Computational Screening for Developing Optimal Intermetallic

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C: Surfaces, Interfaces, Porous Materials, and Catalysis

Computational Screening for Developing Optimal Intermetallic Transition Metal Pt-based ORR Catalysts at the Predictive Volcano Peak Rees B Rankin, and Conor Waldt J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b11494 • Publication Date (Web): 03 May 2019 Downloaded from http://pubs.acs.org on May 3, 2019

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The Journal of Physical Chemistry

Computational Screening for Developing Optimal Intermetallic Transition Metal Pt-Based ORR Catalysts at the Predictive Volcano Peak Rees B Rankin∗ and Conor Waldt Department of Chemical Engineering, WH 313, Villanova University, Villanova University, 800 E Lancaster Ave, Villanova, 19085, PA, USA E-mail: [email protected]

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Abstract Practical development of cost-effective and environmentally-sustainable Oxygen Reduction Reaction (ORR) catalysts has not yet been fully realized despite years of effort. Specifically, state-of-the-art ORR catalysts typically require high Pt-loading while still suffering significant overpotential losses and only providing moderate current density. In this work, we present results from new DFT calculations screening a wide range of Pt3(MN)1 ternary catalysts to see the range of values for oxygen adsorption energy (the known ORR Volcano Plot descriptor) such systems can achieve. Our results identify many promising materials based purely on performance via this descriptor; unfortunately many of these promising candidates still require very high loading of rare precious transition metals in the guest (MN) composition role. We have further used our results to generate a predictive fitted model for the ORR descriptor value itself based on the normalized valence and mass of a given FCC(111) Pt3(MN)1 surface compared to Pt(111). This predictive model is presented to help illustrate and guide the selection of further candidate systems in the continued search for the rational design of costeffective, high-performance Pt-based ORR catalysts.

Introduction The need for abundant energy and commodity chemicals generated or synthesized from efficient chemical routes using low-cost, sustainable catalysts increases daily. Causes for this include rising concerns associated with increasing global population, fossil fuel emissions, and standard-of-living requirements. An example of this is the development of the PEMFC as an alternative to the traditional internal combustion engine and for other related applications.1–5 Development and deployment of the PEMFC and other Oxygen Reduction Reaction (ORR) technologies on a global, sustainable scale require advances in catalyst development. Traditional and still current best-known catalysts are compositionally dependent on high loadings of the rare noble metal platinum (Pt). For these reasons, there is a great need to develop

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ORR catalysts which simultaneously decrease Pt loading while increasing overall efficiency and decreasing overpotential losses that hinder performance. Other concerns include cycle durability and selectivity concerns associated with reactions such as peroxide synthesis. Specific performance of catalysts can be tuned or modified – in principle – through careful design and optimization of their composition and synthesis route, which in turn help direct their shape/size, crystallinity, porosity, and stability.2,6–10 Much effort in recent years has been directed to optimize the composition of other transition metal guest/dopants in Pt-based intermetallic and alloy nanocatalysts and to optimize the synthesis routes to help direct the specific nanostructure/geometries that such Pt-based nanocatalysts may adopt in realistic operating environments and conditions. For searches via compositional dependence, recent work has focused on the incorporation of binary, ternary and even quaternary alloys in the intermetallic system. Examples of this effort include structures made from Pt-alloy, intermetallic, near surface alloy, core-shell, surface-skin type structures, and even extended to including 5th row and f-block transition metals.5,6,11–37 The goal of work in this manuscript is to help predictively identify higher performance, lower cost ORR catalysts; we achieve this through the study of bandwidth sensitivity of monoatomic oxygen adsorption energy, ∆EadsO* , (the established descriptor in ORR community for ORR catalyst activity Volcano Plots) via widespread Density Functional Theory (DFT) computations across a suite of ternary Pt-based 75%-25% intermetallic alloys with both intermetallic and Pt-skin and Pt subsurface layer structure(s). This work expands on previous efforts to study a more limited subset of data.6,11 It was previously discovered in this work that – interestingly– 25% loadings of strongly oxophilic transitional metals such as Ni and Co into the Pt (sub)surface decreased the absolute magnitude of the ∆EadsO*. Unfortunately, this effect weakened the ∆EadsO* too much; such catalyst materials fell past the ‘peak’ of the ORR volcano plot, and into the too-weakly binding leg of the volcano (too oxophobic). In this work, we have identified numerous possible catalyst compositions which more closely fall near the ORR volcano peak (some well within 0.03 eV, the general relative 3



accuracy of a DFT calculation as described in this work) and can, in theory, offer better performance than the previously identified ternary species. We then further develop a predictive fit-based model on our results which uses the catalyst’s atomic number, and catalyst’s valence– both being normalized to ideal Pt(111) surface – to further accelerate predictive pre-screening of future candidate materials.

