Experimentally Validated Structures of Supported Metal Nanoclusters

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Surfaces, Interfaces, and Catalysis; Physical Properties of Nanomaterials and Materials 2

Experimentally Validated Structures of Supported Metal Nanoclusters on MoS Yongliang Shi, Boao Song, Reza Shahbazian-Yassar, Jin Zhao, and Wissam A. Saidi J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.8b01233 • Publication Date (Web): 16 May 2018 Downloaded from http://pubs.acs.org on May 16, 2018

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The Journal of Physical Chemistry Letters

Experimentally Validated Structures of Supported Metal Nanoclusters on MoS2 1

2

2

Yongliang Shi , Boao Song , Reza Shahbazian-Yassar , Jin Zhao

1,3,4

, and Wissam

5*

A. Saidi 1

ICQD/Hefei National Laboratory for Physical Sciences at Microscale, and Key

Laboratory of Strongly-Coupled Quantum Matter Physics, Chinese Academy of Sciences, and Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, China 2

Mechanical and Industrial Engineering Department, University of Illinois at Chicago,

Chicago, Illinois 60607, USA 3

Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA 15260,

United States 4

Synergetic Innovation Center of Quantum Information & Quantum Physics,

University of Science and Technology of China, Hefei, Anhui 230026, China 5

Department of Mechanical Engineering and Materials Science, University of

Pittsburgh, Pittsburgh, Pennsylvania 15261, United States

Corresponding Author

*[email protected]

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TOC GRAPHICS

Keywords: Two-dimensional Materials, in situ TEM, nucleation and growth, Genetic Algorithm, DFT, Classical forcefields


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Abstract In nanometer clusters (NCs) each atom counts. It is the specific arrangement of these atoms that determines the unique size-dependent functionalities of the NCs and hence their applications. Here we employ a self-consistent, combined theoretical and experimental approach to determine atom-by-atom the structures of supported Pt NCs on MoS2. The atomic structures are predicted using a genetic algorithm utilizing atomistic force fields and density functional theory, which are then validated using aberration-corrected scanning transmission electron microscopy. We find that relatively small clusters grow with (111) orientation such that Pt 110 is parallel to MoS2 [100], which is different from predictions based on lattice-match for thinfilms epitaxy. Other 4d and 5d transitions metals show similar behavior. The underpinning of this growth mode is the tendency of the NCs to maximize the metal-sulfur interactions rather than to minimize lattice strain.



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Noble metal nanometer clusters (NCs) play a pivotal role in different technologies such as nanoelectronics, energy storage/conversion, and catalysis.1-5 At the nanoscale limit, the clusters are not stable on their own, and are often supported on substrates to prevent coagulation. Several studies have shown that the properties of the NCs, whether supported or not, are dependent on the cluster size due to their unique morphological patterns and quantum confinement effects.6-9 For example, Au55 has shown a novel enhanced resistance to atomic oxygen when supported on a silicon substrate.8 Pt46 cluster on CdSe has the best catalytic H2 conversion compared to other clusters of different sizes.9 While the data is rich and diverse on the impact of cluster size, it is very challenging to determine the atomic structure of the NCs using either theoretical modeling or experimental imaging alone. This challenge, often referred to as the “nanostructure problem”, 10 precludes tailoring of the chemical activity of nanomaterials at the atomic level.

Structure prediction has been a long-standing challenge in atomistic simulations simply because the number of possible atomic arrangements is very large and increases exponentially with system size.11-12 Thus, finding the true equilibrium structure, as well as metastable low-energy isomers, requires accurate methods to compute the energy and an unbiased and efficient method to sample the large structural space. Methodologies implementing simulated annealing,13-15 genetic algorithms,16-17 basin hopping,18-19 particle swarm optimization,20-21 and deep neural networks22 in conjunction with density functional theory (DFT) calculations have had some success in finding equilibrium structures. However, these approaches, in addition to their 4 ACS Paragon Plus Environment

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extensive computational demands, employ open-ended searches that can only obtain putative energy minima, i.e., they do not guarantee convergence to the lowest global energy configurations. Therefore, an absolute need exists to validate the predictions of the modeling approach and to assess the fidelity of different structure-search methodologies.

