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arXiv 2019年

作者： Cinacchi, Giorgio Torquato, Salvatore Instituto de Ciencias de Materiales "Nicolás Cabrera" Universidad Autónoma de Madrid Ciudad Universitaria de Cantoblanco MadridE-28049 Spain Department of Chemistry Department of Physics Institute for the Science and Technology of Materials Program for Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Among the family of hard convex lens-shaped particles (lenses), the one with aspect ratio equal to 2/3 is ‘optimal’ in the sense that the maximally random jammed (MRJ) packings of such lenses achieve the highest packing fraction φMRJ ' 0.73. This value is only a few percent lower than φDKP = 0.76210 . . . , the packing fraction of the corresponding densest-known crystalline (degenerate) packings. By exploiting the appreciably reduced propensity that a system of such optimal lenses has to positionally and orientationally order, disordered packings of them are progressively generated by a Monte Carlo method-based procedure from the dilute equilibrium isotropic fluid phase to the dense nonequilibrium MRJ state. This allows one to closely monitor how the (micro)structure of these packings changes in the process of formation of the MRJ state. The gradual changes undergone by the many structural descriptors calculated can coherently and consistently be traced back to the gradual increase in contacts between the hard particles until the isostatic mean value of 10 contact neighbors per lens is reached at the effectively hyperuniform MRJ state. Compared to the MRJ state of hard spheres, the MRJ state of such optimal lenses is denser (less porous), more disordered, and rattler-free. This set of characteristics makes them good glass formers. It is possible that this conclusion may also hold for other hard convex uniaxial particles with a correspondingly similar aspect ratio, be they oblate or prolate, and that, by using suitable biaxial variants of them, that set of characteristics might further improve. Copyright © 2019, The Authors. All rights reserved.

关键词： Aspect ratio

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Spectral properties of hyperbolic nanonetworks with tunable aggregation of simplexes

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Physical Review E 2019年第1期100卷 012309-012309页

作者： Marija Mitrović Dankulov Bosiljka Tadić Roderick Melnik Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 11080 Belgrade Serbia Department of Theoretical Physics Jožef Stefan Institute Jamova 39 1000 Ljubljana Slovenia Complexity Science Hub Vienna Josephstadterstrasse 39 1080 Vienna Austria MS2Discovery Interdisciplinary Research Institute M2NeT Laboratory and Department of Mathematics Wilfrid Laurier University 75 University Ave W Waterloo Ontario Canada N2L 3C5 BCAM–Basque Center for Applied Mathematics Alameda de Mazarredo 14 E-48009 Bilbao Spain

Cooperative self-assembly is a ubiquitous phenomenon found in natural systems which is used for designing nanostructured materials with new functional features. Its origin and mechanisms, leading to improved functionality of the assembly, have attracted much attention from researchers in many branches of science and engineering. These complex structures often come with hyperbolic geometry; however, the relation between the hyperbolicity and their spectral and dynamical properties remains unclear. Using the model of aggregation of simplexes introduced by Šuvakov et al. [Sci. Rep. 8, 1987 (2018)], here we study topological and spectral properties of a large class of self-assembled structures or nanonetworks consisting of monodisperse building blocks (cliques of size n=3,4,5,6) which self-assemble via sharing the geometrical shapes of a lower order. The size of the shared substructure is tuned by varying the chemical affinity ν such that for significant positive ν sharing the largest face is the most probable, while for ν<0, attaching via a single node dominates. Our results reveal that, while the parameter of hyperbolicity remains δmax=1 across the assemblies, their structure and spectral dimension ds vary with the size of cliques n and the affinity when ν≥0. In this range, we find that ds>4 can be reached for n≥5 and sufficiently large ν. For the aggregates of triangles and tetrahedra, the spectral dimension remains in the range ds∈[2,4), as well as for the higher cliques at vanishing affinity. On the other end, for ν<0, we find ds≂1.57 independently on n. Moreover, the spectral distribution of the normalized Laplacian eigenvalues has a characteristic shape with peaks and a pronounced minimum, representing the hierarchical architecture of the simplicial complexes. These findings show how the structures compatible with complex dynamical properties can be assembled by controlling the higher-order connectivity among the building blocks.

