Toward a Design of Active Oxygen Evolution Catalysts: Insights from

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Towards a design of active oxygen evolution catalysts: Insights from automated density functional theory calculations and machine learning Seoin Back, Kevin Tran, and Zachary W Ulissi ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.9b02416 • Publication Date (Web): 15 Jul 2019 Downloaded from pubs.acs.org on July 17, 2019

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Towards a design of active oxygen evolution catalysts: Insights from automated density functional theory calculations and machine learning Seoin Back, Kevin Tran, and Zachary W. Ulissi∗ Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA E-mail: [email protected] Abstract Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we provide catalyst design strategies to improve catalytic activity of Ir based catalysts and

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demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.

Keywords density functional theory calculations, water splitting, oxygen evolution reaction, Ir oxide, convolutional neural network, machine learning, high-throughput screening

1

Introduction

The electrochemical oxygen evolution reaction (OER) is one of the most important reactions in renewable energy technology; it is an anode reaction of water splitting to produce H2 and O2 , and it can be coupled with cathodic reactions—such as CO2 or N2 reduction reactions—to produce valuable chemicals at ambient conditions, which otherwise requires energy-intensive processes. 1 When performing OER in acidic conditions (pH ≈ 0), a challenge is that a material choice is limited to noble metal-based oxides (IrO2 and RuO2 ) to survive under the acidic environment and high electrochemical potentials. 2 Typically, Ir based catalysts showed lower OER activity, but higher stability under the acidic condition compared to Ru based catalysts. 3,4 On the other hand, cost effective catalysts consisting of Ni, Mn, Fe and Co have exhibited reasonable OER activities in alkaline condition (pH ≈ 14), but are unstable in acidic conditions. 5 Due to several advantages of acidic OER such as a higher current density and a lower Ohmic loss, the noble metal-based catalysts have been actively investigated to further improve their intrinsic activity to minimize metal contents. 6–12 Density functional theory (DFT) calculations have been employed to model oxide catalyst surfaces and to calculate their thermodynamic OER overpotential, which is related to activity, onset potential, and faradaic efficiency. In an acidic condition, the following

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four-step OER mechanism is assumed: 13

∗ + H2 O ↔ OH ∗ +(H + + e− )

(1a)

OH∗ ↔ O ∗ +(H + + e− )

(1b)

O ∗ +H2 O ↔ OOH ∗ +(H + + e− )

(1c)

OOH∗ ↔ ∗ + O2 + (H + + e− )

(1d)

where * denotes an active site of the catalyst surface, and O*, OH*, OOH* indicate adsorbed species on the active site. This simplified OER mechanism allows us to calculate thermodynamic OER overpotential (η) as:

η = max[∆G1a , ∆G1b , ∆G1c , ∆G1d ]/e − 1.23V

(2)

Extensive DFT reports have calculated η for various types of materials, and a qualitative comparison of theoretical and experimental η values 14 confirmed that theoretical overpotential is a useful measure of the OER activity of catalysts, although the theoretical η does not equivalently correspond to the experimental η. A direct comparison between theoretical and experimental η will require computationally expensive calculations by including water layers and calculating potential dependent proton transfer barriers. 15,16 Nonetheless, this theoretical framework has been successful in understanding the origin of experimentally observed catalytic activities 13,17,18 and discovering state-of-the-art catalysts. 19,20 Unfortunately, most studies have focused on a few specific surfaces due to the added complexity of modelling various oxide surfaces. The most or the second-most stable surfaces of the most stable crystal structure have been chosen mainly as they are more likely to exist in situ due to their relatively low surface energies. 14,17 However, it should be mentioned that less stable surfaces could significantly contribute to the activity. For example, undercoordinated and less stable

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surfaces of Au and Cu were found to be more active than the most stable flat surfaces for CO2 electroreduction. 21,22 In addition, studying only a few stable facets of the most stable crystal structure could miss an opportunity to design new high-performing catalysts consisting of less stable crystal structures, where the stability could be improved through various experimental strategies as will be discussed below. We thus expect that a consideration of various active sites, surfaces and crystal structures of the oxide catalysts provide a deeper understanding of the OER activity and a guidance for a catalyst design. To our knowledge, a theoretical study of various active sites, surfaces, and crystal structures of metal oxides has never been reported. Furthermore, to accurately investigate catalytic properties of oxide materials, it is essential to consider a surface coverage, i.e., termination, at reaction conditions since a binding strength of adsorbates, and thus the catalytic activity, is significantly affected by the local environment of the active site. 14,17,23 Ignoring reaction-relevant surface coverages would mispredict the catalytic activity. Here, we systematically investigate OER catalytic activity of various surfaces of Ir oxides (IrO2 and IrO3 ). We automate a procedure of DFT calculations to find unique surface terminations and active sites; determine the most stable surface termination at the OER potential region; and calculate binding free energies of reaction intermediates to predict OER overpotentials. We calculate the theoretical overpotentials of many active sites of numerous Ir oxide structures and discover new motifs that are more active than the most studied rutile (110) surface. In addition, we apply a crystal graph convolutional neural network to predict binding free energies from initial unrelaxed atomic structures and basic atomic information, which will be an effective approach to reduce the number of DFT jobs required to reach a reasonable accuracy for the future high-throughput catalyst screening.

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a. P42/nm

d. I41/amd

f. Pm3m

g. Cmcm b. Pa3

e. Pbca

h. Amm2

c. Pbcn

Figure 1: Atomic structures and space groups of IrO2 (light green) and IrO3 (dark green) polymorphs. We note that (c) and (e) are hypothetical structures taken from TiO2 polymorphs.

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2

Models and methods

2.1

Models

To evaluate many possibilities of active motifs of Ir oxides, we modelled various reported and hypothetical crystal structures of Ir oxides. Three crystal structures of each IrO2 and IrO3 are taken from the Materials Project. 24 In addition, two hypothetical structures of IrO2 were adopted from TiO2 polymorphs. 25 All crystal structures considered in this work are presented in Figure 1.

