English
Related papers

Related papers: Electronic manifolds for extrapolative alloy disco…

200 papers

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred,…

Refractory multi-principal element alloys (RMPEAs) represent a novel class of alloys characterized by an extensive compositional design space and the potential for exceptional mechanical performance under extreme conditions. While accurate…

Materials Science · Physics 2026-04-21 A. K. Shargh , C. D. Stiles , J. A. El-Awady

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response.…

Materials Science · Physics 2024-09-26 Anurag Bajpai , Ziyuan Rao , Abhinav Dixit , Krishanu Biswas , Dierk Raabe

High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as…

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model…

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify…

Materials Science · Physics 2019-08-13 Jiaxin Zhang , Xianglin Liu , Sirui Bi , Junqi Yin , Guannan Zhang , Markus Eisenbach

High entropy alloys (HEA) represent a class of materials with promising properties, such as high strength and ductility, radiation damage tolerance, etc. At the same time, a combinatorially large variety of compositions and a complex…

Materials Science · Physics 2025-10-03 Franco Moitzi , Lorenz Romaner , Andrei V. Ruban , Oleg E. Peil

High entropy alloys (HEA) show promise as a new type of high-performance structural material. Their vast degrees of freedom provide for extensive opportunities to design alloys with tailored properties. However, the compositional…

Disordered Systems and Neural Networks · Physics 2019-04-19 Qi Jie , Andrew Cheung , S. Joseph Poon

Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors, including aerospace, automotive, and defense industries. However, the scarcity of integrated chemistry, process, structure, and property data…

Refractory high-entropy alloys (RHEAs) are a promising class of alloys that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. However, exploring the vast RHEA…

Materials Science · Physics 2021-12-07 Stephen A. Giles , Debasis Sengupta , Scott R. Broderick , Krishna Rajan

Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the…

Quantum Physics · Physics 2025-01-28 Tomoya Shiota , Kenji Ishihara , Wataru Mizukami

The amount of electronic waste (e-waste) recycled worldwide is less than 20% of the total amount produced. In a world where the need for critical and strategic metals is increasing almost exponentially, it is unacceptable that tons of these…

Materials Science · Physics 2023-11-20 Jose M. Torralba , Diego Iriarte , Damien Tourret , Alberto Meza

Energy efficiency is motivating the search for new high-temperature metals. Some new body-centered-cubic random multicomponent "high entropy alloys (HEAs)" based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional…

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy…

Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a…

Materials Science · Physics 2023-04-28 Stephen A. Giles , Hugh Shortt , Peter K. Liaw , Debasis Sengupta

Although electronic density of states (DOS) is fundamental to materials properties, its general relationship to mechanical properties of alloys is not well established. In this paper, using density functional theory (DFT) calculations, we…

Materials Science · Physics 2025-04-02 Dharmendra Pant , Dilpuneet S. Aidhy

This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited…

Computational Engineering, Finance, and Science · Computer Science 2024-11-08 Spyros Kamnis , Konstantinos Delibasis

Refractory high-entropy alloys (RHEAs) are compositionally complex materials which have been demonstrated to have the potential for exceptional strength at high operating temperatures. However, their composition space is vast, and other…

Materials Science · Physics 2025-11-21 Stephen A. Giles , Hugh Shortt , Peter K. Liaw , Debasis Sengupta

Single-phase body-centered cubic (BCC) refractory multi-principal element alloys (RMPEAs) offer potential for developing alloys with exceptional strength. However, the compositional design space is immense. Exhaustively mapping this space…

Materials Science · Physics 2025-08-22 A. K. Shargh , C. D. Stiles , J. A. El-Awady

The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effective Hamiltonian requires sufficiently large data sets, which…

Materials Science · Physics 2020-01-01 Xianglin Liu , Jiaxin Zhang , Markus Eisenbach , Yang Wang
‹ Prev 1 2 3 10 Next ›