Machine learning-guided exploration and experimental assessment of unreported compositions in the quaternary Ti-Zr-Cu-Pd biocompatible metallic glass system.

Due to their outstanding elastic limit, biocompatible Ti-based bulk metallic glasses (BMGs) are candidate materials to decrease the size of medical implants and therefore reduce their invasiveness. However, the practical use of classical Ti-BMGs in medical applications is in part hindered by their high copper content: more effort is thus required to design low-copper Ti-BMGs. In this work, in line with current rise in AI-driven tools, machine learning (ML) approaches, a neural-network ML model is used to explore the glass-forming ability (GFA) of unreported low-copper compositions within the biocompatible Ti-Zr-Cu-Pd system. Two types of models are trained and compared: one based on the alloy composition only, and a second based on various features derived from the alloying elements. Contrary to expectation, the predictive power of both models in evaluating GFA is similar. The compositional space identified by ML as promising is experimentally assessed, finding unfortunately low GFA. These results indicate that the ML approach may be premature for specific composition tuning of amorphous metallic materials. We emphasise that the development of ML tools in GFA prediction requires an improvement of the dataset, in terms of homogeneity, size and GFA descriptors, which must be supported by increased reporting of high-quality experimental GFA measurements, both positive and negative. STATEMENT OF SIGNIFICANCE: Biocompatible Ti-based bulk metallic glasses (BMGs) are candidate materials for use in the next generation of minimally invasive dental implants where improved mechanical properties, such as high strength are required. Despite promising in vitro/vivo evaluations, implementation of alloys for practical applications is partly hindered by the presence of copper as the main alloying element. Recent studies have presented AI-guided and machine learning strategies as appealing approaches to understand and describe the glass forming ability (GFA) of BMG-forming compositions. In this work, we employ and evaluate the capacity of a machine-learning model to explore low-copper compositional spaces in the biocompatible Ti-Zr-Cu-Pd system. Our results highlight the limits of such a computational approach and suggest improvements for future designing routes.

ZrCuAg Thin-Film Metallic Glasses: Toward Biostatic Durable Advanced Surfaces.

A combinatorial approach has served as a high-throughput strategy to identify compositional windows with optimized desired properties. Here, ZrCuAg thin-film metallic glasses were deposited by DC magnetron sputtering. For the purpose of using these coatings as biomedical surfaces, their durability in terms of mechanical and physicochemical properties as well as antibacterial properties were characterized. The effect of the chemical composition of thin films was studied. In particular, two key parameters were highlighted: the atomic ratio of Zr/Cu (with three values of 65/35, 50/50, and 35/65) and the silver content (from 1 to 16 at. %). All thin films are XRD amorphous and exhibit a typical veinlike pattern, which is characteristic of metallic glasses. They also show a dense and smooth surface and a hydrophobic behavior. Mechanical properties are found to be deeply influenced by the Zr/Cu ratio and the atomic structure. Although a low Zr/Cu ratio and/or a high silver content is detrimental to corrosion behavior, it favors the bactericidal effect of thin films. For all Zr/Cu ratios, ZrCuAg thin-film metallic glasses with silver contents higher than 12 at % are fully bactericidal. For lower silver contents, the bactericidal effect progressively decreases, which paves the way for a biostatic behavior of these surfaces.

Diffusion and segregation of niobium in fcc-nickel.

Niobium is one of the major alloying elements, among the refractory elements, contributing to the strengthening of superalloys. Consequently, data about its behavior and its migration mechanism in fcc-Ni are essential knowledge to understand and control the strengthening in such alloys. We present in this work Nb interactions, solubility and diffusion in Ni performed by using the GGA approximation of the density functional theory. The substituted site is found to be the most favorable configuration in comparison to the tetrahedral and octahedral sites. The effect of temperature on solubility is discussed taking into account the thermal expansion of the lattice parameter and the vibrational contribution. Its diffusion mechanism is also discussed and compared to the literature. We finally discuss the segregation of Nb atoms on a Σ(5)-(012) symmetric tilt grain boundary.