Methods DFT Calculations Plane-Wave DFT calculations were performed to study the energetics of mono-atomic oxygen adsorbate atoms on the surface of the representative Pt-based alloy or skin-alloy surfaces described in this work. Plane-wave DFT calculations are well reported in the literature as accurately reproducing both qualitative and quantitative trends in the ORR activity and selectivity across single crystal surfaces of transition metal catalysts relevant to the ORR.6,13,38,39 Details of the implementation of the calculations are described in the paragraphs that follow. Calculations were performed using the Vienna Ab Initio Simulation Package (VASP), v5.4, as implemented in MedeA v2.21 from the Materials Design group.40–45 To be consistent with the previously published work we have chosen to use the PBE-GGA functional once again.6,46 This functional is well-established to both qualitatively and quantitatively reproduce the experimentally observed trends for ORR activity and selectivity for the type of materials reported in this work. Plane-waves were expanded to a cutoff energy of 520 eV, and spin-polarization was allowed for all calculations reported in this work. Relaxation of the electronic wavefunctions was performed until convergence of at least 10−6 eV was obtained. Smearing of the electronic wavefunctions near the Fermi Energy was treated with the Methfessel-Paxton 2nd order algorithm with a value of 0.2 eV.47 K-point sampling was performed at the 2×2×1 γ-centered grid for initial relaxation of the atomic positions (to 0.05 eV / Å); the grid was refined to a 4


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4×4×1 spacing for further relaxation and more accurate energetic determination. Geometries were relaxed for the adsorbate atoms and the top 2 slab layers of the surface models (described in the next paragraph) to net forces less than 0.03 eV/Å. Dipole corrections parallel to the surface normal were applied to reduce spurious interactions between slabs. All methods and parameters mentioned above match or exceed those typically presented in the community as well as those used in the previous work this manuscript expands on.6

Slab Model For the specific DFT calculations described in this manuscript, 4-layer thick calculation slabs were constructed with the same methods previously described in the literature for a previous study.6 These slabs were comprised by 4-atomic-layer thick slab with 16 atoms, 4 atoms per layer. Such slabs correspond to a p(2×2) surface cell of the FCC(111) surface. The slabs models were placed in the vertical center of the calculation supercell which had a total ’height’ of 28 Å; this affords approximately 20 Å of vacuum spacing; a distance at which the total Bader valence of the ’vacuum’ region is less than 0.01 net electron. For all surface slab models, the bulk lattice constant obtained for Pt was used. The slab contained 16 unique atomic positions in the 4 slab layers; 12 atoms were fixed as Pt, with up to 4 atoms being substituted with other transition metal(s). For the skin alloy (or near-surface alloy, {NSA}) models, this corresponds to a platinum host composition of 75%, with guest atom composition of a cumulative 25%.6 For the alloy overlayer on the Pt-subsurface layer skin models, this also corresponds to a 75% Pt host composition, with a 25% total guest atom composition. The surface coverage of mono-atomic oxygen adsorbates was therefore

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ML.