2.2

Calculation details

Spin-polarized DFT calculations were performed using Vienna Ab-initio Simulation Package (VASP) 26,27 with the generalized gradient approximation (GGA) PBE exchange-correlation functional 28 and projector augmented wave (PAW) method. 29 We note that a choice of the exchange-correlation functional is important, and our calculation results on IrO2 (110) are in agreement with the previous calculations. 14,30 A kinetic cutoff energy for the planewave basis was set to 500 eV. Monkhorst-pack k-point meshes k1 × k2 × k3 (k1 × k2 × 1) for bulk (slab) calculations were chosen so that all components of a product of (k1 , k2 , k3 ) with a norm of a lattice parameter (a1 , a2 , a3 ) of a cell are larger than 30 (20) ˚ A, that is, an kn (n=1, 2, 3) > 30 (20) ˚ A. For all slab geometries, 12 ˚ A of a vacuum layer was added in the z-direction to avoid an imaginary interaction between repeating atomic structures. The bottom two layers were fixed to the bulk positions and the remaining atoms were relaxed until the residual force becomes less than 0.05 eV/˚ A. The electronic self-consistent iteration was stopped if the energy difference between two steps becomes less than 10−4 eV. We have tested various additional factors in the atomistic modelling; H termination on singly-coordinated bottom O atoms to quench dipoles, 31 a dipole correction to correct artificial dipole-dipole interactions between periodic images in z-direction, 32 and a compensation of surface charges. 33 We did not include these aspects in the full calculations to improve the 6


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calculation efficiency as their effects on ∆GO∗ are calculated to be at best 0.06 eV in average (Table S3). The calculated electronic energies were converted into free energies by adding zero-point energies, enthalpic, and entropic contributions of adsorbates obtained from a Harmonic Oscillator approximation at 300 K as implemented in Atomic Simulation Environment (ASE). 34 The free energy correction values of adsorbates calculated at rutile IrO2 (110) were used for all surfaces since they are not strongly dependent on surfaces. 35 We assume that free energy corrections of other surface atoms are cancelled out when we calculate binding free energies of adsorbates. We note that including surface Ir atom directly interacting with adsorbates in the free energy calculations showed negligible effect ( 1, where W is the calculated barrier and kB T is 0.025 eV at 300 K). Thus, we expect that the Harmonic Oscillator approximation could provide a reasonable estimate for entropies. Configuration entropy was calculated to be negligible, thus not included in the free energy calculations (see Supporting Information). Free energy corrections for gaseous molecules were obtained from the ideal gas approximation at 101,325 Pa and 3,534 Pa for H2 and H2 O, respectively. 38 H2 O pressure of 3,534 Pa is the vapor pressure of H2 O at 300 K, where the free energy of gaseous H2 O is equal to that of liquid H2 O. 39 All the correction values are summarized in Table S1. We used the computational hydrogen electrode (CHE) method 38 to include an effect of the electrode potential. It assumes the chemical potential of a proton-electron pair equals to that of gas-phase H2 (G(H + + e− ) = 0.5G(H2 )) in

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standard condition (pH=0 and PH2 = 101,325 Pa). As the potential Uelec is applied, the chemical potential of the electron is shifted by −eUelec , where n is the number of electrons involved in the reaction, thus, G(H + + e− ) = 0.5G(H2 ) − eUelec . This method enabled to calculate potential dependent binding free energies of adsorbates and the theoretical OER overpotentials.

2.3

Automation of DFT workflow

DFT calculations were automated using various modules implemented in the python packages Pymatgen 40 and ASE 34 to follow computational chemists' general procedures to investigate activity of oxide catalysts. 13,23 A workflow is illustrated in Figure 2. After relaxing bulk unit cells of Ir oxides, we modelled various slab structures with Miller indices within [0, 2], inclusively. In many cases, different shift values when generating slabs in Pymatgen corresponded to different terminations of surfaces, and all unique surface terminations were considered. We note that all the generated slab structures at this stage are terminated with O atoms both at the top and at the bottom. DFT calculations were limited to slabs consisting of less than 100 atoms in order to reduce computational burden.

2.3.1

Coverage determination

In the electrochemical OER conditions, catalyst surfaces can be terminated with relevant reaction intermediates such as O*, OH*, OOH* depending on the relative stability of the surfaces at a relevant potential. 17,23 Three extreme coverages were considered: O* terminated, OH* terminated and empty (*) surfaces. We assume that simple coverage calculations could provide a reasonable estimate of the surface coverage and they are suitable for a high-throughput screening. However, we note that complete coverage calculations will be more appropriate when studying a limited number of surfaces (Figure S1). We first identified surface O atoms singly coordinated to Ir, which are more likely to act as a reaction center due to their undercoordinated nature and weaker interaction with Ir compared to doubly 8


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1. Relax Bulk

IrO2 rutile 2. Generate unique slab models

IrO2 rutile (111) ter-1

IrO2 rutile (111) ter-2

3. Determine surface coverage (*, O*, OH*)

Bare surface

O* covered

OH* covered

4. Identify all unique active sites

5. Calculate ΔGO*, ΔGOH*, ΔGOOH* and η

*

OH*

O*

OOH*

Figure 2: A workflow and atomic structure examples associated with each step to systematically investigate OER activity of oxide materials. (111) facet of rutile IrO2 is shown as an example. Three different colored oxygen atoms (grey, blue and purple) in 4 indicate three unique active sites. Each unique oxygen atom is removed to generate bare surfaces, or H and OH were attached to the oxygen atom to generate OH* and OOH* adsorbed surfaces, respectively.