Traditionally, structural determination using electron microscopy relies on transmission electron microscopy (TEM) or scanning tunneling microscopy (STM), which can provide images with fine details to extract atomic scale information.23-24 Because the electron microscopy images are not sufficiently straightforward to generate an atomistic model, DFT calculations are sometimes carried out to aid in the interpretation. Different from images obtained using traditional high-resolution transmission electron microscopy (HRTEM), the intensity obtained using a high-angle annular dark-field (HAADF) detector in STEM mode depends on the atomic number (Z) of the imaged sample.25 Previous studies have used these Z-contrast image intensity differences to determine the atom registry at the interface, e.g. as done for large gold nanoparticles on CeO226 and TiO227.

In contrast to periodic systems, the application of microscopy imaging to NCs is more challenging due to the rapid atomic migration as well as electron damage to the ultra-small structures.28 Thus, previous studies have focused on resolving the orientation and size of relatively large NCs and nanoparticles rather than their atomistic structure.23-24, 29 Further, because the NCs can display distinctly different atomic 5 ACS Paragon Plus Environment


structures that are close in energy, the use of DFT calculations to interpret microscopy images becomes more challenging compared to periodic systems. For example, recently the atomic structures of unsupported germanium NCs were obtained using a combined STM and DFT approach.30 However, in this study, the DFT simulations were carried out only for selected atomistic models based on experimental observations, which makes the approach less predictive considering that two-dimensional images are used to produce three-dimensional atomistic structures.

In this Letter, we apply a complex modeling paradigm that combines theory and experiment, in a self-consistent computational framework, to investigate supported metal NCs metal clusters on 2H-MoS2 substrate focusing mainly on Pt NCs. These systems have been recently the subject of many exciting applications;31-38 e.g., supported Pt NCs on MoS2 have shown better H2 conversion efficiency to commercial Pt electrodes while using 68% less Pt.35 The novelty of our study is in using an adaptive genetic algorithm (AGA)39 based on classical force fields and DFT calculations to systematically investigate a large set of NCs with more than few Pt atoms, and then to validate their atomic structure atom-by-atom using HAADF-STEM microscopy. Further, for a relatively large cluster with few hundred atoms, we determine the NCs structure based on a predictive classical model developed based on the symmetry of the interface atom registry of the smaller NCs. We find that the growth mode of the NCs is distinctly different from thinfilm epitaxy based on domain-match, which we show to be due to maximizing metal-sulfur interactions. These conclusions are found to apply to other 4d and 5d metals. Further, this shows that the equilibrium cluster size of the NCs 6 ACS Paragon Plus Environment

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inversely correlates with lattice mismatch with the substrate, which has been recently observed in Pt, Pd and Ag NCs supported on MoS2 (001).35

Figure 1 summarizes the binding energies of the lowest-energy structures with n≤20 obtained using AGA. In the AGA approach, we employ Embedded-Atom Method (EAM) 40 potential to perform the auxiliary classical potential calculation as implemented in Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code41, and density functional theory using Perdew-Burke-Ehrenzhof (PBE)42 exchange-correlation functional and projector augmented wave (PAW) pseudopotentials43-44 as implemented in VASP. The atomic structure of the NCs are shown in Figure S1 and partly in Figure 3. Comparing the results to those obtained before by using initial structures singled out from an ab initio molecular dynamics trajectory,45 we find that AGA predicts six different structures for Pt6 and Pt12 that are

< 0.14 eV/atom lower in energy. Further, it is noteworthy that despite the overall efficiency of AGA in sampling structure space, we find this approach to be less efficient for n≤5 compared to relaxing many different initial configurations and choosing the lowest-energy structure. This is not surprising because, for these smaller clusters, the potential low-energy structures can be easily enumerated based on symmetry.

All of the equilibrium Ptn NCs with 6≤n≤20 follow epitaxial growth with (111) orientation on (001) MoS2 (see Table S1). Further, the NCs are two layered with AB stacking that is consistent with face-centered cubic (FCC) atomic arrangements. 7 ACS Paragon Plus Environment


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Smaller clusters with n≤5 attach to MoS2 via Pt-Mo bonding, while the larger ones bond via Pt-S interactions. These bonding schemes are attributed to geometrical factors of the MoS2 lattice because the sulfur atoms are more extended to the vacuum side.45 NCs with n >13 are found to have predominantly (111)-oriented hexagonal facets, and few with (110) terminations. This can be rationalized because (111) surfaces followed by (110) and then (100) surface have lowest surface energies46, and hence are expected to be exposed in NCs, at least in the thermodynamic limit as stipulated by Wulff reconstruction.47 We have verified that Pt9 and Pt13 are the smallest NCs with closed-polyhedra structures. However, surprisingly, their energy is not particularly low compared to nearby structures, i.e. they do not belong to the magic clusters.48 For

Pt the closed-polyhedra structures are unfavorable in energy by 0.1-0.8 eV/atom compared to the most stable clusters (see Figure S2).