Schematic diagrams of the local geometry and structure of the models are provided in Figure 1a and 1b below. Adsorption sites for mono-atomic oxygen adsorbate atoms are depicted in Figure 2 below. For all models and systems calculated, it is assumed that the surface retains the ideal lattice constant of bulk Platinum identified with this choice of functional, cutoff energy, and other relevant calculation parameters described in the preceding paragraph. 5



The sites chosen for the adsorption of mono-atomic oxygen are all 3-fold FCC sites of nondegenerate local geometry/composition either though NN,NNN, or NNNN differences (NN= Nearest Neighbor, etc). Such sites (3 fold FCC) have previously been identified and used as the benchmark for the development of the precious metal ORR Volcano Plot and screening of ternary Pt-based ORR alloy catalysts.6,38

Results and Discussion Intermetallic/ Pt-Skin Surface Catalysts This work first focused on quantifying the adsorption energy, ∆EadsO*, relative to that calculated on Pt(111) for mono-atomic oxygen atoms at low surface coverage, similar to that already published.6 Specifically, for each of the catalyst surfaces studied, we characterized this relative shift in the ∆EadsO* as it varies at a given surface composition based on the local site composition; the details of the site models are previously described in Methods, and are used consistently throughout this section and subsection(s) of the manuscript. In Figure 3 we show heatmaps for the ∆EadsO* relative to that on Pt(111) for the Ptskin NSA surfaces studied in this work. The horizontal axis divides catalysts by composition, while the vertical axis divides each catalyst by site type. Preference for specific site type will be discussed in a later paragraph. The critical feature to examine in these heatmaps and the reason for the chosen colormap and scale follows from the original ORR DFT-based Volcano plots.6 In these works, it is seen that the predicted most-optimal (activity) ORR catalyst has a ∆EadsO* which is 0.18 to 0.21 eV weaker than that on the ideal Pt(111) surface. Accordingly, in Figure 3 (and Figure 5) our choice of colormap and scale makes the targeted ∆EadsO* appear white for any composition/site where it occurs. In our sign convention, red indicates binding oxygen more strongly than Pt(111), and blue more weakly than Pt(111). Several qualitative features stand out from inspection of the heat maps shown in Figure

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Figure 1: Representative structure graphics for the systems studied in this manuscript. a e: for the Pt-skin Pt3(MN)1 intermetallic catalyst (111) surface. f-g for the intermetallic overlayer Pt subsurface layer catalyst (111) surface. a) top-down view of calculation cell b) side-on view of calculation cell c) exploded side-on view of multiple repeats of calculation cell into supercell d) actual side-on view of multiple repeats of calculation cell into supercell e) actual top-down view of multiple repeats of calculation cell into supercell f) exploded sideon view of multiple repeats of intermetallic overlayer Pt subsurface layer catalyst supercell g) actual top-down view of multiple repeats of intermetallic overlayer Pt subsurface layer catalyst supercell. Pt atoms in blue, M in pink, N in grey 7 ACS Paragon Plus Environment


Figure 2: a) Top-down view of non-degenerate adsorption 3-fold FCC sites on the Pt-skin Pt3(MN)1 intermetallic catalyst surfaces studied in this work. b) Top-down view of nondegenerate adsorption 3-fold FCC sites on the Pt subsurface layer Pt3(MN)1 intermetallic catalyst. Pt atoms in blue, M in pink, N in grey. Calculation cell shown in overlaid white rhomboid lines. Same slab model scheme as described in prior work.6,45 8


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Figure 3: Heat Maps for ∆EadsO*, the energy of adsorption of mono-atomic oxygen on the Pt-skin near surface alloy (NSA) of Pt3(MN)1 catalyst surfaces as a function of composition and site type. Subpanels are arranged in alphabetical order from left-right and then top-bottom. All subpanels use the same color scheme and scale for the heatmaps. Units of adsorption energy in eV. Reference state is clean slab one half of an O2 diatomic molecule in the gas phase. 3. First, it can be seen that although there are some exceptions, as a general rule of thumb, the adsorption energy is strongest at sites 2 and 4, and generally between the two, stronger at site 4 for most of the compositions where there is an appreciable difference. These two site types (2 &4 ) qualitatively share having sub-surface 2nd layer M and N atoms in adjacent HCP sites but have differing elemental composition immediately below them in the 3rd layer. This latter fact implies and confirms that the alloying creates a relative weakening of the sites (1&3) which have an immediate local environment in the top layer that is akin to a pure

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Pt(111) surface FCC 3-fold site. Second, of the intermetallic Pt-skin Near Surface Alloy compositions studied in this work, a majority of the systems at their strongest adsorption site are not just weaker binding than Pt(111), but also the extent of the weaker binding is sufficient to push many of these materials past the "ideal" shift of 0.18 to 0.21 eV. In other words, in the context of the ORR DFT Volcano plot of 2 legs, many of these materials should fall onto the right leg of the volcano.6 This is in fact exactly what can be seen to happen, as shown in Figure 4.

