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The successful synthesis of high entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, electronics, structural alloys, and energetic…
Recent experiments demonstrate a "robust superconductivity phenomenon" in niobium-based alloys, where the superconducting state remains intact and the critical temperature ($T_c$) is largely unaffected by external pressure well above tens…
In this communication,we propose a sustainable way to produce high entropy alloys from alloy scraps called Alloy mixing.We successfully demonstrate this method using a near equimolar CrCuFeMnNi HEA. Alloy scraps (304L stainless steel,…
The "high-entropy" paradigm has been applied to a central challenge in materials science, the design of new functional materials with enhanced performance for targeted applications, with some notable successes over the last twenty years.…
High-entropy alloys (HEAs) are exceptional candidates for radiation-resistant materials due to their complex local chemical environment and slow defect migration. Despite commonly overlooked, electronic effects on defects evolution in…
In this study Mo-Nb-Ta-W refractory high-entropy alloys (R-HEAs) have been studied for their phase stability for a wide temperature range (100 K to 2000 K). The equilibrium thermodynamic phases are determined by the changes in enthalpy and…
Accurately determining the crystallographic structure of a material, organic or inorganic, is a critical primary step in material development and analysis. The most common practices involve analysis of diffraction patterns produced in…
Behavioral patterns captured in embeddings learned from interaction data are pivotal across various stages of production recommender systems. However, in the initial retrieval stage, practitioners face an inherent tradeoff between embedding…
An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened…
Electron density is a fundamental quantity, which can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a…
The vastness of the space of possible multicomponent metal alloys is hoped to provide improved structural materials but also challenges traditional, low-throughput materials design efforts. Computational screening could narrow this search…
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…
The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational…
Recent theoretical and practical research has focused on multi-component High Entropy Alloys (HEAs), which have superior mechanical and functional properties than standard alloys based on a single major element, thereby establishing a new…
High-entropy alloys (HEAs) stand out between multi-component alloys due to their attractive microstructures and mechanical properties. In this investigation, molecular dynamics (MD) simulation and machine learning were used to ascertain the…
The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high…
A novel phenomenological framework for an efficient estimation of the thermo-electric properties at room temperature and elevated temperatures of body-centered cubic (BCC) transition metal concentrated alloys is proposed in this work. The…
Compositionally complex alloy systems containing more than five principal elements allow exploring a wide range of compositions, processing, and structural variables with the hope for identifying unique properties. Such opportunities also…
The discovery of new materials is often constrained by the need for large labelled datasets or expensive simulations. In this study, we explore the use of Disentangling Autoencoders (DAEs) to learn compact and interpretable representations…
Understanding the strengthening and deformation mechanisms in refractory high-entropy alloys (HEAs), proposed as new high-temperature material, is required for improving their typically insufficient room-temperature ductility. Here,…