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Molecular dynamics (MD) simulations are widely used to provide insights into fracture mechanisms while maintaining chemical specificity. However, particle-based techniques such as MD are limited in terms of accessible length scales and…

Materials Science · Physics 2025-10-14 Felix Weber , Maxime Vassaux , Lukas Laubert , Sebastian Pfaller

A fascinating feature of metallic glasses is their ability to explore different configurations under mechanical deformations. This effect is usually observed through macroscopic observables, while little is known on the consequence of the…

Disordered Systems and Neural Networks · Physics 2023-01-09 A. Cornet , G. Garbarino , F. Zontone , Y. Chushkin , J. Jacobs , E. Pineda , T. Deschamps , S. Li , A. Ronca , J. Shen , G. Morard , N. Neuber , M. Frey , R. Busch , I. Gallino , M. Mezouar , G. Vaughan , B. Ruta

We explore the structural signatures of excitations in amorphous materials with the atomic cluster expansion (ACE), a universal and complete linear basis of descriptors of the atomic environment. Body-orderd linear classifiers are…

Disordered Systems and Neural Networks · Physics 2024-10-07 Joerg Rottler , Christoph Ortner

The fracture energy of brittle materials rises with crack velocity, and this effect is typically attributed to surface roughening from path instabilities. Here we show, using molecular dynamics simulations of silica glass with a…

We develop a generic strategy and simple numerical models for multi-component metallic glasses for which the swap Monte Carlo algorithm can produce highly stable equilibrium configurations equivalent to experimental systems cooled more than…

Statistical Mechanics · Physics 2020-10-09 Anshul D. S. Parmar , Misaki Ozawa , Ludovic Berthier

Oxide glasses have proven to be useful across a wide range of technological applications. Nevertheless, their medium-range structure has remained elusive. Previous studies focused on the ring statistics as a metric for the medium-range…

Disordered Systems and Neural Networks · Physics 2025-11-24 Achraf Atila , Yasser Bakhouch , Zhuocheng Xie

Atomistic simulations have become a powerful tool in materials research due to the extremely fine spatial and temporal resolution provided by such techniques. In order to understand the fundamental principles which govern material behavior…

Materials Science · Physics 2014-08-26 Jason F. Panzarino , Timothy J. Rupert

When a liquid is cooled well below its melting temperature at a rate that exceeds the critical cooling rate $R_c$, the crystalline state is bypassed and an amorphous glassy state forms instead. $R_c$ (or the corresponding critical casting…

Materials Science · Physics 2015-11-11 Kai Zhang , Meng Fan , Yanhui Liu , Jan Schroers , Mark D. Shattuck , Corey S. O'Hern

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of…

Fracture toughness is a critical mechanical property of glasses, but a detailed understanding of its link to composition and structure is still missing. Here, focusing on the industrially important family of calcium aluminosilicate glasses,…

Enhancing the kinetic stability of glasses often necessitates deepening thermodynamic stability, which typically compromises ductility due to increased structural rigidity. Decoupling these properties remains a critical challenge for…

Materials Science · Physics 2025-12-24 Huanrong Liu , Qingan Li , Shan Zhang , Rui Su , Yunjiang Wang , Pengfei Guan

Oxide glasses are made of a network of glass former polyhedra, and modifiers which have a role in neutralizing the charge of the glass former polyhedra or depolymerize the glass network. The effect of the modifier content on the structure…

Materials Science · Physics 2020-07-21 Achraf Atila

Topological constraint theory has become an increasingly popular tool to predict the compositional dependence of glass properties or pinpoint promising compositions with tailored functionalities. This approach reduces complex disordered…

Disordered Systems and Neural Networks · Physics 2020-06-16 Mathieu Bauchy

In the framework of topological constraint theory, network glasses are classified as flexible, stressed--rigid, or isostatic if the number of atomic constraints is smaller, larger, or equal to the number of atomic degrees of freedom. Here,…

Materials Science · Physics 2018-12-13 Ernest Ching , Mathieu Bauchy

The structural motifs of a Zr$_{50}$Cu$_{45}$Al$_{5}$ metallic glass were learned from atomistic models using a new structure analysis method called motif extraction that employs point-pattern matching and machine learning clustering…

Materials Science · Physics 2019-07-19 Jason J. Maldonis , Arash Dehghan Banadaki , Srikanth Patala , Paul M. Voyles

There exists a variety of theories of the glass transition and many more numerical models. But because the models need built-in complexity to prevent crystallization, comparisons with theory can be difficult. We study the dynamics of a…

Statistical Mechanics · Physics 2010-04-16 P. Charbonneau , A. Ikeda , J. A. van Meel , K. Miyazaki

Density is one of the most commonly measured or estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science and sustainable cements.…

Materials Science · Physics 2022-09-07 Kai Gong , Elsa Olivetti

Molecular dynamics simulations were performed for five monatomic metallic liquids and the atomic structural characteristics and dynamical properties were systematically investigated and compared for understanding the underlying structural…

Disordered Systems and Neural Networks · Physics 2022-04-06 X. Qin , J. Q. Wu , M. Z. Li

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual…

Materials Science · Physics 2022-04-05 Heejung Chung , Rodrigo Freitas , Gowoon Cheon , Evan J. Reed

Recently, the machine learning force field has emerged as a powerful atomic simulation approach for its high accuracy and low computational cost. However, its applications in the multi-component materials are relatively less. In this study,…

Materials Science · Physics 2018-07-06 Wenwen Li , Yasunobu Ando