关键词： Classical statistical mechanics Geometric & topological phases Network structure Synchronization transition Topological tools

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New tessellation-based procedure to design perfectly hyperuniform disordered dispersions for materials discovery

arXiv

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arXiv 2019年

作者： Kim, J. Torquato, S. Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Disordered hyperuniform dispersions are exotic amorphous two-phase materials characterized by an anomalous suppression of long-wavelength volume-fraction fluctuations, which endows them with novel physical properties. While such unusual materials have received considerable attention, a stumbling block has been an inability to create large samples that are truly hyperuniform due to current computational and experimental limitations. To overcome such barriers, we introduce a new and simple construction procedure that guarantees perfect hyperuniformity for very large sample sizes. This methodology involves tessellating space into cells and then inserting a particle into each cell such that the local-cell particle packing fractions are identical to the global packing fraction. We analytically prove that such dispersions are perfectly hyperuniform in the infinite-sample-size limit. Our methodology enables a remarkable mapping that converts a very large nonhyperuniform disordered dispersion into a perfectly hyperuniform one, which we numerically demonstrate in two and three dimensions. A similar analysis also establishes the hyperuniformity of the famous Hashin-Shtrikman multiscale dispersions, which possess optimal transport and elastic properties. Our hyperuniform designs can be readily fabricated using modern photolithographic and 3D printing technologies. The exploration of the enormous class of hyperuniform dispersions that can be designed and tuned by our tessellation-based methodology paves the way for accelerating the discovery of novel hyperuniform materials. Copyright © 2019, The Authors. All rights reserved.

关键词： 3D printers

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Multifunctional Composites for Elastic and Electromagnetic Wave Propagation

arXiv

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arXiv 2019年

作者： Kim, Jaeuk Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Composites are ideally suited to achieve desirable multifunctional effective properties, since the best properties of different materials can be judiciously combined with designed microstructures. Here we establish the first cross-property relations for two-phase composite media that link effective elastic and electromagnetic wave characteristics to one another, including effective wave speeds and attenuation coefficients. This is achieved by deriving accurate formulas for the effective elastodynamic properties that depend on the microstructure via the spectral density and utilizing previous analogous results for effective electromagnetic properties. Our formulas enable us to explore the wave characteristics of a broad class of disordered microstructures, including exotic disordered "hyperuniform" varieties, that can have advantages over crystalline ones, such as nearly optimal, direction-independent properties and robustness against defects. We specifically show that disordered "stealthy" hyperuniform/nonhyperuniform microstructures exhibit novel elastic wave characteristics that have the potential for future applications, e.g., low-pass or narrow-band-pass (using negative Poisson ratio materials) filters that transmit or absorb elastic waves "isotropically" for a range of wavenumbers. We also demonstrate that two-phase media with engineered disorder and phase properties can exhibit "anomalous dispersion" with resonance-like attenuation. Our cross-property relations for effective wave characteristics can be applied to design multifunctional composites via inverse techniques, including exterior components of spacecraft or building materials that require both excellent stiffness and electromagnetic absorption, and heat-sinks for CPUs that have to efficiently emit thermal radiation and suppress mechanical vibrations, and nondestructive evaluation of the elastic moduli of materials from the effective dielectric response. DR Copyright © 2019, The Authors. All rights res

关键词： Microstructure

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Methodology to construct large realizations of perfectly hyperuniform disordered packings