Figure 4: Volcano plots for the Pt-skin near surface alloy (NSA) of Pt3(MN)1 catalyst surfaces as a function of composition and site type. Units of adsorption energy in eV. Reference state is clean slab and 1/2 of O2 diatomic molecule in the gas phase. Activity scaled to that of Pt(111) as described in prior work.6 Expanded view for larger range of x-axis is given as Fig SI.1 Figure 4 shows the original ORR DFT Volcano Plot ’legs’ within the region of interest to the less oxophilic region of platinum. The sign and activity conventions used in this plot are the same as the work it is adapted from.6We would like to remind the reader that hitting 10


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the target goal of the "volcano peak" would improve the overpotential for electro-chemical ORR by approximately .1 V compared to Pt(111)

{corresponding to an efficiency increase from

approximately 67% to 75%} and the specific activity by approximately 40x. As can be seen in Figure 4 our work in this manuscript has identified many promising candidate compositions which possess lowest energy adsorption sites that move much closer to the predicted Volcano Peak than those previously identified. Several of the materials we have identified in this work fall at or on the volcano peak as can be seen in the zoomed-in region of the plot as shown in Figure 4. Specific compositions of MN in Pt-skin Pt3(MN)1 which fall the closest to this predicted volcano maximum include: on the left leg — Os2Ir2, Ru2Cu2, Os2Ru2, Co2Pd2, Ru2Rh2, Ru2Pd2, Ir2Pd2,

and on the right leg — Ru2Ir2, Os2Pd2, Ru4, Rh2Ir2, Rh2Ni2, Ni4, and then the base-metal ternary intermetallics previously reported.6 Many of these combinations of Os, Pd, Cu, and Rh compositions are showing performance similar to or better than those recently reported for bimetallic or ternary Pt-based systems.5,12,14,48–67 We note that due to limitations of the computational model, and calculation accuracy and cutoff criteria, values within ~0.03 eV for the descriptor ∆EadsO* should be treated as identical.

Alloy/Intermetallic-skin Pt-subsurface layer Catalysts The results presented in Results and Discussion: Intermetallic/ Pt-Skin Surface Catalysts , specifically Figures 3 and 4 highlight the results of our study of the Pt-skin near-surface intermetallic alloys of Pt3(MN)1 as depicted in Figure 1 (a-e). As can be seen in the results and discussion already given, we have identified many catalyst compositions and the site types which move the adsorption energy of mono-atomic oxygen, ∆EadsO*, very close to, if not precisely to, the existing ORR

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DFT Volcano peak. However, as was detailed above, many of these promising active catalysts unfortunately still use very expensive/rare transition metal elements to comprise the MN in the Pt3(MN)1 composition space. Accordingly, we also investigated the shift in ∆EadsO* relative to Pt(111) for all the systems previously described in Results and Discussion: Intermetallic/ Pt-Skin Surface Catalysts, but now with an underlayer of Pt subsurface atoms in the 2nd layer from the top of the calculation slab model. We have chosen to call these model systems intermetallic skin Pt-subsurface catalysts for the remainder of the discussion presented in this section of the manuscript. The schematic depiction of the structure of these systems is given in Figure 1 (f-g).

Figure 5: Heat Maps for ∆EadsO*, the energy of adsorption of mono-atomic oxygen on the intermetallic-skin Pt-subsurface (NSA) of Pt3(MN)1 catalyst surfaces as a function of composition and site type. Subpanels are arranged in alphabetical order from left-right and then top-bottom. All subpanels use the same color scheme and scale for the heatmaps. Units of adsorption energy in eV. Reference state is clean slab and one half of an O2 diatomic molecule in the gas phase. 12