To validate the NC structures determined using the AGA approach, we carried out experiments focusing on resolving not only the orientation but also the atomic structure of the supported NCs. Pt is deposited on MoS2 in situ via wet-chemical synthesis from K2PtCl4 electroreduction. We then carried out atomic level HAADF-STEM imaging to characterize the Pt NCs and determine their orientation on the MoS2 substrate. Figure 2a shows a typical HAADF-STEM image of pristine MoS2 nanoflake in 〈001〉 zone axis. The brighter spots correspond to atomic columns with both Mo and S atoms, while the darker spots indicate atomic columns with Mo atoms only. Figure 2b shows Pt nanoclusters deposited on MoS2 (001) basal plane. The Pt atoms can be clearly distinguished due to the highest atomic number (Z) of Pt (Z=78) compared to that of 8 ACS Paragon Plus Environment

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Mo (Z=42) and S (Z=14). Further, as seen from the figure, the Pt clusters are well dispersed on MoS2 surface in a diameter range of ~1-5 nm. The intensity line profile along Pt deposited MoS2 is shown in Figure 2c. As can be observed, four atomic columns marked with arrows have a significantly higher intensity, indicating the presence of Pt atoms. This interpretation is also confirmed from energy dispersive spectrometer (EDS) line scans shown in Figure 2d and 2e. Hence, the location of these brighter spots compared to the spots of the underlying lattice identifies the positions of Pt atoms on the substrate. Further, Figure 2c shows that the Pt atoms are located atop sulfur even though Pt monomers are more stable atop Mo sites.45, 49 This will be discussed more below.

We have applied this approach to determine the Pt/MoS2 interface models. Figure 3a-e show the pseudo-color HAADF-STEM images of Pt clusters on MoS2 (first row), AGA-predicted Pt structures (second row), and the overlaid images between the two structures (third row) for 9, 13, 16, 20, and 38 Pt. Similar structures were observed from different locations (Figure S9). The analysis of the overlaid images shows that the mismatch along zigzag and armchair directions to be less than 10% for each of these structures. In the case of Pt20, an individual Pt atom is observed, as exemplary shown in circled area in Figure 3d, which is not consistent with the DFT model. However, this atom is likely to be an isolated monomer on the surface that is not bonded with the cluster, as can be judged from its large distance ~4.8 Å to the nearest Pt atom of Pt20. To further validate the atomic structure, we compare the experimental HAADF-STEM image (Figure 3f) with multislice image simulation (Figure 3g) for the case of Pt13. 9 ACS Paragon Plus Environment


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Box-average intensity profile along a zigzag and armchair direction of HAADF, and simulated image are shown in Figure 3h and 3i in yellow and pink, respectively. For example, the comparison between HAADF counts and simulation line profile along zigzag direction in Figure 3h indicates the two Pt atoms at top layer with the higher intensity among four bright spots. Similarly, Figure 3i shows two high intensity peaks that originate from Pt at top layer, one low intensity peak from Pt at bottom layer, and the lowest intensity indicates the column without Pt atom. The good agreement between experimental HAADF-STEM and simulated image further confirms the validity of the Pt structure obtained from DFT calculation.

In our experiments, we find that small NCs with size less than ~2 nm grow with (111) orientation on MoS2 (001), while as the large NCs follow (111) and (110) orientation on the substrate. This is consistent with Huang et al. who reported (111) and (110) growth modes for their large > 2 nm metal NCs on MoS2.35 Further, these findings are consistent with the AGA results, which show that ultra-small metal NCs (size less 1 nm) favor a growth along the (111) termination.