arXiv

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arXiv 2019年

Disordered hyperuniform packings (or dispersions) are unusual amorphous two-phase materials that are endowed with exotic physical properties. Such hyperuniform systems are characterized by an anomalous suppression of volume-fraction fluctuations at infinitely long-wavelengths, compared to ordinary disordered materials. While there has been growing interest in such singular states of amorphous matter, a major obstacle has been an inability to produce large samples that are perfectly hyperuniform due to practical limitations of conventional numerical and experimental methods. To overcome these limitations, we introduce a general theoretical methodology to construct perfectly hyperuniform packings in d-dimensional Euclidean space Rd. Specifically, beginning with an initial general tessellation of space by disjoint cells that meets a "bounded-cell" condition, hard particles are placed inside each cell such that the local-cell particle packing fractions are identical to the global packing fraction. We prove that the constructed packings with a polydispersity in size in Rd are perfectly hyperuniform in the infinite-sample-size limit and the hyperuniformity of such packings is independent of particle shapes, positions, and numbers per cell. We use this theoretical formulation to devise an efficient and tunable algorithm to generate extremely large realizations of such packings. We employ two distinct initial tessellations: Voronoi as well as sphere tessellations. Beginning with Voronoi tessellations, we show that our algorithm can remarkably convert extremely large nonhyperuniform packings into hyperuniform ones in R2 and R3. Implementing our theoretical methodology on sphere tessellations, we establish the hyperuniformity of the classical Hashin-Shtrikman multiscale coated-spheres structures, which are known to be two-phase media microstructures that possess optimal effective transport and elastic properties. A consequence of our work is a rigorous demonstration that pack

关键词： Cells

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Phase field crystal model for heterostructures

arXiv

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arXiv 2019年

作者： Hirvonen, Petri Heinonen, Vili Dong, Haikuan Fan, Zheyong Elder, Ken R. Ala-Nissila, Tapio QTF Centre of Excellence Department of Applied Physics Aalto University School of Science P.O. Box 11000 Aalto EspooFIN-00076 Finland Department of Mathematics Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139-4307 United States School of Mathematics and Physics Bohai University Jinzhou121000 China Department of Physics Oakland University RochesterMI48309 United States Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University LoughboroughLeicestershireLE11 3TU United Kingdom

Atomically thin 2-dimensional heterostructures are a promising, novel class of materials with groundbreaking properties. The possiblity of choosing the many constituent components and their proportions allows optimizing these materials to specific requirements. The wide adaptability comes with a cost of large parameter space making it hard to experimentally test all the possibilities. Instead, efficient computational modelling is needed. However, large range of relevant time and length scales related to physics of polycrystalline materials poses a challenge for computational studies. To this end, we present an efficient and flexible phase-field crystal model to describe the atomic configurations of multiple atomic species and phases coexisting in the same physical domain. We extensively benchmark the model for two-dimensional binary systems in terms of their elastic properties and phase boundary configurations and their energetics. As a concrete example, we demonstrate modelling lateral heterostructures of graphene and hexagonal boron nitride. We consider both idealized bicrystals and large-scale systems with random phase distributions. We find consistent relative elastic moduli and lattice constants, as well as realistic continuous interfaces and faceted crystal shapes. Zigzag-oriented interfaces are observed to display the lowest formation energy. Copyright © 2019, The Authors. All rights reserved.

关键词： Polycrystalline materials

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ABACUS: An Electronic Structure Analysis Package for the AI Era

arXiv

引用

arXiv 2025年

作者： Zhou, Weiqing Zheng, Daye Liu, Qianrui Lu, Denghui Liu, Yu Lin, Peize Huang, Yike Peng, Xingliang Bao, Jie J. Cai, Chun Jin, Zuxin Wu, Jing Zhang, Haochong Jin, Gan Ji, Yuyang Shen, Zhenxiong Liu, Xiaohui Sun, Liang Cao, Yu Sun, Menglin Liu, Jianchuan Chen, Tao Liu, Renxi Li, Yuanbo Han, Haozhi Liang, Xinyuan Bao, Taoni Chen, Nuo Ren, Hongxu Zhang, Xiaoyang Liu, Zhaoqing Fu, Yiwei Liu, Maochang Li, Zhuoyuan Wen, Tongqi Tang, Zechen Xu, Yong Duan, Wenhui Wang, Xiaoyang Gu, Qiangqiang Dai, Fu-Zhi Zheng, Qijing Zhao, Jin Zhang, Yuzhi Ou, Qi Jiang, Hong Liu, Shi Xu, Ben Xu, Shenzhen Ren, Xinguo He, Lixin Zhang, Linfeng Chen, Mohan AI for Science Institute Beijing100080 China HEDPS CAPT School of Physics College of Engineering Peking University Beijing100871 China College of Engineering Peking University Beijing100871 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Supercomputing Center University of Science and Technology of China Anhui Hefei230026 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Materials Science and Engineering Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China College of Chemistry and Molecular Engineering Peking University Beijing100871 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow Xi’an Jiaotong University Shaanxi Xi’an710049 China Suzhou Academy of Xi’an Jiaotong University Jiangsu Suzhou215123 China Center for Structural Materials Department of Mechanical Engineering The University of Hong Kong Hong Kong The University of Hong Kong Shenzhen China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing China Saitama Wako351-0198 Japan Institute for Advanced Study Tsinghua University Beijing100084 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Physics University of Science and

