Stability is an important aspect of alloys, and proposed alloys may be unstable due to unfavorable atomic interactions. Segregation of an alloy may occur preferentially at specific exposed surfaces, which could affect the alloy's structure since certain surfaces may become enriched in certain elements. Using density functional theory (DFT), we modeled surface segregation in bimetallic alloys involving all transition metals doped in Pt, Pd, Ir, and Rh. We not only modeled common (111) surfaces of such alloys, but we also modeled (100), (110), and (210) facets of such alloys. Segregation is more preferred for early and late transition metals, with middle transition metals being most stable within the parent metal. We find these general trends in segregation energies for the parent metals: Pt > Rh > Pd > Ir. A comparison of different surfaces suggests no consistent trends across the different parent hosts, but segregation energies can vary up to 2 eV depending on the exposed surface. We also developed a statistical model to predict surface-dependent segregation energies. Our model is able to distinguish segregation at different surfaces (as opposed to generic segregation common in previous models), and agrees well with the DFT data. The present study provides valuable information about surface-dependent segregation and helps explain why certain alloy structures occur ( e.g. core–shell).
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Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions
A catalytic surface should be stable under reaction conditions to be effective. However, it takes significant effort to screen many surfaces for their stability, as this requires intensive quantum chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient machine learning (ML) approach to accurately and efficiently predict the surface energies of metal alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was used as a vector representation for fitting models for predicting surface formation energies. The ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or gas-phase molecules. Models using the ECFP as input can be quite general, as we created models with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression ML model which is able to predict the surface energies of alloys of diverse metal combinations with a mean absolute error of 0.017 eV atom−1. Combining this model with an existing model for predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys (SAAs)with and without CO adsorbed on these surfaces. As a simple test of the approach, we identify specific cases where CO does not induce segregation in these SAAs.
more » « less- Award ID(s):
- 1946231
- PAR ID:
- 10380564
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Physics: Energy
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2515-7655
- Page Range / eLocation ID:
- Article No. 015002
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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