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In Figures 5, and 6 we present analogous heat maps, and volcano plots to those shown in Figure 3 and 4, respectively with similar level of zoom on the x-axis of Figure 6. All sign convention, physical meaning, and other factors should be interpreted in the same manner as previously described. It becomes immediately apparent upon viewing Figures 5, and 6 that the qualitative physical trends observed in the preceding sub-section mostly do not hold for the intermetallic skin Pt-subsurface catalysts discussed in this section compared to the simple Ptskin NSA intermetallic alloy catalysts discussed in Results and Discussion: Intermetallic/ Pt-Skin Surface Catalysts. For example, as we can see in Figure 5 there are fundamental differences in the preference for local environment site type at a given composition/system; this is because the adsorbate O* atom is directly bonding to the alloy element(s) rather than interacting with it/them only through the 2nd or 3rd layer. Similarly, in Figure 6, we see that only approximately 5 candidate intermetallic overlayer Pt-subsurface slab catalysts lie close to the Volcano Peak from both legs combined. The results of these analyses are not entirely surprising; it has been shown in previous work that many examples of these types of Intermetallic skin Pt-subsurface catalysts can either be unstable or can be stabilized much more strongly by adsorbing species such as mono-atomic oxygen upon them. Accordingly, the nearly universal shift in adsorption energies of mono-atomic oxygen ∆EadsO* to values closer to Pt(111) is not entirely unreasonable. We would like to note that these specific subsurface slab results could help explain what is observed in the prior work where the experimentally measured catalyst activity for Pt3(MN)1 systems did not map precisely 1:1 in terms of the position on the DFT Volcano; our possible explanation being that the specific surface composition of Pt3(MN)1 nanoparticles (NP) actually synthesized is certain to have at least small regions of compositional and structural variance. Even if the surface of the NP is mostly Pt-skin – it could have small amounts of local surface dominated by regions of sites as described in this subsection of the manuscript. A fully detailed microkinetic model based on a Wulff equilibrium structure analysis is unfortunately beyond the scope of the intended work in this manuscript but could be the basis of a future study and would help address this possibility.

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Figure 6: Volcano plots for the intermetallic skin Pt-subsurface near surface alloy (NSA) of Pt3(MN)1 catalyst surfaces as a function of composition and site type. Units of adsorption energy in eV. Reference state is clean slab and 1/2 of O2 diatomic molecule in the gas phase. Activity scaled to that of Pt(111) as described in prior work.6 Expanded view is given in Figure SI.2

Combined Analysis and Comparison of Intermetallic/Near Surface Alloys Catalysts and Pt-skin Intermetallic Catalysts In Figure SI.2 we present the data from Figure 4 and Figure 6 shown together on the same plot for side-by-side comparison. As can be seen in this figure, the vast majority of the systems studied which fall closest to the DFT Volcano maximum are the in the Pt-skin Pt3(MN)1 structure. In total, this work has identified approximately 40 candidate materials with lowest energy adsorption site values of ∆EadsO* which should produce catalysts that operate above the Pt(111) normalized current density as predicted by the legs of the Volcano

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Plot. The results of this analysis are in qualitative and sometimes quantitative agreement with many recent papers showing high activity for ternary Pt-based ternary intermetallics using a variety of guest/dopant elements; specific discussion on matching systems is expanded in Results and Discussion: Comparison to Recent Experimental Studies that follows. Recent works have shown that the difference in solvent induced stabilization of OH* intermediates on the various alloy surfaces can be on the order of 0.05 to 0.10 eV. Accordingly, we would estimate that with the relative DFT "error bar" of 0.03 eV previously discussed for differences in adsorption energies in these systems, combined with the 0.05 to 0.10 eV just mentioned, that it would be possible that the uncertainty on the activity for the descriptor positions on the Volcano presented in Figure 4, Figure 6, and Figure SI.2 should likely be taken as ~ +/0.05 to 0.15 eV. Nonetheless, even with the assumption of the worst case of such an error bar, many of the identified candidate materials still are predicted to be improvements over known and verified ternary Pt intermetallic catalysts which have an overpotential typically exceeding 0.4 V.