Inspecting the Pt/MoS2 interfaces, we find that all NCs attach to MoS2 via Pt-S bonds such that Pt 110 is parallel to MoS2 [100]. This configuration is surprising considering the large lattice misfit between the metal and MoS2 lattices as Pt-Pt = 2.79Å is 12% smaller than that of S-S = 3.16Å. As seen in Figure S1, the metal NCs partially alleviate this strain by forming tilted Pt-S bond towards the inner of the NC. To understand why this bonding scheme is favorable, we generate four different 10 ACS Paragon Plus Environment

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interface models with low misfit for Pt/MoS2 using the lattice-match approach of Zur and McGill.50 The models are shown in Figure 4; the metal layers in Figure 4(a) have the same orientation as the NCs while as the interface of Figure 4(b) is similar to a previous model.31, 37 These interfaces are denoted by 110 ∥ [100], 110 ∥ [120],

510 ∥ [100] and 110 ∥ [310] in accordance with their relative orientations with respect to MoS2 lattice. We find that the corresponding interface energies are respectively 0.14, 0.16, 0.12 and 0.16 eV/Å2, which implies that 510 ∥ [100] rather than 110 ∥ [100] is the equilibrium heterostructure in the thinfilm epitaxy limit. By comparing the atomic registry of the different interfaces, we find that 110 ∥ [100] bonding is favorable as this maximizes the interactions between Pt and S adopting almost atop adsorption configuration (see Figure S5, S6 and S7). Thus, it is expected that as the NCs grow in size, 110 ∥ [100] interface will become high in energy due to strain buildup, as was observed before for Au nanoparticles on TiO2.27 Further, these results demonstrate that the lattice-match approach50 has limitations in predicting the interfaces of supported NCs, at which quantum mechanical factors such as metal-substrate charge transfer, and bonding type and strength might be more dominant than lattice strain.

There are several challenges associated with the study of structural transition between different interface models as the NCs grow in size. Experimentally, for relatively thick NCs in the direction of electron beam pathway, it is difficult to spatially resolve both the supported cluster and substrate due to loss of contrast difference between edge of Pt cluster and MoS2. This would limit the ability to analyze their relationships. Further, on 11 ACS Paragon Plus Environment


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the modeling side, the AGA simulations also become challenging due to the large increase in computational needs with system size as well as the requirements to having more AGA generations to adequately sample the exponentially expanding structure manifold.

To address this limitation, we have developed an adhoc interface model that can determine the atomic structure of closed polyhedra on MoS2 for an arbitrary large number of atoms. From the experimentally validated NCs shown in Figure 3, we find that the supported NCs have the following characteristics: (1) The bottom layer follows 110 ∥ [100] growth with a hexagonal symmetry (Figure 4a) and has the largest number of Pt atoms; (2) The metal layers follow ABC FCC closed-pack stacking. Using these constraints, we can enumerate potential configurations of the bottom layer for a given cluster size, as illustrated in Figure S4. Subsequent layers are added to the NC following ABC FCC stacking by distinguishing the incenters of all triangles formed by Pt atoms in the layer below and classifying them to either belong to B or C layers. We have verified that the classical method can successfully predict all of the AGA structures for ≤ 20. Also, the predicted model for Pt38 agrees with experimental structure as can be seen from Figure 3e, although for this larger cluster two Pt atoms (marked with white arrows) were hard to detect experimentally.

Using the adhoc interface model, we have generated NCs with the different orientations of Figure 4 for ≤ 311. The binding energies of the optimized structures are shown in Figure 1. As seen from the figure, NCs with 110 ∥ [100] interfaces have the 12 ACS Paragon Plus Environment

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lowest binding energy. For ≤ 100, we find that most clusters converge to 110 ∥

[100] during the optimization regardless of the initial interface type. These results are consistent with the AGA findings. Further, by inspecting the energy of the 110 ∥

[100] and 510 ∥ [100] interfaces in Figure 1 (and Table S4), we see that the energy difference between the two interfaces decrease as the size of the NC increases. For example, this value is 0.11 eV/atom for = 82 and decreases to 0.03 eV/atom for

= 275. This suggests that for larger clusters, 110 ∥ [100] will be less favorable compared to the interface 510 ∥ [100], which is the most stable in the thinfilm limit. To show this more clearly, we examined a three-layer thick Pt325 NC that has a relatively large contact area with MoS2. As expected 510 ∥ [100] is found to be lower in energy than 110 ∥ [100] by 0.02 eV/atom. Although this cluster is not the equilibrium structure because of its small height/width ratio, it nevertheless illustrates that 110 ∥ [100] becomes unfavorable in the large cluster limit due to increased energy penalty associated with strain build up.

