An improved d-band model of the catalytic activity of magnetic transition metal surfaces
Satadeep Bhattacharjee, Umesh V Waghmare, S. C. Lee

TL;DR
This paper extends the d-band model to better predict catalytic activity on magnetically polarized transition metal surfaces by incorporating spin-dependent interactions, validated through DFT calculations of NH3 adsorption.
Contribution
The paper introduces a generalized d-band model that accounts for spin polarization effects, improving predictions of catalytic activity on magnetic transition metal surfaces.
Findings
The generalized model better predicts NH3 adsorption energies on magnetic TM surfaces.
Spin-dependent interactions influence metal-adsorbate stabilization.
Conventional d-band model is inadequate for high spin polarization surfaces.
Abstract
The d-band center model of Hammer and N{\o}rskov is widely used in understanding and predicting catalytic activity on transition metal (TM) surfaces. Here, we demonstrate that this model is inadequate for capturing the complete catalytic activity of the magnetically polarized TM surfaces and propose its generalization. We validate the generalized model through comparison of adsorption energies of the NH molecule on the surfaces of 3d TMs (V, Cr, Mn, Fe, Co, Ni, Cu and Zn) determined with spin-polarized density functional theory (DFT)-based methods with the predictions of our model. Compared to the conventional d-band model, where the nature of the metal-adsorbate interaction is entirely determined through the energy and the occupation of the d-band center, we emphasize that for the surfaces with high spin polarization, the metal-adsorbate system can be stabilized through a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Electrocatalysts for Energy Conversion
