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Related papers: Atomistic Models of Amorphous Metal-Organic Framew…

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Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in…

Materials Science · Physics 2025-04-08 Pieter Dobbelaere , Sander Vandenhaute , Veronique Van Speybroeck

High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning…

Recent years have seen a large increase in the number of reported framework materials, including the nowadays-ubiquitous metal-organic frameworks (MOFs). Many of these materials show flexibility and stimuli-responsiveness, i.e. their…

Materials Science · Physics 2019-07-11 François-Xavier Coudert

Zn(imidazolate)$_2$ metal-organic frameworks (MOFs) exhibit a remarkable degree of polymorphism. Because of their promising industrial applications, many research groups have investigated phase transitions, phase diagram and relative…

Materials Science · Physics 2026-05-01 Emilio Méndez , Rocio Semino

The atomic-level structure of bulk metallic glasses is a key determinant of their properties. An accurate representation of amorphous systems in computational studies has traditionally required large supercells that are unfortunately…

Materials Science · Physics 2024-01-17 Siya Zhu , Jan Schroers , Stefano Curtarolo , Hagen Eckert , Axel van de Walle

By virtue of their open network structures and low densities, metal--organic frameworks (MOFs) are soft materials that exhibit elastic instabilities at low applied stresses. The conventional strategy for improving elastic stability is to…

We investigate the quality of structural models generated by the Reverse Monte Carlo (RMC) method in a typical application to amorphous systems. To this end we calculate surrogate diffraction data from a Li2O-SiO2 molecular dynamics (MD)…

Materials Science · Physics 2009-09-08 Christian R. Mueller , Vindu Kathriarachchi , Michael Schuch , Philipp Maass , Valeri Petkov

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space.…

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

Metal-organic frameworks show both fundamental interest and great promise for applications in adsorption-based technologies, such as the separation and storage of gases. The flexibility and complexity of the molecular scaffold poses a…

Topological phases of matter are often understood and predicted with the help of crystal symmetries, although they don't rely on them to exist. In this chapter we review how topological phases have been recently shown to emerge in amorphous…

Disordered Systems and Neural Networks · Physics 2022-08-23 Adolfo G. Grushin

Designing metal-organic frameworks (MOFs) with novel chemistries is a longstanding challenge due to their large combinatorial space and complex 3D arrangements of the building blocks. While recent deep generative models have enabled…

Biomolecules · Quantitative Biology 2026-02-05 Nayoung Kim , Seongsu Kim , Sungsoo Ahn

We investigate bulk thermodynamic and microscopic structural properties of amorphous solids in the framework of the phase-field crystal (PFC) model. These are metastable states with a non-uniform density distribution having no long-range…

Soft Condensed Matter · Physics 2024-09-11 Shaho Abdalla , Andrew J. Archer , László Gránásy , Gyula I. Tóth

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally…

Materials Science · Physics 2021-10-19 Andrew S. Rosen , Justin M. Notestein , Randall Q. Snurr

We present a coupled atomistic-continuum method for the modeling of defects and interface dynamics of crystalline materials. The method uses atomistic models such as molecular dynamics near defects and interfaces, and continuum models away…

Materials Science · Physics 2009-11-07 Weinan E , Zhongyi Huang

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar…

Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range orderings,…

Materials Science · Physics 2025-09-18 Jonas A. Finkler , Yan Lin , Tao Du , Jilin Hu , Morten M. Smedskjaer

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in…

Materials Science · Physics 2021-06-28 Aditya Nandy , Chenru Duan , Heather J. Kulik

Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF…

Machine Learning · Computer Science 2020-11-02 Shehtab Zaman , Christopher Owen , Kenneth Chiu , Michael Lawler

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…