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coordinated O atoms. ∗ + nH2 O ↔ nO ∗ +2n(H + + e− )

(3a)

∗ + nH2 O ↔ nOH ∗ +n(H + + e− )

(3b)

To simulate OH* covered surfaces or empty surfaces, we added H to all singly coordinated O atoms or removed those O atoms at the top surface, respectively. It is worthwhile to mention that three orientations of OH* were considered and the most stable one was used. In addition, there can be several bare surfaces depending on combinations of singly coordinated O atoms we remove. We used the most stable bare surface, where the free energy is set to 0 eV. More details can be found in Figure S2. We then considered O* and OH* adsorption processes as shown in Eqn. (3a) and (3b), where n is the number of species on the surface, and calculated binding free energies of O* and OH* following Eqn. (4a) and (4b). The effect of potential is implemented as Eqn. (5a) and (5b) as a generation of O* and OH* releases 2 and 1 electron per adsorbate, respectively. Based on the calculated binding free energies, we generated a surface Pourbaix diagram, determined the most stable surface termination at 1.63 VRHE (0.4 V overpotential) and used it as a reference surface for further OER activity calculations. Surface Pourbaix diagrams of all considered surfaces are shown in Figure S4-11.

=0V ∆GUnO∗ = GnO∗ + nGH2 − G∗ − nGH2 O

=0V ∆GUnOH∗ = GnOH∗ +

2.3.2

n GH2 − G∗ − nGH2 O 2

(4a)

(4b)

6=0V =0V ∆GUnO∗ = GUnO∗ − 2neU

(5a)

6=0V =0V ∆GUnOH∗ = GUnOH∗ − neU

(5b)

OER activity calculations

Once we determined the surface coverages, we identified unique and singly coordinated surface O (OH) atoms of O* (OH*) covered surfaces. Ir atoms coordinated to those O (OH) 10


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are considered as active sites. For each unique site, four cases (*, O*, OH*, OOH*) were explicitly calculated to establish OER energetics. We note that three orientations for OH* and OOH* were considered and the most stable one was used.

2.4

Convolutional neural network (CNN)

A crystal graph convolutional neural network (CNN) method recently developed by Xie and Grossman 41 converts atomic structures into a graph representation that includes atomic information and bonding information between atoms, and performs a convolutional neural network on top of the graph representation. It predicted various bulk properties within a DFT accuracy using 30,000 data. To be applicable for adsorption energy prediction of slab geometries, we modified the code to use Voronoi connectivity to capture geometric information rather than atomic distances of neighboring atoms as in the original code. Our modification in CNN code and its performance on predicting adsorption energies will be presented in more detail in a separate publication (Figure S17). DFT results were divided into 20 %, 20 % and 60 % for test, validation and training sets, respectively. The optimized hyperparameters are summarized in Table S2. In the case where there are many active sites for one surface during the OER calculations, we fed adjusted energies into the CNN, where the adjustment value is the difference in bare surface energies of unique active sites relative to the most stable one (Figure S3).

3

Results and discussions

We first compare the catalytic activity of various surfaces of the most stable rutile structure and link the calculated results with the previous experimental observations. The IrO2 (110) surface is the most commonly investigated catalyst surface for the OER, 14,16,18 mainly because its relatively low surface energy causes it to be more experimentally common in synthesized catalysts. 42 From the surface Pourbaix analysis (Figure S4), we found that all

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rutile IrO2 surfaces prefer O* coverage at the OER potential region. The calculated OER overpotential of (110) surface is 0.51 V, similar to the previous DFT calculations. 14,18,43 The OER overpotential of the second most stable (100) surface was calculated to be 0.48 V, which could be linked to the previous experimental observation by Stoerzinger et al. that IrO2 (100) is more active for OER than (110). 44 Further, we found that two unique terminations of (111) surfaces have highly active sites, suggesting that low index surfaces other than (110) are more active than the most stable (110) surface (Figure 3). The analysis on other higher index surfaces revealed that they consist of several active sites that are ∼0.2 V more active in overpotential than (110) surface. Particularly, all active sites of (121) surface are predicted to have lower overpotentials than (110) and (100) surfaces. Interestingly, the previous DFT calculations on various kink and step sites of RuO2 also revealed that some of these higher index surfaces outperformed (110) surface by as much as 0.5 V in overpotential. 35 A comparative experimental study on IrO2 morphologies by Abbott et al. revealed that the domination of (110) surface resulted in the lowest intrinsic activity compared to other morphologies consisting of more active high index IrO2 surfaces. 9 An optimization strategy that can be drawn from the DFT results of the rutile structure and the previous experimental observations is to expose either (100) or (111) low index surfaces rather than (110) and to create small IrO2 nanoparticles to maximize the number of active high index surfaces such as kink or stepped sites. In Figure 4, we illustrate the most active sites of each IrO3 structure, but we mention that many sites of IrO3 exhibited similar binding strength of adsorbates and OER overpotentials (Table S4), which could be due to very similar local structures of IrO3 . It is interesting to note that both low index surfaces (Amm2 (110) and Cmcm (100)) and high index edge site (Pm3m (120)) exhibited similar high activity. However, it should be noted that IrO3 has been rarely studied compared to IrO2 since its isolated form is unstable in the absence of alkali, 45,46 lanthanide, 18 or post-transition metals. 47 Recently, Seitz et al. reported that acid leaching of Sr from surface layers of thin films

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0.46 V

0.48 V

0.44 V 0.43 V

0.39 V

0.52 V 0.30 V

(100) 0.35 V

0.64 V

(012)

0.26 V

0.29 V

(121) 0.70 V

0.47 V

0.47 V

0.39 V

(120)-ter 0 (120)-ter 1

(111)-ter 0 (111)-ter 1

Figure 3: All considered unique active sites of various surfaces of rutile IrO2 with the calculated OER overpotential values on each site. Each dashed circle indicates a unique active site. For (111) and (120) surfaces, two unique terminations are presented.

0.27 V

0.27 V

Amm2 (110)

Pm3m (120)

0.28 V

Cmcm (100)

Figure 4: The most active sites of each IrO3 structure and their OER overpotentials. We note that Cmcm (100) surface is most stable with OH* coverage.

