Related papers: Metallic glasses for biodegradable implants

The development by machine learning of models predicting materials' properties usually requires the use of a large number of consistent data for training. However, quality experimental datasets are not always available or self-consistent.…

Fully bioresorbable polymer matrix composites have long been considered as potential orthopaedic implant materials, however their combination of mechanical strength, stiffness, ductility and bioresorbability is also attractive for cardiac…

While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic…

We present a comparative study of the glass forming ability of binary systems with varying composition, where the systems have similar global crystalline structure (CsCl+fcc). Biased Monte Carlo simulations using umbrella sampling technique…

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young's modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for…

We characterize the importance of metallicity on the presence of molecular hydrogen in damped Lyman-alpha (DLA) systems. We construct a representative sample of 18 DLA/sub-DLA systems with log N(HI)>19.5 at high redshift (zabs>1.8) with…

Here we show that a new class of glasses composed of heavy metal oxides involving transition metals (V$_\text {2}$O$_\text{}$5-TeO$_\text{2}$), can surprisingly be designed from very basic tools using topology and rigidity of their…

The design of multi-functional BMGs is limited by the lack of a quantitative understanding of the variables that control the glass-forming ability (GFA) of alloys. Both geometric frustration (e.g. differences in atomic radii) and energetic…

In interfaces between inorganic and biological materials relevant for technological applications, the general challenge of structure determination is exacerbated by the high flexibility of bioorganic components, chemical bonding, and charge…

This study proposes an Artificial Intelligence (AI) driven methodology for predicting a combination of brazed ceramic-metal composite materials. Multiple machine learning (ML) algorithms are compared with the deep learning (DL) model. The…

Glasses derive their functional properties from complex relaxation dynamics that remain enigmatic under extreme conditions. While the temperature dependence of these relaxation processes is well-established, their behavior under…

Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe-O, Mg-O, and Fe-Mg binary systems under pressure have been investigated, there are still very few studies of the Fe-Mg-O ternary system…

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors…

We have used density functional theory to study the structural stability of surface alloys. Our systems consist of a single pseudomorphic layer of $M_xN_{1-x}$ on the Ru(0001) surface, where $M$ = Fe or Co, and $N$ = Pt, Au, Ag, Cd, or Pb.…

A growing body of experimental work indicates that physical vapor deposition provides an effective route for preparation of stable glasses, whose properties correspond in some cases to those expected for glasses that have been aged for…

Structure and properties of magnesium silicate and borate melts and glasses were investigated by using newly parameterized interaction potentials in molecular dynamics simulations and compared with those of calcium silicate and borate. The…

Mechanical behaviors of bulk metallic glasses (BMGs) including heterogeneous and homogeneous deformation are interpreted by phenomenological shear transformation zones (STZs) model. Currently, information about STZs, i.e. size and density,…

The usefulness of glasses, and particularly of metallic glasses, in technological applications is often limited by their toughness, which is defined as the area under the stress vs. strain curve before plastic yielding. Recently toughness…

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…

Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and…