Our findings are general and apply to other 4d and 5d metals with similar FCC lattices. To verify this, we additionally investigated Cu, Ir, Pd, Ag and Au NCs. The first two (last three) have lattice constants smaller (larger) than Pt. In the pseudomorphic thinfilm limit, the favored interfaces are 510 ∥ [100] for Cu, Ir, Pt, and Pd,

110 ∥ [100] for Ag, and 110 ∥ [310] for Au (see Table S3). The lattice misfit between the metals and MoS2 decreases along the sequence Cu, Ir, Pt, Pd, Ag, and Au. This can be verified by noting that the metal-metal distance increases respectively as 2.73, 2.74, 2.75, 2.79, 2.89, and 2.95Å becoming closer to S-S bondlength of 3.18Å for 13 ACS Paragon Plus Environment


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Au. However, despite the different levels of strain-buildup, we find that metal NCs with ≲ 200 still favor 110 ∥ [100] similar to Pt case (Table S4). This is more noticable for Ir and Pd than Cu, Ag, and Au due to the stronger metal-MoS2 interactions for Ir and Pd.49 Recent experiments reported that supported Pt, Pd, and Ag clusters on MoS2(001) grow increasingly in the size of 1-3, ~5, and 30-60 nm.35 Our results are consistent with these experiments and support that strain buildup can hinder the growth of the nanoparticles, resulting in nanoparticles with sizes that inversely correlates with lattice mismatch.

In conclusion, we have applied a coordinated theoretical and experimental approach to provide precise atomistic information on the interfaces of supported metal clusters. The theoretical modeling is based on a multiscale approach that employs a genetic algorithm with atomistic force fields and DFT, and a predictive classical model using symmetry for arbitrary large clusters. The small NCs with ≤ 20 are validated atom-by-atom using STEM. We show that the domain-match approach, which is typically applied to determine epitaxial interface structures, is not viable for small clusters due to quantum effects regardless of the lattice mismatch. Also, our results suggest that there is a maximum size for the growth of the supported metal clusters on MoS2 due to strain buildup, which agrees with previous experimental results for Pt, Pd and Ag. The experimentally validated atom-by-atom NC structures can be further screened using atomistic simulations for different catalytic reactions. Further, the NC structures can serve as benchmarks to assess the fidelity of different computational methodologies. 14 ACS Paragon Plus Environment

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Supporting Information

Computational and experimental approaches; AGA force field form; Number of atoms in each layer for NCs; atomic structures of the NCs; energies of NCs in different interface models for Cu, Ir, Pd, Pt, Ag, and Au. Experimental images of Pt9, Pt13, Pt16, Pt20 at different locations, and images of Pt9 and Pt16 after two-time scans.

Acknowledgment W. A. Saidi acknowledges a start-up fund from the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. J. Z. acknowledges the support of the Ministry of Science and Technology of China, Grant No. 2016YFA0200604 and 2017YFA0204904; National Natural Science Foundation of China, Grant No. 11620101003, 21421063; the Fundamental Research Funds for the Central Universities WK3510000005; the support of US National Science Foundation, Grant No. CHE-1213189. R. Shahbazian-Yassar acknowledges financial support from the National Science Foundation (Award No. DMR-1620901). The TEM work made use of the JEOL JEM-ARM200CF and JEOL JEM-3010 in the Electron Microscopy Service (Research Resources Center, UIC). The acquisition of the UIC JEOL JEM-ARM200CF was supported by an MRI-R2 grant from the National Science Foundation (DMR-0959470). We are grateful for computing time provided in part by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation (#NSF OCI-1053575), Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under 15 ACS Paragon Plus Environment


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Contract DE-AC02-06CH11357, Environmental Molecular Sciences Laboratory at the PNNL, a user facility sponsored by the DOE Office of Biological and Environmental Research, and Supercomputing Center at University of Science and Technology of China. Declaring Financial Interests The authors declare no competing financial interests. References (1) Rase, H. F.; Handbook of Commercial Catalysts: Heterogeneous Catalysts. CRC Press: Boca Raton, 2000. (2) Aiken, J. D.; Finke, R. G.; A Review of Modern Transition-Metal Nanoclusters: Their Synthesis, Characterization, and Applications in Catalysis. J. Mol. Catal. A:

Chem. 1999, 145, 1-44. (3) Lewis, L. N.; Chemical Catalysis by Colloids and Clusters. Chem. Rev. 1993, 93, 2693-2730. (4) Somorjai, G. A.; Borodko, Y. G.; Research in Nanosciences - Great Opportunity for Catalysis Science. Catal. Lett. 2001, 76, 1-5. (5) Farmer, J. A.; Campbell, C. T.; Ceria Maintains Smaller Metal Catalyst Particles by Strong Metal-Support Bonding. Science 2010, 329, 933-6. (6) Sanchez, A.; Abbet, S.; Heiz, U.; W.-D.Schneider; Hakkinen, H.; Barnett, R. N.; Landman, U.; When Gold Is Not Noble: Nanoscale Gold Catalysts. J. Phys. Chem. A 1999, 103, 9573-9578.


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(7) Heiz, U.; Vanolli, F.; Sanchez, A.; Schneider, W. D.; Size-Dependent Molecular Dissociation on Mass-Selected, Supported Metal Clusters. J. Am. Chem. Soc. 1998, 120, 9668-9671. (8) Boyen, H. G.; Kastle, G.; Weigl, F.; Koslowski, B.; Dietrich, C.; Ziemann, P.; Spatz, J. P.; Riethmuller, S.; Hartmann, C.; Moller, M., et al.; Oxidation-Resistant Gold-55 Clusters. Science 2002, 297, 1533-6. (9) Schweinberger, F. F.; Berr, M. J.; Doblinger, M.; Wolff, C.; Sanwald, K. E.; Crampton, A. S.; Ridge, C. J.; Jackel, F.; Feldmann, J.; Tschurl, M., et al.; Cluster Size Effects in the Photocatalytic Hydrogen Evolution Reaction. J. Am. Chem. Soc. 2013,

135, 13262-5. (10) Billinge, S. J. L.; Levin, I.; The Problem with Determining Atomic Structure at the Nanoscale. Science 2007, 316, 561. (11) Maddox, J.; Crystals from First Principles. Nature 1988, 335, 201-201. (12) Zhai, H.; Alexandrova, A. N.; Fluxionality of Catalytic Clusters: When It Matters and How to Address It. Acs Catalysis 2017, 7, 1905-1911. (13) Doll, K.; Schon, J. C.; Jansen, M.; Global Exploration of the Energy Landscape of Solids on the Ab Initio Level. Phys. Chem. Chem. Phys. 2007, 9, 6128-33. (14) Wille, L. T.; Searching Potential-Energy Surfaces by Simulated Annealing. Nature 1986, 324, 46-47. (15) Kirkpatrick, S.; Gelatt, C. D., Jr.; Vecchi, M. P.; Optimization by Simulated Annealing. Science 1983, 220, 671-80.



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(16) Wu, S. Q.; Umemoto, K.; Ji, M.; Wang, C. Z.; Ho, K. M.; Wentzcovitch, R. M.; Identification of Post-Pyrite Phase Transitions in Sio2 by a Genetic Algorithm. Phys.

Rev. B 2011, 83, 184102. (17) Deaven, D. M.; Ho, K. M.; Molecular Geometry Optimization with a Genetic Algorithm. Phys. Rev. Lett. 1995, 75, 288-291. (18) Goedecker, S.; Minima Hopping: An Efficient Search Method for the Global Minimum of the Potential Energy Surface of Complex Molecular Systems. J. Chem.

Phys. 2004, 120, 9911-7. (19) Wales, D. J.; Doye, J. P. K.; Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms. J.

Phys. Chem. A 1997, 101, 5111-5116. (20) Wang, Y. C.; Lv, J.; Zhu, L.; Ma, Y. M.; Calypso: A Method for Crystal Structure Prediction. Comput. Phys. Commun. 2012, 183, 2063-2070. (21) Wang, Y. C.; Lv, J. A.; Zhu, L.; Ma, Y. M.; Crystal Structure Prediction Via Particle-Swarm Optimization. Phys. Rev. B 2010, 82, 094116. (22) Zhai, H.; Alexandrova, A. N.; Ensemble-Average Representation of Pt Clusters in Conditions of Catalysis Accessed through Gpu Accelerated Deep Neural Network Fitting Global Optimization. J. Chem. Theory Comput. 2016, 12, 6213-6226. (23) Henry, C. R.; Morphology of Supported Nanoparticles. Prog. Surf. Sci. 2005, 80, 92-116. (24) Tao, F. F.; Crozier, P. A.; Atomic-Scale Observations of Catalyst Structures under Reaction Conditions and During Catalysis. Chem. Rev. 2016, 116, 3487-539. 18 ACS Paragon Plus Environment

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(25) Pennycook, S. J.; Jesson, D. E.; High-Resolution Z-Contrast Imaging of Crystals.