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SrIrO3 resulted in a formation of IrOx on top of SrIrO3 which showed very high activity and stability for acidic OER. 18 Their Ab initio molecular dynamics simulations and DFT calculations suggested that high activities could be originated from the formation of IrO3 and anatase IrO2 motifs. This work implies that IrO2 structures other than the most stable rutile, and rarely studied IrO3 could be utilized to further improve OER activity of Ir oxides. A similar leaching strategy has been applied to Ir Pyrochlores (Bi2 Ir2 O7 , Pb2 Ir2 O7 , Y2 Ir2 O7 and their mixtures). Particularly, Y2 Ir2 O7 approached activity of the state-of-the-art IrO2 nanoparticle catalysts, where its high activity and stability are correlated with Y3+ leaching and the formation of IrOx surface layer. 48 A different approach has also been reported as a possible strategy to utilize IrO3 : oxide layer formation on top of other types of inorganic materials during electrochemical reactions. For example, Zhao et al. reported a formation of Co oxide thin films on CoS2 under O2 reduction reaction (ORR) conditions, and DFT calculations suggested that the oxide layers could contribute to the improved ORR activity. 49 Kreider et al. also observed an amorphous surface oxide formation on top of Ni nitrides during the ORR. 50 We expect these approaches could be employed to utilize IrO3 for the active oxygen evolution. We then extend our analysis toward the best active sites of various IrO2 /IrO3 structures and discuss possible design strategies to improve OER catalytic activity of Ir based catalysts. Scaling relations between binding free energies of adsorbates are presented in Figure S12. The relation between ∆GOH∗ and ∆GOOH∗ (∆GOOH∗ =0.98∆GOH∗ +3.04) agrees well with the previously reported scaling relation for IrO2 /IrO3 18 and various catalyst materials. 14,51 We observed that an energy range spans ∼1.5 eV for all adsorbate cases, implying that a significant activity improvement of Ir oxide materials can be achieved through a finetuning of catalyst morphologies. In Figure 5 (a), we present a two-dimensional volcano plot that predicts OER overpotential based on ∆GOOH∗ =0.98∆GOH∗ +3.04 scaling, and ∆GOH∗ , ∆GO∗ − ∆GOH∗ . For simplicity, we only plotted the best active sites of each structure, but all data point can be found in Figure S13. Particularly, white star symbols in Figure 5 (a)

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ACS Catalysis

a.

b. P42/nm

c. I41/amd

d. Pa3

e. Pbca

2.0

1.5

IrO3/SrIrO3 (3 ML)

e h 1.0

g i f c

b Anatase IrO2 (100)

0.5

d

Rutile IrO2 (100)

Rutile IrO2 (110)

0.0 1.0

f. Pbcn

1.2

1.4

1.6

1.8

2.0

g. Amm2

h. Pm3m

i. Cmcm

Figure 5: (a) Two dimensional OER volcano plot to predict overpotential as a function of ∆GOH∗ and ∆GO∗ − ∆GOH∗ . The IrO2 /IrO3 scaling relation between ∆GOH∗ and ∆GOOH∗ was adopted to generate the volcano plot. The best active sites of each crystal structure are plotted, where the calculated energies are summarized in Table S5. Star symbols in (a) are results from literature, 18 where only a limited number of surface were investigated for each crystal structure. (b)∼(i) are side views of OOH* adsorbed surface of the best sites as marked in Figure 5 (a). Light and dark green correspond to IrO2 and IrO3 structures, respectively. show the calculated results for Ir oxides taken from Ref. 18, where only a few surfaces were examined. Our calculation results of the relevant rutile (Figure 5 (b)) and anatase (Figure 5 (c)) structures indicate that there are more active sites than the previously examined ones that have been unexplored by conventional approaches. We discovered very active sites of both IrO2 and IrO3 with the overpotentials ranging from 0.22 V to 0.28 V. The Bader charge analysis 52 for the best active sites and rutile (110) illustrates that there is a correlation between ∆GOH∗ and Bader charge of surface Ir atom (Figure S14 and Table S5). Surface Ir atom in rutile (110) is least oxidized (+1.52 |e|) compared to the best Ir sites, where 15


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|e| ranges from +1.61 to +2.00. Interestingly, surface Ir atom in (111) surface of the same rutile structure was calculated to have +1.84 |e| and much weaker ∆GOH∗ (0.88 eV vs. 0.09 eV on rutile (110)). Considering that active OER catalytic sites should have a moderate ∆GOH∗ (0.3 eV ∼ 1.7 eV in Figure 5 (a)) as well as ∆GO∗ − ∆GOH∗ close to 1.5 eV, lower activity of rutile (110) could be attributed to less oxidized Ir atom and corresponding stronger binding of species. Less oxidation of Ir atoms in rutile (110) surface could be attributed to more closed characteristic of this surface as evidenced by the experiments. 44,53 We note that adsorbate binding strengths on IrO3 are generally weaker than on IrO2 , possibly due to highly oxidized nature of IrO3 . In Figure S15, IrO2 are mainly located at the strong binding region, lying on the lower side of the volcano (stronger ∆GOH∗ , Figure S13), while IrO3 are at the weak binding region. Figure S15 shows that the rutile crystal structures bind O* and OH* most strongly on average, and binding free energies become weaker as the crystal structures change. Notably, all IrO3 values are similarly close to the ideal catalyst values. Altogether, we expect that higher oxidation states of surface Ir could lead to higher OER activity. It is worth mentioning that some of newly discovered surfaces are more stable than rutile (110) in terms of surface energies (Table S5), although the crystal structures are less stable than the rutile structure. This highlights the importance of stabilizing such crystal structures to expose those active surfaces. The two-dimensional volcano plot allows prediction of OER overpotential of the active sites based on ∆GOH∗ and ∆GO∗ . In Figure S16, we present the histogram of ηDF T − ηvolcano , where the average difference is calculated to be as low as 0.04 V. Although this scaling relation and volcano-based overpotential prediction is very accurate and has been successful in finding new catalysts and in suggesting design strategies, we emphasize that it is not suitable for finding the ideal catalysts. 54–56 For example, the ideal OER catalytic site should have 1.23 eV, 2.46 eV and 3.69 eV for ∆GOH∗ , ∆GO∗ and ∆GOOH∗ , respectively, where ∆GOOH∗ − ∆GOH∗ is 2.46 eV. This is far from ∆GOOH∗ = ∆GOH∗ +3.0(±0.2) scaling observed in this study and in literature, 57 and the ideal catalyst has little chance to be discovered through the scaling

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relation. Thus, it is important to calculate all adsorbate binding energies rather than relying on the scaling relation to develop groundbreaking catalysts.

a.

b.

