相关论文: Machine learning-based prediction of elastic prope…
The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a…
The elastic properties of the Zr$_{50}$Cu$_{40}$Ag$_{10}$ metallic alloy, such as the bulk modulus $B$, the shear modulus $G$, the Young's modulus $E$ and the Poisson's ratio $\sigma$, are investigated by molecular dynamics simulation in…
Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (T_m) and glass transition temperature (T_g)…
The mechanical properties of amorphous solids like metallic glasses can be dramatically changed by adding small concentrations (as low as 0.1\%) of foreign elements. The glass-forming-ability, the ductility, the yield stress and the elastic…
The yield stress is a defining feature of amorphous materials which is difficult to analyze theoretically, because it stems from the strongly non-linear response of an arrested solid to an applied deformation. Mode-coupling theory predicts…
Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use…
Properties of metals are fundamentally determined by their electron behavior, which is largely reflected by the electron work function ($\varphi $). Recent studies have demonstrated that many properties of metallic materials are directly…
The common notion suggests that metallic glasses (MGs) are a homogeneous solid at the macroscopic scale; however, recent experiments and simulations indicate that MGs contain nano-scale elastic heterogeneities. Despite the fundamental…
The shear-modulus and yield-stress of amorphous solids are important material parameters, with the former determining the rate of increase of stress under external strain and the latter being the stress value at which the material flows in…
Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of…
We study the effect of periodic, spatially uniform temperature variation on mechanical properties and structural relaxation of amorphous alloys using molecular dynamics simulations. The disordered material is modeled via a non-additive…
We statistically reconstruct a three-dimensional model of a tungsten-silver composite from an experimental two-dimensional image. The effective Young's modulus ($E$) of the model is computed in the temperature range 25-1060^o C using a…
The temperature dependence of mechanical properties of steel nanowires with varying carbon content was studied using molecular dynamics simulations. Four interatomic potentials were assessed, with the Modified Embedded Atom Method (MEAM)…
A solid - amorphous or crystalline - is defined by a finite shear modulus while a fluid lacks such. We thus experimentally investigate the elastic properties of a colloidal glass former near the glass transition: spectroscopy of vibrational…
Sheared multi-component bulk metallic glasses are characterized by both chemical and structural disorder that define their properties. We investigate the behavior of the local, microstructural elastic modulus across the plastic yielding…
Despite the critical importance of the elastic properties of modern materials, there is not a singular model that can predict the modulus to an accuracy needed for industrial glass design. To address this problem, we propose an approach to…
The yield of amorphous solids like metallic glasses under external stress was discussed asserting that it is related to the glass transition by increasing temperature, or that it can be understood using statistical theories of various…
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.…
Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a…
A new approach is theoretically proposed to study the glass transition of active pharmaceutical ingredients and a glass-forming anisotropic molecular liquid at high pressures. We describe amorphous materials as a fluid of hard spheres.…