Ultramicroscopy 1991, 37, 14-38. (26) Akita, T.; Tanaka, S.; Tanaka, K.; Haruta, M.; Kohyama, M.; Sequential Haadf-Stem Observation of Structural Changes in Au Nanoparticles Supported on Ceo2. J. Mater. Sci. 2011, 46, 4384-4391. (27) Shibata, N.; Goto, A.; Matsunaga, K.; Mizoguchi, T.; Findlay, S.; Yamamoto, T.; Ikuhara, Y.; Interface Structures of Gold Nanoparticles on Tio 2 (110). Phys. Rev. Lett. 2009, 102, 136105. (28) Li, Z. Y.; Young, N. P.; Di Vece, M.; Palomba, S.; Palmer, R. E.; Bleloch, A. L.; Curley, B. C.; Johnston, R. L.; Jiang, J.; Yuan, J.; Three-Dimensional Atomic-Scale Structure of Size-Selected Gold Nanoclusters. Nature 2007, 451, 46. (29) Deepak, F. L.; Anumol, E. A.; Li, J., Advanced Electron Microscopy Techniques toward the Understanding of Metal Nanoparticles and Clusters. In Metal Nanoparticles

and Clusters, Deepak, F. L., Ed. Springer International Publishing: Cham, 2018; pp 219-287. (30) Bals, S.; Van Aert, S.; Romero, C. P.; Lauwaet, K.; Van Bael, M. J.; Schoeters, B.; Partoens, B.; Yucelen, E.; Lievens, P.; Van Tendeloo, G.; Atomic Scale Dynamics of Ultrasmall Germanium Clusters. Nat. Commun. 2012, 3, 897. (31) Popov, I.; Seifert, G.; Tomanek, D.; Designing Electrical Contacts to MoS2 Monolayers: A Computational Study. Phys. Rev. Lett. 2012, 108, 156802. (32) Radisavljevic, B.; Radenovic, A.; Brivio, J.; Giacometti, V.; Kis, A.; Single-Layer MoS2 Transistors. Nature Nanotech. 2011, 6, 147-50. 19 ACS Paragon Plus Environment


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(33) Lee, E.; Kwon, Y. U.; Epitaxial Growth of Pd Nanoparticles on Molybdenum Disulfide by Sonochemistry and Its Effects on Electrocatalysis. Rsc Advances 2016, 6, 47468-47473. (34) Li, H.; Wang, S.; Sawada, H.; Han, G. G.; Samuels, T.; Allen, C. S.; Kirkland, A. I.; Grossman, J. C.; Warner, J. H.; Atomic Structure and Dynamics of Single Platinum Atom Interactions with Monolayer MoS2. ACS Nano 2017, 11, 3392-3403. (35) Huang, X.; Zeng, Z.; Bao, S.; Wang, M.; Qi, X.; Fan, Z.; Zhang, H.; Solution-Phase Epitaxial Growth of Noble Metal Nanostructures on Dispersible Single-Layer Molybdenum Disulfide Nanosheets. Nat. Commun. 2013, 4, 1444. (36) Kang, J. H.; Liu, W.; Sarkar, D.; Jena, D.; Banerjee, K.; Computational Study of Metal Contacts to Monolayer Transition-Metal Dichalcogenide Semiconductors. Phys.

Rev. X 2014, 4, 031005. (37) Saidi, W. A.; Influence of Strain and Metal Thickness on Metal-MoS2 Contacts. J.

