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The last decade has seen intense research in materials with reduced dimensionality. The low dimensionality leads to interesting electronic behavior due to electronic confinement and reduced screening. The investigations have to a large…

Materials Science · Physics 2019-03-27 Peter Mahler Larsen , Mohnish Pandey , Mikkel Strange , Karsten Wedel Jacobsen

With the increasing volume of streaming data in industrial systems, online anomaly detection has become a critical task. The diverse and rapidly evolving data patterns pose significant challenges for online anomaly detection. Many existing…

Machine Learning · Computer Science 2026-01-06 Zewei Yu , Jianqiu Xu , Caimin Li

Materials informatics, data-enabled investigation, is a "fourth paradigm" in materials science research after the conventional empirical approach, theoretical science, and computational research. Materials informatics has two essential…

Materials Science · Physics 2023-05-09 Bhupesh Bishnoi

Recently, osmium diboride (OsB2) has attracted considerable attention as an incompressible and hard material. We investigate the structural property, elastic constants, and electronic structure of orthorhombic OsB2 by the first-principles…

Materials Science · Physics 2007-05-23 Z. F. Hou

Three-dimensional electron diffraction (3DED) is a powerful technique providing for crystal structure solutions of sub-micron sized crystals too small for structure determination via X-ray techniques. The entry requirement, however, of a…

Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators and magnetic refrigeration systems. Discovering materials with large magnetic…

The electronic structure of a material fundamentally determines its underlying physical, and by extension, its functional properties. Consequently, the ability to identify or generate materials with desired electronic properties would…

Materials Science · Physics 2025-04-09 Shuyi Jia , Panchapakesan Ganesh , Victor Fung

The understanding and modeling of complex physical phenomena through dynamical systems has historically driven scientific progress, as it provides the tools for predicting the behavior of different systems under diverse conditions through…

Machine Learning · Computer Science 2025-10-03 Karin L. Yu , Eleni Chatzi , Georgios Kissas

The electronic and optical properties of graphene monoxide, a new type of semiconductor materials, are first theoretically studied based on density functional theory. Electronic calculations show that the band gap is 0.952 eV which indicate…

Materials Science · Physics 2012-09-05 Gui Yang , Yufeng Zhang , Xunwang Yan

Large-scale computational surveys are increasingly used to map the landscape of stable crystalline materials. We report a high-throughput energy screening of inorganic crystals that enumerates binary and ternary compositions up to a…

Materials Science · Physics 2026-01-30 Abhijith S Parackal , Florian Trybel , Felix Andreas Faber , Rickard Armiento

Substructure search in chemical compound databases is a fundamental task in cheminformatics with critical implications for fields such as drug discovery, materials science, and toxicology. However, the increasing size and complexity of…

Databases · Computer Science 2023-10-04 Vsevolod Vaskin , Dmitri Jakovlev , Fedor Bakharev

The Materials Project crystal structure database has been searched for materials possessing layered motifs in their crystal structures using a topology-scaling algorithm. The algorithm identifies and measures the sizes of bonded atomic…

Materials Science · Physics 2017-06-08 Michael Ashton , Joshua Paul , Susan B. Sinnott , Richard G. Hennig

We show how generative machine learning can be used for the rapid computation of strongly dynamical electron diffraction directly from crystal structures, specifically in large-angle convergent-beam electron diffraction (LACBED) patterns.…

Materials Science · Physics 2025-07-31 Joseph J. Webb , Richard Beanland , Rudolf A. Römer

Molecular structures are always depicted as 2D printed form in scientific documents like journal papers and patents. However, these 2D depictions are not machine-readable. Due to a backlog of decades and an increasing amount of these…

Computer Vision and Pattern Recognition · Computer Science 2022-05-24 Youjun Xu , Jinchuan Xiao , Chia-Han Chou , Jianhang Zhang , Jintao Zhu , Qiwan Hu , Hemin Li , Ningsheng Han , Bingyu Liu , Shuaipeng Zhang , Jinyu Han , Zhen Zhang , Shuhao Zhang , Weilin Zhang , Luhua Lai , Jianfeng Pei

Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that…

Materials Science · Physics 2021-05-26 Achintha Ihalage , Yang Hao

The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we…

Materials Science · Physics 2024-10-31 Abhijith S. Parackal , Rhys E. A. Goodall , Felix A. Faber , Rickard Armiento

Structural identifiability is an important property of parametric ODE models. When conducting an experiment and inferring the parameter value from the time-series data, we want to know if the value is globally, locally, or non-identifiable.…

Discrete Mathematics · Computer Science 2024-06-25 Natali Gogishvili

In the purpose of expanding the family of two-dimensional materials, we predict the existence of two-dimensional octa-structure of nitrogen group elements that are composed of squares and octagons in first-principle method based on density…

Materials Science · Physics 2015-12-08 Yu Zhang , Jason Lee , Wei-Liang Wang , Dao-Xin Yao

A systematic review is made for the AA-, AB- and ABC-stacked graphites. The generalized tight-binding model, accompanied with the effective-mass approximation and the Kubo formula, is developed to investigate electronic and optical…

Mesoscale and Nanoscale Physics · Physics 2017-05-09 Chiun-Yan Lin , Rong-Bin Chen , Yen-Hung Ho , Ming-Fa Lin

Energy band theory is a foundational framework in condensed matter physics. In this work, we employ a deep learning method, BNAS, to find a direct correlation between electronic band structure and superconducting transition temperature. Our…

Superconductivity · Physics 2025-09-08 Jun Li , Wenqi Fang , Shangjian Jin , Tengdong Zhang , Yanling Wu , Xiaodan Xu , Yong Liu , Dao-Xin Yao