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Register a new userA Generalizable Machine-learning Potential of Ag-Au Nanoalloys and its Application on Surface Reconstruction, Segregation and Diffusion
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Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To achieve high accuracy in molecular simulations of the Ag-Au nanoalloys, the surface properties are required to be modeled with first-principles precision. In this work, we propose a generalizable machine-learning interatomic potential for the Ag-Au nanoalloys based on deep neural networks, trained from a database constructed with the first-principle calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au towards the Ag-Au nanoalloy surface, where the empirical force field failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the empirical force fields. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database, therefore, the reported potential is expected to have a strong ability of generalization to a wide range of properties and to play a key role in the investigation of nanostructured Ag-Au evolution, where the accurate descriptions of free surfaces are necessary.
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Part of developing new strategies for fabrications of nanowire structures involves in many cases the aid of metal nanoparticles (NPs). It is highly beneficial if one can define both diameter and position of the initial NPs and make well-defined nanowire arrays. This sets additional requirement on the NPs with respect to being able to withstand a pre-growth annealing process (i.e. de- oxidation of the III-V semiconductor surface) in an epitaxy system. Recently, it has been demonstrated that Ag may be an alternative to using Au NPs as seeds for particle-seeded nanowire fabrication. This work brings light onto the effect of annealing of Au, Ag and Au-Ag alloy NP arrays in two commonly used epitaxial systems, the Molecular Beam Epitaxy (MBE) and the Metalorganic Vapor Phase Epitaxy (MOVPE). The NP arrays are fabricated with the aid of Electron Beam Lithography on GaAs 100 and 111B wafers and the evolution of the NPs with respect to shape, size and position on the surfaces are studied after annealing using Scanning Electron Microscopy (SEM). We find that while the Au NP arrays are found to be stable when annealed up to 600 $^{circ}$C in a MOVPE system, a diameter and pitch dependent splitting of the particles are seen for annealing in a MBE system. The Ag NP arrays are less stable, with smaller diameters ($leq$ 50 nm) dissolving during annealing in both epitaxial systems. In general, the mobility of the NPs is observed to differ between the two the GaAs 100 and 111B surfaces. While the initial pattern is found be intact on the GaAs 111B surface for a particular annealing process and particle type, the increased mobility of the NP on the 100 may influence the initial pre-defined positions at higher annealing temperatures. The effect of annealing on Au-Ag alloy NP arrays suggests that these NP can withstand necessary annealing conditions for a complete de-oxidation of GaAs surfaces.
While it is known that alloy components can segregate to grain boundaries (GBs), and that the atomic mobility in GBs greatly exceeds the atomic mobility in the lattice, little is known about the effect of GB segregation on GB diffusion. Atomistic computer simulations offer a means of gaining insights into the segregation-diffusion relationship by computing the GB diffusion coefficients of the alloy components as a function of their segregated amounts. In such simulations, thermodynamically equilibrium GB segregation is prepared by a semi-grand canonical Monte Carlo method, followed by calculation of the diffusion coefficients of all alloy components by molecular dynamics. As a demonstration, the proposed methodology is applied to a GB is the Cu-Ag system. The GB diffusivities obtained exhibit non-trivial composition dependencies that can be explained by site blocking, site competition, and the onset of GB disordering due to the premelting effect.
The classic metallurgical systems -- noble metal alloys -- that have formed the benchmark for various alloy theories, are revisited. First-principles fully relaxed general potential LAPW total energies of a few ordered structures are used as input to a mixed-space cluster expansion calculation to study the phase stability, thermodynamic properties and bond lengths in Cu-Au, Ag-Au, Cu-Ag and Ni-Au alloys. (i) Our theoretical calculations correctly reproduce the tendencies of Ag-Au and Cu-Au to form compounds and Ni-Au and Cu-Ag to phase separate at T=0 K. (ii) Of all possible structures, Cu/sub 3/Au (L1/sub 2/) and CuAu (L1/sub 0/) are found to be the most stable low-temperature phases of Cu/sub 1-x/Au/sub x/ with transition temperatures of 530 K and 660 K, respectively, compared to the experimental values 663 K and 670 K. The significant improvement over previous first-principles studies is attributed to the more accurate treatment of atomic relaxations in the present work. (iii) LAPW formation enthalpies demonstrate that L1/sub 2/, the commonly assumed stable phase of CuAu/sub 3/, is not the ground state for Au-rich alloys, but rather that ordered <100> superlattices are stabilized. (iv) We extract the non-configurational (e.g., vibrational) entropies of formation and obtain large values for the size mismatched systems: 0.48 k/sub B//atom in Ni/sub 0.5/Au/sub 0.5/ (T=1100 K), 0.37 k/sub B//atom in Cu/sub 0.14/Ag/sub 0.86/ (T=1052 K), and 0.16 k/sub B//atom in Cu/sub 0.5/Au/sub 0.5/ (T=800 K). (v) Using 8 atom/cell special quasirandom structures we study the bond lengths in disordered Cu-Au and Ni-Au alloys and obtain good qualitative agreement with recent EXAFS measurements.
Atomically engineered oxide multilayers and superlattices display unique properties responsive to the electronic and atomic structures of the interfaces. We have followed the growth of ferroelectric BaTiO3 on SrRuO3 electrode with in situ atomic scale analysis of the surface structure at each stage. An oxygen-induced surface reconstruction of SrRuO3 leads to formation of SrO rows spaced at twice the bulk periodicity. This reconstruction modifies the structure of the first BaTiO3 layers grown subsequently, including intermixing observed with cross-section spectroscopy. These observations reveal that this common oxide interface is much more interesting than previously reported, and provide a paradigm for oxygen engineering of oxide structure at an interface.
Biomass compounds adsorbed on surfaces are challenging to study due to the large number of possible species and adsorption geometries. In this work, possible intermediates of erythrose, glyceraldehyde, glycerol and propionic acid are studied on the Rh(111) surface. The intermediates and elementary reactions are generated from first 2 recursions of a recursive bond-breaking algorithm. These structures are used as the input of an unsupervised Mol2Vec algorithm to generate vector descriptors. A data-driven scheme to classify the reactions is developed and adsorption energies are predicted. The lowest mean absolute error (MAE) of our prediction on adsorption energies is 0.39 eV, and the relative ordering of different surface adsorption geometries is relatively accurate. We show that combining geometries from density functional tight-binding (DFTB) calculations with energies from machine-learning predictions provides a novel workflow for rapidly assessing the stability of various molecular geometries on the Rh(111) surface.
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