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We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been…
Modern tools for modeling stellar evolution, such as MESA (Modules for Experiments in Stellar Astrophysics), offer state-of-the-art implementations of stellar theories. However, this parametric approach introduces many free parameters that…
We report results from the molecular dynamics simulations of a binary colloidal mixture subjected to an external potential barrier along one of the spatial directions at low volume fraction, {\phi} = 0.2. The variations in the asymmetry of…
Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments…
We propose an efficient computational methodology for predicting the synthesizability of high entropy oxides (HEOs) in a large space of possible candidate compounds. HEOs are a growing field with an enormous potential chemical composition…
Multi-principal element alloys (MPEAs) can potentially offer exceptional material properties, but their complex, costly manufacturing limits their scalability. Chemical complexity and complex manufacturing processes lead to the formation of…
The characterization of the formation mechanisms of amorphous solids is a large avenue for research, since understanding its non-Arrhenius behavior is challenging to overcome. In this context, we present one path toward modeling the…
The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and…
High-entropy alloys (HEAs) are a new class of materials which are being energetically studied around the world. HEAs are characterized by a multi-component alloy in which five or more elements randomly occupy a crystallographic site. The…
The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning…
Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational space and the limitations of existing methods. Here we…
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…
The recently proposed pair-interaction model is applied to study a series of refractory high entropy alloys. The results demonstrate the simplicity, robustness, and high accuracy of this model in predicting the configuration energies of…
We employ both the effective medium approximation (EMA) and Bloch theory to compare the dispersion properties of semiconductor hyperbolic metamaterials (SHMs) at mid-infrared frequencies and metallic hyperbolic metamaterials (MHMs) at…
In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission reactors, fusion devices, space applications, etc), design, prediction and control of advanced materials…
Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation…
The hydrogen trapping behaviour of metallic alloys is generally characterised using Thermal Desorption Spectroscopy (TDS). However, as an indirect method, extracting key parameters (trap binding energies and densities) remains a significant…
High-entropy alloys (HEAs) are materials that consist of equimolar or near-equimolar multiple principal components but tend to form single phases, which is a new research topic in the field of metallurgy, have attracted extensive attention…
A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The Discrete Element Method, DEM, was used to generate 92,378 sphere packings that covered many different…
In recent years, metal hydride research has become one of the driving forces of the high-pressure community, as it is believed to hold the key to superconductivity close to ambient temperature. While numerous novel metal hydride compounds…