Search

We have combined extensive density functional theory calculations with an evolutionary algorithm to investigate possible structural models for two-dimensional (2D) Pb films supported on the Al 13 Co 4 (100) quasicrystal approximant surface. The minimization of the total energy while maximizing the atomic density in the layer leads to 2D atomic arrangement with pentagonal motifs, reflecting the symmetry of the substrate. Our findings show that the 2D structure can be interpreted as a stable structure with 16 Pb atoms per surface cell in the film, in line with the measured coverage. This conclusion is also supported by the reasonable agreement between the experimental scanning tunneling microscopy images and those simulated using this structural model. Alternatively, a metastable 2D film made of 15 Pb atoms fits with the experimental observations. This study opens a route toward the prediction of supported complex 2D films. [ABSTRACT FROM AUTHOR]

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.