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The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and…

Disordered Systems and Neural Networks · Physics 2018-03-29 Nongnuch Artrith , Alexander Urban , Gerbrand Ceder

We report a coherent layer-by-layer high-order harmonic generation (HHG) build-up in artificially stacked transition metal dichalcogenides (TMDC) crystals in their various stacking configurations. In the experiments, millimeter-sized single…

The changes in the electronic properties of the substitutionally disordered MgC(Ni_{1-x}T_{x})_{3} (T=Fe, Co or Cu) alloys are studied using the atomic sphere formulation of the Korringa-Kohn-Rostoker coherent-potential approximation method…

Materials Science · Physics 2007-05-23 P. Jiji Thomas Joseph , Prabhakar P. Singh

Stabilization of grain structure is important for nanocrystalline alloys, and grain boundary segregation is a common approach to restrict coarsening. Doping can alter grain boundary structure, with high temperature states such as amorphous…

Materials Science · Physics 2023-09-15 Tianjiao Lei , Esther C. Hessong , Daniel S. Gianola , Timothy J. Rupert

Method(s) that can reliably predict phase evolution across thermodynamic parameter space, especially in complex systems are of critical significance in academia as well as in the manufacturing industry. In the present work, phase stability…

Materials Science · Physics 2024-05-03 Palash Swarnakar , M. Ghosh , B. Mahato , Partha Sarathi De , Amritendu Roy

Motivated by interest in the elastic properties of high strength amorphous metals, we examine the elastic properties of select crystalline phases. Using first principles methods, we calculate elastic moduli in various chemical systems…

A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded…

Materials Science · Physics 2024-04-24 Dario Baratella , Omar Abou El Kheir , Marco Bernasconi

As an empirical tool in materials science and engineering, the iconic phase diagram owes its robustness and practicality to the topological characteristics rooted in the celebrated Gibbs phase law (F = C - P + 2). When crossing the phase…

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains…

Materials Science · Physics 2026-05-08 Zheng Yu , Jiayan Xu , Abhirup Patra , Sharan Shetty , Detlef Hohl , Roberto Car

Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanced materials. Unlike crystalline materials that can be described by unit cells…

Machine Learning · Computer Science 2026-05-01 Yan Lin , Jilin Hu , N. M. Anoop Krishnan , Morten M. Smedskjaer

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 are finding significant scientific interest due to their exotic microstructures and exceptional properties resulting thereof. These alloys have excellent corrosion resistance and may find broad range of applications from…

Applied Physics · Physics 2021-06-08 Aditya Ayyagari , Riyadh Salloom , Harpreet Singh Arora , Sundeep Mukherjee

Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder…

Materials Science · Physics 2026-03-24 Fanli Zhou , Hao Chen , Pengxiang Xu , Kai Yang , Zongrui Pei , Xianglin Liu

Elastocaloric cooling, which exploits the latent heat released and absorbed as stress-induced phase transformations are reversibly cycled in shape memory alloys, has recently emerged as a frontrunner in non-vapor-compression cooling…

Structural phase transitions often couple to magnetic and electronic degrees of freedom, enabling emergent phenomena in solids. In high-entropy oxides (HEOs), which typically stabilize in highly symmetric cubic phases, such transitions are…

The controlled design of martensitic transformation (MT) to achieve specific properties is crucial for the innovative application of shape memory alloys (SMAs) in advanced technologies. Herein, we explore and design the MT behaviors and the…

Materials Science · Physics 2021-05-14 Yuquan Zhu , Tao Xu , Qinghua Wei , Hongxin Yang , Takahiro Shimada , Takayuki Kitamura , Tong-Yi Zhang

Modern compound prediction methods can efficiently screen large numbers of crystal structure phases and direct the experimental search for new materials. One of the most challenging problems in alloy theory is the identification of stable…

Materials Science · Physics 2007-05-23 Aleksey N. Kolmogorov , Stefano Curtarolo

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…

In this work, we have designed obtained a novel multiferroic nanocomposite using the high-pressure torsion (HPT) method. The crystal structure, phase composition, morphology, ferromagnetic (FM) and ferroelectric (FE) properties of the…

The CoCrFeNi alloy is widely accepted as an exemplary stable base for high entropy alloys (HEAs). Although various investigations prove it to be stable solid solution, its phase stability is still suspicious. Here, we identified that the…

Materials Science · Physics 2016-06-14 Feng He , Zhijun Wang , Qingfeng Wu , Junjie Li , Jincheng Wang , C. T. Liu