Comparison to Recent Experimental Studies In the previous sub-sections, we identified the following set of materials as lying closest to the theoretical Volcano maximum peak position(s). For the Pt-skin NSA system, moving from the most to the least oxophilic, we observed Os2Ir2, Ru2Cu2, Os2Ru2, Co2Pd2, Ru2Rh2, Ru2Pd2, Ir2Pd2, the Volcano Peak, and then Ru2Ir2, Os2Pd2, Ru4, Rh2Ir2, Rh2Ni2, and Ni4 as the relevant (MN)1 species in the Pt12(MN)1 catalyst models. Moving from the left edge of the data shown in Figure 4 towards the Volcano Peak, overpotential losses improve as approximately 0.01 V for every 0.02 eV shift in oxophobic direction to the peak and similarly decrease as approximately 0.01 V for every 0.02 eV shift further yet in the oxophobic direction. Using the typical assumption that the overpotential losses from the thermodynamics of OH* formation and removal dominate the small kinetic barriers associated with fast watermediated proton shuttling, this therefore implies that the associated rates 15 ACS Paragon Plus Environment


(current/activity) increase (and then decrease) approximately 50% in the corresponding 0.02 eV shifts in the value of the descriptor ∆EadsO∗ shown in these figures at room temperature. The same numbers would apply to the species and data shown in Figure 6, the volcano plot for the Alloy-skin Pt-subsurface NSA systems; in this figure we see the progression of predicted activity go as Ni2Pd2, Ir2Pd2, Cu2N2, Rh2Pd2, the Volcano Peak and then Cu4 when moving from oxophilic to oxophobic across the ∆EadsO∗ plot range shown. With these numbers and orderings in mind, we can begin comparison to recently published experimental (or theoretical) values for ORR activity on novel ternary intermetallic Pt-based catalysts of similar compositions; we can do so in both a qualitative sense (trends in overpotential and activity ordering) and a qualitative sense (direct improvement in overpotential to Pt(111), where possible and applicable. In doing so, the following observations can be made. At an overpotential of 0.33 V (Operation @ 0.9 V), the ternary alloys have decreasing performance: NiMo, Ni, NiRh, Fe.11,14,25,27,28 Our results as discussed above have the same ordering for Ni and NiRh, with both falling on the too weakly binding side of the Volcano. We note that the predictive Volcano Plot maximum in Figure 4/6 would correspond to an overpotential of 0.31 V and a maximum operating voltage of 0.92 V. Further, we can compare to recent work which has examined Pt-based CuNi catalysts, CuCo catalysts, RuN catalysts, and IrN catalysts which -as ensembles- show improvements in the overpotential of 0.02 to 0.09 V.23,24,28–34,36,37,55,58,62,63,65,67–70 Direct comparisons of exact mass activity is complicated across all materials given the array of synthesis routes and exact NP morphologies and associated ECSA values. To date, we are not aware that every system predicted by our analysis in this work has a yet described experimental analog, particularly in the case of the Osmium-based catalysts. Similarly, for the intermetallic-skin Pt-subsurface catalysts, direct comparison of all the “best” candidates in this work to direct experimentally published results are not feasible in all cases. However, we note that for the materials: PtNiCu it has been shown dissolution or segregation of the catalyst Ni atoms from the surface structure over repeated cycling does not impact performance as significantly as for many other materials. We further discuss the consequence of this type of effect 16


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and the relevance of the intermetallic-skin catalysts in the next paragraph(s). In general, it is known that for most of the compositional systems where Pt(1-x)-MNx alloys can be synthesized, the Pt-skin or a related core-shell structure either can be made or are thermodynamically the preferred structure due to lattice strain effects.1,6,38 Our results show agreement with this as can be seen in both Figure 4/6, the related Figure(s) in the Supplementary Information, and Table SI.2 of the Supplementary Information. However, we wish to emphasize that as a design goal; it would be interesting to identify candidates that show a descriptor value near the Volcano maximum for both the Pt-skin and the intermetallic-skin structures. The reason for this is because at harsh pH conditions and under repeated cycling, it can be expected that re-arrangement of the surface structure, dissolution of Pt atoms, and other effects may enhance the proportion of the surface sites with the intermetallic-skin structure. Unfortunately, as far as we are aware, to date, no known materials have been identified either in the previous experiments or in this study which lie very close to the volcano maximum position for both a Pt-skin and an intermetallic-skin Pt-subsurface structure(s). However, in light of the aforementioned qualitative and quantitative comparisons between recent experiments in the literature and our new data, we conclude that our predictions for the novel candidate systems warrant consideration for real-world catalyst testing should they be able to be feasibly synthesized. To help accelerate consideration of additional candidate materials and their associated screening, in the subsection Results and Discussion: Predictive Fit Plots for Descriptor Value by Composition Variables and Site of this manuscript we present a predictive fit model that can be used in conjunction with simple algebraic calculations based on elemental mass and valence.