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

arXiv

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arXiv 2023年

作者： Adewole, Maruf Rudie, Jeffrey D. Gbdamosi, Anu Toyobo, Oluyemisi Raymond, Confidence Zhang, Dong Omidiji, Olubukola Akinola, Rachel Suwaid, Mohammad Abba Emegoakor, Adaobi Ojo, Nancy Aguh, Kenneth Kalaiwo, Chinasa Babatunde, Gabriel Ogunleye, Afolabi Gbadamosi, Yewande Iorpagher, Kator Calabrese, Evan Aboian, Mariam Linguraru, Marius Albrecht, Jake Wiestler, Benedikt Kofler, Florian Janas, Anastasia LaBella, Dominic Kzerooni, Anahita Fathi Li, Hongwei Bran Iglesias, Juan Eugenio Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Van Leemput, Koen Bukas, Christina Piraud, Maire Conte, Gian-Marco Johansson, Elaine Meier, Zeke Menze, Bjoern H. Baid, Ujjwal Bakas, Spyridon Dako, Farouk Fatade, Abiodun Anazodo, Udunna C. Lagos Nigeria Department of Radiation Biology Radiotherapy and Radiodiagnosis University of Lagos Lagos Nigeria Department of Radiology University of California San Diego United States Crestview Radiology Limited Lagos Nigeria Lagos University Teaching Hospital Lagos Nigeria Lagos State University Teaching Hospital Lagos Ikeja Nigeria NSIA-Kano Diagnostic Center Kano Nigeria Nnamdi Azikiwe University Teaching Hospital Anambra State Nnewi Nigeria Federal Medical Centre Ogun State Abeokuta Nigeria Federal Medical Centre Abia State Umuahia Nigeria National Hospital Abuja FCT Nigeria Benue State University Teaching Hospital Benue State Makurdi Nigeria Duke University Medical Center Department of Radiology United States University of California San FranciscoCA United States Yale University New HavenCT United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States Sage Bionetworks United States Department of Neuroradiology Technical University of Munich Munich Germany Helmholtz Research Center Munich Germany Duke University Medical Center Department of Radiation Oncology United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States University of Zurich Switzerland Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Mayo Clinic MN United States Precision FDA U.S. Food and Drug Administration Silver SpringMD United States Booz Allen Hamilton McLeanVA United States Department of Radiology Perelman School of Medicine U

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state- of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely. © 2023, CC BY.

关键词： Tumors

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Active learning of uniformly accurate inter-atomic potentials for materials simulation

arXiv

引用

arXiv 2018年

作者： Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy

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Experimental investigation of nonlinear internal waves in deep water with miscible uids

arXiv

引用

arXiv 2018年

作者： Camassa, Roberto Hurley, Matthew W. McLaughlin, Richard M. Passaggia, Pierre-Yves Thomson, Colin F. C. Carolina Center for Interdisciplinary Applied Mathematics Department of Mathematics University of North Carolina Chapel HillNC27599 United States Department of Marine Sciences University of North Carolina Chapel HillNC27599 United States

Laboratory experimental results are presented for nonlinear Internal Solitary Waves (ISW) propagation in 'deep water' configuration with miscible uids. The results are validated against direct numerical simulations and traveling wave exact solutions where the effect of the diffused interface is taken into account. The waves are generated by means of a dam break and their evolution is recorded with Laser Induced Fluorescence (LIF) and Particle Image Velocimetry (PIV). In particular, data collected in a frame moving with the waves are presented here for the first time. Our results are representative of geophysical applications in the deep ocean where weakly nonlinear theories fail to capture the characteristics of large amplitude ISWs from field observations. Copyright © 2018, The Authors. All rights reserved.

关键词： Velocity measurement

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