Coverage calculations 4

train MAE=0.05, RMSE=0.07, R2=0.97 valid MAE=0.05, RMSE=0.07, R2=0.96 test MAE=0.07, RMSE=0.10, R2=0.93

OER calculations train MAE=0.05, RMSE=0.07, R2=0.98 valid MAE=0.11, RMSE=0.16, R2=0.86 test MAE=0.13, RMSE=0.18, R2=0.80

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Figure 6: Parity plots between CNN-predicted and DFT-calculated ∆G for (a) coverage calculations and (b) OER calculations. Insets are histograms of the DFT-calculated ∆G for coverage calculations (O* and OH*) and OER calculations (O*, OH* and OOH*). Dashed vertical lines in inset figures are ∆G of the ideal catalyst: 1.23 eV, 2.46 eV and 3.69 eV for ∆GOH∗ , ∆GO∗ and ∆GOOH∗ , respectively. However, DFT calculations of all sites, surfaces and adsorbates could be very computationally demanding. For example, we have performed more than 800 and 1,200 DFT calculations to determine coverages and calculate OER overpotentials of Ir oxides, respectively, and we spent more than 3 million CPU hours. This high computational cost of DFT calculations can be alleviated by recent developments of machine learning (ML) methods, 58–61 as ML could help to reduce the number of DFT jobs necessary to achieve a reasonable accuracy. In this sense, we applied CNN to predict binding free energies (∆G) from initial atomic geometries that are unrelaxed by DFT. Different from other ML applications where users explicitly provide fingerprints of various chemical and physical features of consisting atoms and atomic structures, CNN utilizes a graph representation of atomic structures and basic atomic information as inputs. 41 We particularly note that we trained with ∆G standardized relative to that of the ideal catalyst, ∆GX − ∆GX,Ideal where X=O*, OH*, OOH*. For 17


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example, ∆GOH∗ − 1.23 eV, ∆GO∗ − 2.46 eV and ∆GOOH∗ − 3.69 eV were provided to train the CNN, and the values were restored after training. This is because a strong binding region of one adsorbate (3.5∼4.0 eV of ∆GOOH∗ ) corresponds to a weak binding region of another adsorbate (3.5∼4.0 eV of ∆GO∗ ) as shown in Figure 6 (b) inset. By standardizing with respect to the free energies of the ideal catalysts, the distinction between weak and strong binding regions holds across the different adsorbates. We note that the training score was worse otherwise (Figure S17). We achieved test error of 0.07 eV (MAE), 0.10 eV (RMSE) for coverage calculations, and 0.13 eV (MAE), 0.18 eV (RMSE) for OER calculations (Figure 6 (a) and (b)). This result is remarkable considering that the number of our training data is 300 and 500 for coverage and OER calculations, respectively, while our group previously reported a similar prediction accuracy using more than 16,000 data and fingerprints-based surrogate models for CO and H adsorption energy prediction. 58 A greater prediction accuracy of the CNN method may be attributed to the relatively small search space of oxide systems where initial and final geometries are similar and adsorbates did not move considerably. A learning curve shows that at best 300 and 400 data (180 and 240 training data) are required to achieve less than 0.10 and 0.20 eV MAE for the coverage and OER calculations, respectively (Figure S18). From this result, we expect that CNN will help to reduce the number of DFT jobs and to predict the catalytic activity accurately. The DFT calculation results presented herein highlight the importance of investigating all possible active sites for the future design of active catalysts. We believe that a thorough search of various sites of different surfaces will help identify promising active sites from undiscovered chemical space. This work is the first step towards large screens of complex oxide surfaces.

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Conclusions

Oxide catalysts are significantly more complicated to study than transition metal catalysts due to added complexity of modelling various facets and surface coverages at reaction conditions. To tackle this challenge for the high-throughput catalyst screening, we have developed the automated approach to effortlessly predict the catalytic activity, and we applied our approach to study various previously-unstudied facets and polymorphs of Ir oxide catalysts. We find several active sites and surfaces of IrO2 and IrO3 that are more active than the most studied rutile (110) surface, and our results are well correlated with the previous experimental observations. Comparing the most active sites of various crystal structures with rutile (110), we suggest that less oxidized Ir atoms in rutile (110) and corresponding stronger binding of species could be the reason of lower activity. Based on our DFT results, catalyst design strategies can be drawn to further boost OER activity of Ir based catalysts: (1) expose more active low index surfaces other than rutile (110), (2) make smaller Ir oxide nanoparticle to maximize the number of active high index surfaces, (3) make surface Ir atoms have higher oxidation states as more oxidized Ir atoms were found to be more active for OER. To perform high-throughput catalyst screening in the follow-up research, we introduce the convolutional method based on CNN to accurately predict both the surface Pourbaix diagram and binding free energies. This will significantly reduce the computational cost for future studies. We expect our integrated computational framework of DFT and ML to facilitate the discovery of promising OER catalysts.

