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High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on…

Materials Science · Physics 2020-04-10 Liang Zhang , Kun Qian , Björn W. Schuller , Cheng Lu , Yasushi Shibuta , Xiaoxu Huang

High-entropy alloys (HEA) form solid solutions with large chemical disorder and excellent mechanical properties. We investigate the origin of HEA strengthening in face-centered cubic (FCC) single-phase HEAs through molecular dynamics…

Materials Science · Physics 2025-01-08 A. Esfandiarpour , S. Papanikolaou , M. Alava

In this work, molecular dynamics (MD) simulations were used to investigate elementary dislocation properties in a Co-free high entropy (HEA) model alloy ($Cr_{15}Fe_{46}Mn_{17}Ni_{22}$ at. %) in comparison with a model alloy representative…

Atomic and Molecular Clusters · Physics 2022-05-19 Ayobami Daramola , Anna Fraczkiewicz , Giovanni Bonny , Akiyoshi Nomoto , Gilles Adjanor , Christophe Domain , Ghiath Monnet

The influence of the physical mechanisms activated during deformation and annealing on the microstructure and texture evolution as well as on the mechanical properties in the equiatomic CoCrFeMnNi high-entropy alloy (HEA) were investigated.…

Materials Science · Physics 2022-12-26 Christian Haase , Luis A Barrales-Mora

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…

Materials Science · Physics 2022-09-28 Shunshun Liu , Kyungtae Lee , Prasanna V. Balachandran

High entropy alloys (HEAs) are promising magnetocaloric materials with tunable operating temperature conditions using compositional modifications. Here, we combine experiments and first principles based spin modelling to engineer…

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

Cobalt-free high-entropy alloys (HEAs) have garnered interest for nuclear structural applications due to their good mechanical performance, thermal stability, and resistance to radiation-induced degradation, while avoiding long-lived Co…

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these…

Materials Science · Physics 2022-09-08 Xianglin Liu , Jiaxin Zhang , Zongrui Pei

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development…

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

The development of oxidation-resistant high-entropy alloy (HEA) bond coats is restricted by the limited understanding of how multi-principal element interactions govern scale formation across temperatures. This study uncovers new oxidation…

Materials Science · Physics 2025-12-18 Dennis Boakye , Chuang Deng

High-entropy alloys (HEAs) comprise a compositionally complex class of materials that in certain cases exhibit outstanding mechanical properties. While substantial progress has been made in understanding their phase stability,…

Materials Science · Physics 2025-12-15 Mingwei Zhang , Uwe Glatzel , Martin Heilmaier , Easo P. George

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on…

Materials Science · Physics 2026-03-06 Dennis Boakye , Chuang Deng

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been…

Materials Science · Physics 2024-05-31 Shivam Dangwal , Yuji Ikeda , Blazej Grabowski , Kaveh Edalati

In this degree work, a theoretical computational study on high entropy alloys (HEAs) was carried out. Two specific alloys were chosen for study, AlCoCrFeNi and TiVZrNbHf. The study was based on the parameters deduced from the Hume-Rothery…

Materials Science · Physics 2023-12-19 Jhonn Royero

The concept of alloying multiple principal elements at high concentrations has led to the development of High Entropy Alloys (HEAs) with exceptional mechanical properties, making them the focus of major recent scientific endeavors.…

Materials Science · Physics 2022-11-28 Ishat Raihan Jamil , Ali Muhit Mustaquim , Mahmudul Islam , Mohammad Nasim Hasan

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…

Materials Science · Physics 2021-02-19 Yuanyuan Shang , Jamieson Brechtl , Claudio Psitidda , Peter K. Liaw

The present work reported a novel L12-strengthening NiCoCrAlTi high entropy alloy (HEA) with an outstanding synergy of tensile strength and ductility at both ambient and high temperatures. Transmission electron microscopy (TEM)…

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…

Materials Science · Physics 2022-07-13 Baldur Steingrimsson , Xuesong Fan , Rui Feng , Peter K. Liaw
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