Slab Model DFT-Enthalpy of Alloy Formation In the preceding section(s), we have provided results that discuss the DFT calculated energetics for the enthalpy of adsorption, ∆EadsO*, on high symmetry 3 fold sites of Pt3(MN)1 catalyst slab models of varying compositions and configurations (Pt-skin or Pt-subsurface layer). Because we are using 4-layer thick slab models which can mimic the exterior layers of 17



a core-shell or alloy surface of a real nanoparticle, we must discuss consideration of the thermodynamics of formation of our alloy/intermetallic slab model as a proxy for stability differences that might arise between our models and as-synthesized nanoparticles. We attempt to clarify our confidence in our results accordingly. First, we highlight that previous results using a small subset of Pt-based ternary alloys has shown agreement between DFT-predicted descriptor value and remarkably observed experimental activity using base metal dopants/guests such as Ni and Co.6 Next, we would highlight that among our proposed best candidate materials, the following set has been able to be synthesized and tested experimentally by other groups in at least reasonably similar composition (to 75-12.5-12.5): Pt-Pd-Au, Pt-Ru-Pd, Pt-Ni-Cu, Pt-Pd, Pt-Ru, Pt-Ag-Cu, and Pt-Pd-Ag.49−70 We provide in the Supplementary Information a table of the corresponding DFT ‘enthalpies’ of formation of the skin/alloy catalysts to their constitutive elements in corresponding FCC/HCP surface structures as composed of 16 atom slab-models. We further provide a table of the corresponding enthalpies of surface formation analysis of the four-layer Pt-skin vs. Pt-subsurface intermetallic catalysts at the same compositions as calculated in this work on a surface-area basis {difference in surface formation energy}. In the former analysis, it can is seen that for the Pt-skin models, more than 90% show DFT energetics that favor ‘stability’ compared to their constitutive elemental surfaces by analysis of DFT energies for 16-atom slab models. In the latter analysis, it can be seen that although almost all of the alloyskin/pt-subsurface models, the energy penalty to adopt such a structure is small compared to the typical surface formation energies of transition metal FCC(111) surfaces (on the scale of ~1% of the overall surface formation energy). Accordingly we conclude that although the former category of catalyst surface models presented in this work is likely to dominate the surface of as-synthesized alloy/intermetallic catalyst nanoparticles, it is premature to rule out the possibility (which we have raised previously) about small and localized regions of alloyskin/pt-subsurface as they are not significantly higher in energy, particularly when monoatomic oxygen adsorbs to their hollow sites. The real surface of as-synthesized catalyst NP 18


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may well have regions of both Pt-skin and Pt-subsurface depending on the kinetics of atomic rearrangement and the thermodynamics of surface segregation during the actual catalytic process post-synthesis. A definitive expansion of our model to account for such effects is beyond the scope of the work presented in this manuscript but could be considered as advanced future work. Based on the arguments above, and the known ability to synthesize working ORR NP catalysts of varying compositions as described in previous sections - at least catalysts of the Pt-skin intermetallic structure- we recommend the following catalysts be considered as viable candidates worthy of experimental validation. Pt-skins of Pt-Rh-Pd, Pt-Ru-Pd, Pt-Rh-Ir, Pt-Rh-Os, Pt-Ru-Os, Pt-Cu-Os, and Alloy overlayers of Cu-Ni-Pt on Pt-subsurface layer catalysts Given the error-bars for descriptor values and descriptor-predicted-activity as described above, these materials all are predicted to offer superior performance to previously described Pt and Pt-NiCo catalysts based on the results described in this manuscript.

