Surface segregation is a phenomenon common to
all multicomponent materials and one that plays a critical role in
determining their surface properties. Comprehensive studies of
surface segregation versus bulk composition in ternary alloys have
been prohibitive because of the need to study many different
compositions. In this work, high-throughput low-energy He+ ionscattering
spectra and energy-dispersive X-ray spectra were
collected from a CuxAuyPd1−x−y composition spread alloy film
under ultrahigh vacuum conditions. These have been used to
quantify surface segregation across the entire CuxAuyPd1−x−y
composition space (x = 0 → 1 and y = 0 → 1 − x). Surface
compositions at 164 different bulk compositions were measured at 500 and 600 K. At both temperatures, Au shows the greatest
tendency for segregation to the top-most surface while Pd is always depleted from the surface. Higher temperatures enhance the Au
segregation. Segregation at most of the binary alloy bulk compositions matches with observations previously reported in the
literature. However, surface compositions in the CuPd B2 composition region reveal segregation profiles that are nonmonotonic in
bulk alloy composition. These were not observable in prior studies because of their limited resolution of composition space. An
extended Langmuir−MacLean model, which describes ternary alloy segregation, has been used to analyze experimental data from the
ternary alloys and to estimate pair-wise segregation free energies and segregation equilibrium constants. The ability to study surface
segregation across the ternary alloy composition space with high-throughput methods has been validated, and the impact of bulk
alloy phase on surface segregation is demonstrated and discussed.
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This content will become publicly available on July 2, 2025
Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data
Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.
more » « less- Award ID(s):
- 1921946
- PAR ID:
- 10558019
- Publisher / Repository:
- European Chemical Societies Publishing
- Date Published:
- Journal Name:
- ChemPhysChem
- Volume:
- 25
- Issue:
- 13
- ISSN:
- 1439-4235
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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