Supporting Information Available Free energy corrections, all calculated binding free energies and overpotentials, surface Pourbaix diagrams, a trend of adsorbate binding as a function of crystal structures, optimized CNN hyperparameters, a learning curve of the CNN. 19


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This material is available free of charge via the Internet at http://pubs.acs.org/.

Acknowledgement This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. We thank Dr. Stefan Ringe (KAIST) for helpful discussions on atomistic modelling.

References (1) Seh, Z. W.; Kibsgaard, J.; Dickens, C. F.; Chorkendorff, I.; Nørskov, J. K.; Jaramillo, T. F. Combining Theory and Experiment in Electrocatalysis: Insights into Materials Design. Science 2017, 355, eaad4998. (2) Carmo, M.; Fritz, D. L.; Mergel, J.; Stolten, D. A Comprehensive Review on PEM Water Electrolysis. Int. J. Hydrog. Energy 2013, 38, 4901–4934. (3) Danilovic, N.; Subbaraman, R.; Chang, K.-C.; Chang, S. H.; Kang, Y. J.; Snyder, J.; Paulikas, A. P.; Strmcnik, D.; Kim, Y.-T.; Myers, D. Activity-stability Trends for the Oxygen Evolution Reaction on Monometallic Oxides in Acidic Environments. J. Phys. Chem. Lett 2014, 5, 2474–2478. (4) Cherevko, S.; Zeradjanin, A. R.; Topalov, A. A.; Kulyk, N.; Katsounaros, I.; Mayrhofer, K. J. Dissolution of Noble Metals during Oxygen Evolution in Acidic Media. ChemCatChem 2014, 6, 2219–2223. (5) McCrory, C. C.; Jung, S.; Peters, J. C.; Jaramillo, T. F. Benchmarking Heterogeneous

20


Page 20 of 28

Page 21 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Electrocatalysts for the Oxygen Evolution Reaction. J. Am. Chem. Soc. 2013, 135, 16977–16987. (6) Lin, Y.; Tian, Z.; Zhang, L.; Ma, J.; Jiang, Z.; Deibert, B. J.; Ge, R.; Chen, L. Chromium-Ruthenium Oxide Solid Solution Electrocatalyst for Highly Efficient Oxygen Evolution Reaction in Acidic Media. Nat. Commun. 2019, 10, 162. (7) Kim, Y.-T.; Lopes, P. P.; Park, S.-A.; Lee, A.-Y.; Lim, J.; Lee, H.; Back, S.; Jung, Y.; Danilovic, N.; Stamenkovic, V. Balancing Activity, Stability and Conductivity of Nanoporous Core-Shell Iridium/Iridium Oxide Oxygen Evolution Catalysts. Nat. Commun. 2017, 8, 1449. (8) Lee, H.; Kim, J. Y.; Lee, S. Y.; Hong, J. A.; Kim, N.; Baik, J.; Hwang, Y. J. Comparative Study of Catalytic Activities among Transition Metal-doped IrO2 nanoparticles. Sci. Rep 2018, 8, 16777. (9) Abbott, D. F.; Lebedev, D.; Waltar, K.; Povia, M.; Nachtegaal, M.; Fabbri, E.; Coperet, C.; Schmidt, T. J. Iridium Oxide for the Oxygen Evolution Reaction: Correlation between Particle Size, Morphology, and the Surface Hydroxo Layer from Operando XAS. Chem. Mater. 2016, 28, 6591–6604. (10) Sun, W.; Liu, J.-Y.; Gong, X.-Q.; Zaman, W.-Q.; Cao, L.-M.; Yang, J. OER Activity Manipulated by IrO6 Coordination Geometry: An Insight from Pyrochlore Iridates. Sci. Rep 2016, 6, 38429. (11) Grimaud, A.; Demortière, A.; Saubanère, M.; Dachraoui, W.; Duchamp, M.; Doublet, M.-L.; Tarascon, J.-M. Activation of Surface Oxygen Sites on an Iridium-based Model Catalyst for the Oxygen Evolution Reaction. Nat. Energy 2017, 2, 16189. (12) Escudero-Escribano, M.; Pedersen, A. F.; Paoli, E. A.; Frydendal, R.; Friebel, D.; Malacrida, P.; Rossmeisl, J.; Stephens, I. E.; Chorkendorff, I. Importance of Surface IrOx in Stabilizing RuO2 for Oxygen Evolution. J. Phys. Chem. B 2017, 122, 947–955. 21


ACS Catalysis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(13) Friebel, D.; Louie, M. W.; Bajdich, M.; Sanwald, K. E.; Cai, Y.; Wise, A. M.; Cheng, M.-J.; Sokaras, D.; Weng, T.-C.; Alonso-Mori, R. Identification of Highly Active Fe Sites in (Ni, Fe) OOH for Electrocatalytic Water Splitting. J. Am. Chem. Soc. 2015, 137, 1305–1313. (14) Man, I. C.; Su, H.; CalleVallejo, F.; Hansen, H. A.; Martnez, J. I.; Inoglu, N. G.; Kitchin, J.; Jaramillo, T. F.; Nørskov, J. K.; Rossmeisl, J. Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces. ChemCatChem 2011, 3, 1159–1165. (15) Chan, K.; Nørskov, J. K. Potential Dependence of Electrochemical Barriers from abinitio Calculations. J. Phys. Chem. Lett. 2016, 7, 1686–1690. (16) Gauthier, J. A.; Dickens, C. F.; Chen, L. D.; Doyle, A. D.; Nørskov, J. K. Solvation Effects for Oxygen Evolution Reaction Catalysis on IrO2 (110). J. Phys. Chem. C 2017, 121, 11455–11463. (17) Bajdich, M.; Garca-Mota, M.; Vojvodic, A.; Nørskov, J. K.; Bell, A. T. Theoretical Investigation of the Activity of Cobalt Oxides for the Electrochemical Oxidation of Water. J. Am. Chem. Soc. 2013, 135, 13521–13530. (18) Seitz, L. C.; Dickens, C. F.; Nishio, K.; Hikita, Y.; Montoya, J.; Doyle, A.; Kirk, C.; Vojvodic, A.; Hwang, H. Y.; Nørskov, J. K. A Highly Active and Stable IrOx /SrIrO3 Catalyst for the Oxygen Evolution Reaction. Science 2016, 353, 1011–1014. (19) Zhang, B.; Zheng, X.; Voznyy, O.; Comin, R.; Bajdich, M.; Garca-Melchor, M.; Han, L.; Xu, J.; Liu, M.; Zheng, L. Homogeneously Dispersed Multimetal Oxygen-evolving Catalysts. Science 2016, 352, 333–337. (20) Lu, Z.; Chen, G.; Li, Y.; Wang, H.; Xie, J.; Liao, L.; Liu, C.; Liu, Y.; Wu, T.; Li, Y. Identifying the Active Surfaces of Electrochemically Tuned LiCoO2 for Oxygen Evolution Reaction. J. Am. Chem. Soc. 2017, 139, 6270–6276.

