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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 Al5Fe2and Al13Fe4, 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 Al13Fe4(010)∥Al5Fe2(001) interface. The epitaxial relationships are found to be [130]Al5Fe2∥[010]Al13Fe4and [1 1̅0]Al5Fe2∥[100]Al13Fe4. 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 Al13Fe4and Al5Fe2phases at the Al∥Fe interface.

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 Al13Co4compound, 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-Al13Co4surface, 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/m2, i.e., much smaller than the ones of the pseudo-10-fold (1.49–1.68 J/m2) and pseudo-2-fold (1.66 J/m2) surfaces. Faceting is also a main factor at the origin of the Al13Co4catalytic 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 Al1+xV2Sn2–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 GaV2Sn2structure 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 Al1.19V2Sn1.81single-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.

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 nsites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al13Co4(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 nis 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.