate for application as opto-electrical component [22,23] in organic electronic devices like OPV cells.…”

Machine learning for design principles for single atom catalysts towards electrochemical reactions

“…13,32,162 ML is considered as a new direction for the rational design of SACs by exploring feature importance analysis for electroreduction reactions to introduce more perceptions on the origin of the activity and stability of SACs. [163][164][165] For example, ML integrated DFT was applied to establish a relationship between various descriptors and hydrogen adsorption free energy (DG H* ) for the HER by altering the size and dimensionality of a nitrogen-doped 2D-carbon substrate for 3d, 4d, and 5d transition metals (TMs) as SACs. 166 The sure independent screening and sparsifying operator (SISSO) as the supervised ML algorithm was applied with 10 input features including the d-state center (3 d ), covalent radius (r cov ), Bader charge (q), number of occupied d states (d occ ), Zunger radius (r d ), number of valence electrons (N e ), ionization energy (IE), electronegativity (EN), and formation energy of single atom sites (E f ).…”