22


Page 22 of 28

Page 23 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

(21) Back, S.; Yeom, M. S.; Jung, Y. Active Sites of Au and Ag Nanoparticle Catalysts for CO2 Electroreduction to CO. ACS Catal. 2015, 5, 5089–5096. (22) Peterson, A. A.; Abild-Pedersen, F.; Studt, F.; Rossmeisl, J.; Nørskov, J. K. How Copper Catalyzes the Electroreduction of Carbon Dioxide into Hydrocarbon Fuels. Energy Environ. Sci. 2010, 3, 1311–1315. (23) Back, S.; Hansen, M. H.; Garrido Torres, J. A.; Zhao, Z.; Nørskov, J. K.; Siahrostami, S.; Bajdich, M. Prediction of Stable and Active (Oxy-hydro) Oxide Nanoislands on Noblemetal Supports for Electrochemical Oxygen Reduction Reaction. ACS Appl. Mater. Interfaces 2018, 11, 2006–2013. (24) Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G. Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation. APL Mater. 2013, 1, 011002. (25) Sen, F.; Kinaci, A.; Narayanan, B.; Gray, S.; Davis, M.; Sankaranarayanan, S.; Chan, M. Towards Accurate Prediction of Catalytic Activity in IrO2 Nanoclusters via First Principles-based Variable Charge Force Field. J. Mater. Chem. A 2015, 3, 18970– 18982. (26) Kresse, G.; Furthm¨ uller, J. Efficient Iterative Schemes for ab-initio Total-energy Calculations using a Plane-wave Basis Set. Phys. Rev. B 1996, 54, 11169. (27) Kresse, G.; Furthm¨ uller, J. Efficiency of ab-initio Total Energy Calculations for Metals and Semiconductors using a Plane-wave Basis Set. Comput. Mater. Sci. 1996, 6, 15–50. (28) Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 3865. (29) Blöchl, P. E. Projector Augmented-wave Method. Phys. Rev. B 1994, 50, 17953.

23


ACS Catalysis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(30) Gauthier, J. A.; Dickens, C. F.; Chen, L. D.; Doyle, A. D.; Nørskov, J. K. Solvation Effects for Oxygen Evolution Reaction Catalysis on IrO2 (110). J. Phys. Chem. C 2017, 121, 11455–11463. (31) Kelly, S.; Shi, X.; Back, S.; Vallez, L.; Park, S. Y.; Siahrostami, S.; Zheng, X.; Norskov, J. K. ZnO as an Active and Selective Catalyst for Electrochemical Water Oxidation to Hydrogen Peroxide. ACS Catal. 2019, 9, 4593–4599. (32) Bengtsson, L. Dipole Correction for Surface Supercell Calculations. Phys. Rev. B 1999, 59, 12301. (33) Mathew, K.; Sundararaman, R.; Letchworth-Weaver, K.; Arias, T.; Hennig, R. G. Implicit Solvation Model for Density-functional Study of Nanocrystal Surfaces and Reaction Pathways. J. Chem. Phys. 2014, 140, 084106. (34) Larsen, A. H.; Mortensen, J. J.; Blomqvist, J.; Castelli, I. E.; Christensen, R.; Duak, M.; Friis, J.; Groves, M. N.; Hammer, B.; Hargus, C. The Atomic Simulation Environment–a Python Library for Working with Atoms. J. Phys. Condens. Matter 2017, 29, 273002. (35) Dickens, C. F.; Nørskov, J. K. A Theoretical Investigation into the Role of Surface Defects for Oxygen Evolution on RuO2 . J. Phys. Chem. C 2017, 121, 18516–18524. (36) Sprowl, L. H.; Campbell, C. T.; Arnadottir, L. Hindered Translator and Hindered Rotor Models for Adsorbates: Partition Functions and Entropies. J. Phys. Chem. C 2016, 120, 9719–9731. (37) Henkelman, G.; Uberuaga, B. P.; Jónsson, H. A Climbing Image Nudged Elastic Band Method for Finding Saddle Points and Minimum Energy Paths. J. Chem. Phys. 2000, 113, 9901–9904. (38) Nørskov, J. K.; Rossmeisl, J.; Logadottir, A.; Lindqvist, L.; Kitchin, J. R.; Bligaard, T.;