Chem. Phys. 2014, 141, 094707. (38) Gong, C.; Huang, C.; Miller, J.; Cheng, L.; Hao, Y.; Cobden, D.; Kim, J.; Ruoff, R. S.; Wallace, R. M.; Cho, K., et al.; Metal Contacts on Physical Vapor Deposited Monolayer MoS2. ACS Nano 2013, 7, 11350-7. (39) Wu, S. Q.; Ji, M.; Wang, C. Z.; Nguyen, M. C.; Zhao, X.; Umemoto, K.; Wentzcovitch, R. M.; Ho, K. M.; An Adaptive Genetic Algorithm for Crystal Structure Prediction. J. Phys.: Condens. Matter 2014, 26, 035402. (40) Daw, M. S.; Baskes, M. I.; Embedded-Atom Method - Derivation and Application to Impurities, Surfaces, and Other Defects in Metals. Phys. Rev. B 1984, 29, 6443-6453. 20 ACS Paragon Plus Environment

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(41) Plimpton, S.; Fast Parallel Algorithms for Short-Range Molecular-Dynamics. J.

Comput. Phys. 1995, 117, 1-19. (42) Perdew, J. P.; Burke, K.; Ernzerhof, M.; Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 3865-3868. (43) Blochl, P. E.; Projector Augmented-Wave Method. Phys. Rev. B 1994, 50, 17953-17979. (44) Kresse, G.; Joubert, D.; From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method. Phys. Rev. B 1999, 59, 1758-1775. (45) Saidi, W. A.; Density Functional Theory Study of Nucleation and Growth of Pt Nanoparticles on MoS2(001) Surface. Cryst. Growth. Des. 2015, 15, 642-652. (46) Singh-Miller, N. E.; Marzari, N.; Surface Energies, Work Functions, and Surface Relaxations of Low-Index Metallic Surfaces from First Principles. Phys. Rev. B 2009,

80, 235407-235407. (47) Barmparis, G. D.; Lodziana, Z.; Lopez, N.; Remediakis, I. N.; Nanoparticle Shapes by Using Wulff Constructions and First-Principles Calculations. Beilstein J.

Nanotec. 2015, 6, 361-8. (48) Nayak, S. K.; Jena, P.; Stepanyuk, V. S.; Hergert, W.; Wildberger, K.; Magic Numbers in Supported Metal Clusters. Phys. Rev. B 1997, 56, 6952-6957. (49) Saidi, W. A.; Trends in the Adsorption and Growth Morphology of Metals on the MoS2(001) Surface. Cryst. Growth. Des. 2015, 15, 3190-3200. (50) Zur, A.; Mcgill, T. C.; Lattice Match - an Application to Heteroepitaxy. J. Appl.

Phys. 1984, 55, 378-386. 21 ACS Paragon Plus Environment


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Figure 1. NCs binding energy for different cluster sizes, n. The purple symbols indicate the values obtained using AGA. The data shown in blue, red, orange, and green are for the clusters generated using classical approach using the four binding interfaces shown in Figure 4; the solid lines are the corresponding values in the thinfilm limit



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Figure 2. Atomic resolution HAADF-STEM images of (a) pristine MoS2 nanoflake and (b) Pt clusters on MoS2 nanoflake. (c) Intensity profile along zigzag direction from blue-marked area in (b) showing the Pt atomic columns and for red-marked area in (b) indicating the S atomic columns. (d) EDS line scan pathway along several Pt clusters on MoS2. (e) Normalized EDS line scan profile corresponding to (d).


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Figure 3. Pseudo-color HAADF-STEM images of Pt clusters on MoS2 nanoflake, DFT calculated Pt structure and the overlaid images. Cluster configurations for (a) 9 Pt, (b) 13 Pt, (c) 16 Pt, (d) 20 Pt and (e) 38 Pt. All scale bars are 5 Å. In DFT configurations, S, Mo and Pt are shown as yellow, purple, and gray spheres. (f)(g) Comparison between HAADF-STEM image and simulated HAADF image of 13 Pt atoms. (h)(i) Comparison between HAADF and simulated box-average intensity profile along yellow and pink region in (f)(g).



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Figure 4. (a) 110 ∥ [100], (b) 110 ∥ [120], (c) 510 ∥ [100] and (d) 110 ∥ [310] interface models. The notation indicates metal/substrate orientation dependence, e.g. Pt110 is parallel to MoS2 [100] in (a). Pt (111) surface is parallel to MoS2 (001) for all models. The supercell is indicated by solid black lines.


Experimentally Validated Structures of Supported Metal Nanoclusters

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