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High-entropy alloys (HEAs), as multi-component materials with high configurational entropy, have garnered significant attention as new biomaterials; still, their low yield stress and high elastic modulus need to be overcome for future…

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…

Materials Science · Physics 2026-04-23 Vickey Nandal , Vít Beneš , Pavel Baláž , Jiří Ryjáček , Karel Tesař

Given the growing significance of 2D materials in various optoelectronic applications, it is imperative to have simulation tools that can accurately and efficiently describe electron correlation effects in these systems. Here, we show that…

Strongly Correlated Electrons · Physics 2023-08-14 Oinam Romesh Meitei , Troy Van Voorhis

High-entropy alloys, which exist in the high-dimensional composition space, provide enormous unique opportunities for realizing unprecedented structural and functional properties. A fundamental challenge, however, lies in how to predict the…

Materials Science · Physics 2021-05-20 Jie Qi , Andrew M. Cheung , S. Joseph Poon

Developing ductile refractory BCC alloys has remained a challenge. The intrinsic ductility (D) of an alloy is the ratio of surface energy ($\gamma_s$) and unstable stacking fault energy ($\gamma_{usfe}$). Lowering the valence electron…

Materials Science · Physics 2023-01-03 Sufyan M. Shaikh , B. S. Murty , Satyesh K. Yadav

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 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

Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a…

High entropy alloys (HEAs) have emerged as a new class of multicomponent materials, which have potential for high temperature applications. Phase stability and creep deformation, two key selection criteria for high temperature materials,…

Materials Science · Physics 2017-07-14 M. Vaidya , K. G. Pradeep , B. S. Murty , G. Wilde , S. V. Divinski

Accurate phase equilibria are foundational to alloy design because they encode the underlying thermodynamics governing stability, transformations, and processing windows. However, while the CALculation of Phase Diagrams (CALPHAD) provides a…

Machine Learning · Computer Science 2026-04-21 Eunjeong Park , Amrita Basak

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)…

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the…

Materials Science · Physics 2020-11-03 Xianglin Liu , Jiaxin Zhang , Junqi Yin , Sirui Bi , Markus Eisenbach , Yang Wang

In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants,…

Materials Science · Physics 2022-10-17 Ivan Novikov , Olga Kovalyova , Alexander Shapeev , Max Hodapp

Refractory high-entropy alloys can function at temperatures exceeding those of nickel-based superalloys. Aluminum, as an alloying element, contributes multiple advantageous characteristics to various high-temperature alloys. The Aluminum…

Materials Science · Physics 2025-03-28 M. Sreenidhi Iyengar , M. K Anirudh , P. H. Anantha Desik , M. P. Phaniraj

In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed. The capabilities of the framework are demonstrated on an example case related to the discovery of precipitation strengthened…

Computational modeling of high entropy alloys (HEA) is challenging given the scalability issues of Density functional theory (DFT) and the non-availability of Interatomic potentials (IP) for molecular dynamics simulations (MD). This work…

Materials Science · Physics 2023-12-04 Gurjot Dhaliwal , Abu Anand , Prasanth B. Nair , Chandra Veer Singh

Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…

Chemical Physics · Physics 2022-09-16 Chenru Duan , Aditya Nandy , Gianmarco Terrones , David W. Kastner , Heather J. Kulik

High-entropy alloys (HEAs) based on tungsten (W) have emerged as promising candidates for plasma-facing components in future fusion reactors, owing to their excellent irradiation resistance. In this study, we construct an efficient…

Materials Science · Physics 2025-07-17 Jiahui Liu , Jesper Byggmastar , Zheyong Fan , Bing Bai , Ping Qian , Yanjing Su

Refractory multi-principal element alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanistic understanding of plastic deformation. Here, we perform…

Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials…

Machine Learning · Computer Science 2026-02-11 Cheng Zeng , Zulqarnain Khan , Nathan L. Post