24


Page 24 of 28

Page 25 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

Jonsson, H. Origin of the Overpotential for Oxygen Reduction at a Fuel-cell Cathode. J. Phys. Chem. B 2004, 108, 17886–17892. (39) Lide, D. R. CRC Handbook of Chemistry and Physics, 84th ed.; CRC press, 2004; Vol. 85; p 994. (40) Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. A.; Ceder, G. Python Materials Genomics (Pymatgen): A Robust, Open-source Python Library for Materials Analysis. Comput. Mater. Sci 2013, 68, 314–319. (41) Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018, 120, 145301. (42) Perron, H.; Domain, C.; Roques, J.; Drot, R.; Simoni, E.; Catalette, H. Optimisation of Accurate Rutile TiO2 (110),(100),(101) and (001) Surface Models from Periodic DFT Calculations. Theor. Chem. Acc. 2007, 117, 565–574. (43) Gao, J.; Xu, C.-Q.; Hung, S.-F.; Liu, W.; Cai, W.; Zeng, Z.; Jia, C.; Chen, H. M.; Xiao, H.; Li, J. Breaking Long-Range Order in Iridium Oxide by Alkali Ion for Efficient Water Oxidation. J. Am. Chem. Soc. 2019, 141, 3014–3023. (44) Stoerzinger, K. A.; Qiao, L.; Biegalski, M. D.; Shao-Horn, Y. Orientation-dependent Oxygen Evolution Activities of Rutile IrO2 and RuO2 . J. Phys. Chem. Lett 2014, 5, 1636–1641. (45) Singh, Y.; Manni, S.; Reuther, J.; Berlijn, T.; Thomale, R.; Ku, W.; Trebst, S.; Gegenwart, P. Relevance of the Heisenberg-Kitaev Model for the Honeycomb Lattice Iridates A2 IrO3 . Phys. Rev. Lett. 2012, 108, 127203. (46) Siegrist, T.; Chamberland, L. The Crystal Structure of BaIrO3 . J. of the Less Common Metals 1991, 170, 93–99. 25


ACS Catalysis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(47) Parrondo, J.; George, M.; Capuano, C.; Ayers, K. E.; Ramani, V. Pyrochlore Electrocatalysts for Efficient Alkaline Water Electrolysis. J. Mater. Chem. A 2015, 3, 10819– 10828. (48) Lebedev, D.; Povia, M.; Waltar, K.; Abdala, P. M.; Castelli, I. E.; Fabbri, E.; Blanco, M. V.; Fedorov, A.; Coperet, C.; Marzari, N. Highly Active and Stable Iridium Pyrochlores for Oxygen Evolution Reaction. Chem. Mater. 2017, 29, 5182–5191. (49) Zhao, W.-W.; Bothra, P.; Lu, Z.; Li, Y.; Mei, L.-P.; Liu, K.; Zhao, Z.; Chen, G.; Back, S.; Siahrostami, S.; Kulkarni, A.; Xu, J.-J.; Chen, H.-Y.; Nørskov, J.; Bajdich, M.; Cui, Y. Surface Evolution of CoS2 Electrocatalyst for Oxygen Reduction Reaction under Acidic and Alkaline conditions. (submitted). (50) Kreider, M.; Gallo, A.; Back, S.; Liu, Y.; Siahrostami, S.; Nordlund, D.; Sinclair, R.; Nørskov, J. K.; King, L. A.; Jaramillo, T. F. Precious Metal-Free Nickel Nitride Catalyst for Oxygen Reduction in Acid and Alkaline Electrolytes. ACS Appl. Mater. Interfaces (in press). (51) GarcaMota, M.; Vojvodic, A.; Metiu, H.; Man, I. C.; Su, H.; Rossmeisl, J.; Nørskov, J. K. Tailoring the Activity for Oxygen Evolution Electrocatalysis on Rutile TiO2 (110) by TransitionMetal Substitution. ChemCatChem 2011, 3, 1607–1611. (52) Henkelman, G.; Arnaldsson, A.; Jónsson, H. A Fast and Robust Algorithm for Bader Decomposition of Charge Density. Comput. Mater. Sci. 2006, 36, 354–360. (53) Guerrini, E.; Trasatti, S. Recent Developments in Understanding Factors of Electrocatalysis. Russ. J. Electrochem. 2006, 42, 1017–1025. (54) Montoya, J. H.; Seitz, L. C.; Chakthranont, P.; Vojvodic, A.; Jaramillo, T. F.; Nørskov, J. K. Materials for Solar Fuels and Chemicals. Nat. Mater. 2017, 16, 70– 81.

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Page 26 of 28

Page 27 of 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

(55) Back, S.; Jung, Y. Importance of Ligand Effects Breaking the Scaling Relation for Core—Shell Oxygen Reduction Catalysts. ChemCatChem 2017, 9, 3173–3179. (56) Chai, G.-L.; Qiu, K.; Qiao, M.; Titirici, M.-M.; Shang, C.; Guo, Z. Active Sites Engineering leads to Exceptional ORR and OER Bifunctionality in P, N Co-doped Graphene Frameworks. Energy & Environ. Sci. 2017, 10, 1186–1195. (57) Siahrostami, S.; Tsai, C.; Karamad, M.; Koitz, R.; Garc´ıa-Melchor, M.; Bajdich, M.; Vojvodic, A.; Abild-Pedersen, F.; Nørskov, J. K.; Studt, F. Two-dimensional Materials as Catalysts for Energy Conversion. Catal. Lett. 2016, 146, 1917–1921. (58) Tran, K.; Ulissi, Z. W. Active Learning across Intermetallics to Guide Discovery of Electrocatalysts for CO2 Reduction and H2 Evolution. Nat. Catal. 2018, 1, 696–703. (59) Noh, J.; Back, S.; Kim, J.; Jung, Y. Active Learning with non-ab Initio Input Features toward Efficient CO2 Reduction Catalysts. Chem. Sci. 2018, 9, 5152–5159. (60) Li, Z.; Omidvar, N.; Chin, W. S.; Robb, E.; Morris, A.; Achenie, L.; Xin, H. MachineLearning Energy Gaps of Porphyrins with Molecular Graph Representations. J. Phys. Chem. A 2018, 122, 4571–4578. (61) Ma, X.; Li, Z.; Achenie, L. E.; Xin, H. Machine-learning-augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. J. Phys. Chem. Lett. 2015, 6, 3528–3533.

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Graphical TOC Entry Automated DFT Analysis for OER Catalysis More active

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Toward a Design of Active Oxygen Evolution Catalysts: Insights from

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