Predictive Fit Plots for Descriptor Value by Composition Variables and Site Based on the results presented and discussed in the preceding subsections of Results and Discussion, we have created the predictive fit model plots shown in Figure 7 below. While the use of the Volcano Plot and the single descriptor ∆EadsO* significantly accelerates

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the search for Pt-based catalysts through the bypassing of costly computation of complete Free Energy Diagrams for every possible reaction pathway and set of intermediates, the work presented so far spanned 90+ compositions in

2 structural modes and 4 site types.

Accordingly, the data presented in this work identified 30-40 promising candidate ternary Ptbased alloys/intermetallics. However, most of the promising candidates still use costly/rare elemental species to fill the role of guest/dopant MN atoms in the Pt3(MN)1 catalyst. We have therefore performed best-fit analysis on the results presented in our work in this manuscript using the system valence and system atomic mass as normalized to that of pure Pt(111). Specifically, the 3d valence and the atomic mass of 16 atoms of Pt are used as the basis for normalization. All systems reported in this manuscript then have their 16 atoms (12 Pt, 2M, 2N) 3d valence and atomic mass calculated. The choice of the 3d electron valence and atomic mass correspond to a motivation to help ’screen’ future candidates simply by looking at the periodic table or applying combinatorial numeric analysis. The fits are surprisingly strong using a simple nonlinear 3rd order polynomial as implemented in Matlab. Figure 7 has top-down views and 3-d iso-views of regions of the fitted surface closest to the volcano maximum. The major benefit of this analysis as can be seen in Figure 7 is that it may be possible to predictively ’pre-screen’ further materials by merely applying arithmetic operations from the periodic table before doing large sets of DFT calculations to attain ∆EadsO* descriptor values for Pt3(MN)1 catalysts. The goal of future work is to validate this predictive fit using elements not studied in this work; compositions varying from Pt3(MN)1 such as Pt2(MN)2 or perhaps even quaternary systems such as Pt2(MNQ)2. The seeming optimal values for system valence and mass are ~ 0.92 to 0.96 and 0.97 to 1.00, respectively. Results for alternative fitting models (such as 5th order nonlinear polynomial and exponentialexponential fits) from the volcano peak are presented in the Supplementary Information document supplied with this manuscript. Neither of those alternative fit methods seems to offer the correct qualitative behavior even if the fit statistics are high. The fit equation is also provided in the Supplementary Information. 20


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Figure 7: Left: 3d iso-view top-down views of best-fitted 3rd order polynomial for site 1 (top row), site 2 (second row), site 3 (third row), site 4 (fourth row), and all sites (bottom row). Right: top-down views of best-fitted3rd order polynomial for site 1 (top row), site 2 (second row), site 3 (third row), site 4 (fourth row), and all sites (bottom row). All plots use units of kJ/mol for energy. 21 ACS Paragon Plus Environment


Conclusions In this manuscript we have presented the work from DFT calculations to examine the ∆EadsO* on FCC(111) surface of Pt-skin NSA Intermetallic and intermetallic skin Ptsubsurface catalysts with a Pt3(MN)1 composition. We have compared our results for each type of model surface to previously generated Pt3(MN)1 ternary intermetallic systems and their relative position on the precious metal ORR Volcano Plot. Our results to date indicate that overwhelmingly the vast percentage of compositions which can improve both the overpotential and specific current for the ORR fall in the class of Pt-skin NSA Intermetallic catalyst surface structure in the FCC(111) model for these compositions. Our best-identified catalysts while improving performance according to their position on the Volcano plot unfortunately still use high loadings of precious metals in the MN role. To complete our analysis and guide future work, we have made a new predictive fit model for the distance from the ORR Volcano peak as a function of a normalized alloy/intermetallic valence and atomic mass compared to an ideal Pt(111) surface of the same size and dimension. Further efforts are underway to use this predictive fit model to help pre-screen other candidate ternary (and higher order) alloy/intermetallics to further increase the sustainability of transition metal ORR catalyst design for PEMFC applications.

Acknowledgments The authors acknowledge support from the Department of Chemical Engineering, and the College of Engineering at Villanova University for assistance in funding this work. The authors acknowledge assistance from the Villanova UNIT team for computer support during the execution of this research project. The authors declare/acknowledge that there are no conflicts of interest or competing concerns with this research or its publication.

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Computational Screening for Developing Optimal Intermetallic

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