Search

Intermetallic compounds are promising materials in numerous fields, especially those involving surface interactions, such as catalysis. A key factor to investigate their surface properties lies in adsorption energy maps, typically built using first-principles approaches. However, exploring the adsorption energy landscapes of intermetallic compounds can be cumbersome, usually requiring huge computational resources. In this work, we propose an efficient method to predict adsorption energies, based on a Machine Learning (ML) scheme fed by a few Density Functional Theory (DFT) estimates performed on n sites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al 13 Co 4 (100) quasicrystalline approximant surface for several atomic adsorbates (H, O, and Pb). On this specific example, our approach is shown to outperform both simple interpolation strategies and the recent ML force field MACE [arXiv.2206.07697], especially when the number n is small, i.e., below 36 sites. The ground-truth DFT adsorption energies are much more correlated with the predicted FPS-ML estimates (Pearson R-factor of 0.71, 0.73, and 0.90 for H, O and Pb, respectively, when n = 36) than with interpolation-based or MACE-ML ones (Pearson R-factors of 0.43, 0.39, and 0.56 for H, O, and Pb, in the former case and 0.22, 0.35, and 0.63 in the latter case). The unbiased root-mean-square error (ubRMSE) is lower for FPS-ML than for interpolation-based and MACE-ML predictions (0.15, 0.17, and 0.17 eV, respectively, for hydrogen and 0.17, 0.25, and 0.22 eV for lead), except for oxygen (0.55, 0.47, and 0.46 eV) due to large surface relaxations in this case. We believe that these findings and the corresponding methodology can be extended to a wide range of systems, which will motivate the discovery of novel functional materials.

Steel is the most commonly manufactured material in the world. Its performances can be improved by hot-dip coating with the low weight aluminum metal. The structure of the Al∥Fe interface, which is known to contain a buffer layer made of complex intermetallic compounds such as Al 5 Fe 2 and Al 13 Fe 4 , is crucial for the properties. On the basis of surface X-ray diffraction, combined with theoretical calculations, we derive in this work a consistent model at the atomic scale for the complex Al 13 Fe 4 (010)∥Al 5 Fe 2 (001) interface. The epitaxial relationships are found to be [130] Al 5 Fe 2 ∥[010] Al 13 Fe 4 and [1 1̅0] Al 5 Fe 2 ∥[100] Al 13 Fe 4 . Interfacial and constrained energies, as well as works of adhesion, calculated for several structural models based on density functional theory, identify the lattice mismatch and the interfacial chemical composition as main factors for the stability of the interface. Molecular dynamics simulations suggest a mechanism of Al diffusion to explain the formation of the complex Al 13 Fe 4 and Al 5 Fe 2 phases at the Al∥Fe interface.

The tunability offered by alloying different elements is useful to design catalysts with greater activity, selectivity, and stability than single metals. By comparing the Pd(111) and PdZn(111) model catalysts for CO 2 hydrogenation to methanol, we show that intermetallic alloying is a possible strategy to control the reaction pathway from the tuning of adsorbate binding energies. In comparison to Pd, the strong electron-donor character of PdZn weakens the adsorption of carbon-bound species and strengthens the binding of oxygen-bound species. As a consequence, the first step of CO 2 hydrogenation more likely leads to the formate intermediate on PdZn, while the carboxyl intermediate is preferentially formed on Pd. This results in the opening of a pathway from carbon dioxide to methanol on PdZn similar to that previously proposed on Cu. These findings rationalize the superiority of PdZn over Pd for CO 2 conversion into methanol and suggest guidance for designing more efficient catalysts by promoting the proper reaction intermediates.

A few low-order approximants to decagonal quasicrystals have been shown to provide excellent activity and selectivity for the hydrogenation of alkenes and alkynes. It is the case for the Al 13 Co 4 compound, for which the catalytic properties of the pseudo-2-fold orientation have been revealed to be among the best. A combination of surface science studies, including surface X-ray diffraction, and calculations based on density functional theory is used here to derive an atomistic model for the pseudo-2-fold o -Al 13 Co 4 surface, whose faceted and columnar structure is found very similar to the one of the 2-fold surface of the d -Al-Ni-Co quasicrystal. Facets substantially stabilize the system, with energies in the range 1.19-1.31 J/m 2 , i.e., much smaller than the ones of the pseudo-10-fold (1.49-1.68 J/m 2 ) and pseudo-2-fold (1.66 J/m 2 ) surfaces. Faceting is also a main factor at the origin of the Al 13 Co 4 catalytic performances, as illustrated by the comparison of the pseudo-10-fold, pseudo-2-fold and facet potential energy maps for hydrogen adsorption. This work gives insights toward the design of complex intermetallic catalysts through surface nanostructuration for optimized catalytic performances.

A new ternary phase with a composition Al 1+ x V 2 Sn 2- x ( x = 0.19) has been found during investigation of the Al-V-Sn ternary system. Single-crystal X-ray diffraction measurements reveal that this ternary phase crystallizes with an orthorhombic structure with a = 5.5931(1) Å, b = 18.8017(5) Å, and c = 6.7005(2) Å (space group Cmce ). This compound is thus isostructural to the GaV 2 Sn 2 structure type, showing a layered structure composed of vanadium cluster bands formed with pentagonal faces intercalated by Sn atom layers. High-resolution transmission electron microscopy measurements confirm the orthorhombic structure. Regarding lattice perfection, no dislocation could be identified within the probed Al 1.19 V 2 Sn 1.81 single-crystal lamella. Ab initio calculations reveal a reduction of the density of states at the Fermi level, which could be attributed to both a Hume-Rothery effect combined with strong spd hybridization.

Self-organized molecular films with long-range quasiperiodic order have been grown by using the complex potential energy landscape of quasicrystalline surfaces as templates. The long-range order arises from a specific subset of quasilattice sites acting as preferred adsorption sites for the molecules, thus enforcing a quasiperiodic structure in the film. These adsorption sites exhibit a local 5-fold symmetry resulting from the cut by the surface plane through the cluster units identified in the bulk solid. Symmetry matching between the C60 fullerene and the substrate leads to a preferred adsorption configuration of the molecules with a pentagonal face down, a feature unique to quasicrystalline surfaces, enabling efficient chemical bonding at the molecule-substrate interface. This finding offers opportunities to investigate the physical properties of model 2D quasiperiodic systems, as the molecules can be functionalized to yield architectures with tailor-made properties.