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Characterizing the mechanical response of metallic glasses to uniaxial tension using a spring network model

Physical Review Materials 2023年第7期7卷 073605-073605页

作者： Aya Nawano Weiwei Jin Jan Schroers Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

A coarse-grained spring network model is proposed for the prediction of the mechanical response of metallic glasses as a function of the microstructure prior to loading. This model describes the mechanical response of metallic glasses using a network of parallel springs that can break and reform, mimicking atomic rearrangements during deformation. We compare predictions of the spring network model for stress versus strain to results from numerical simulations of athermal quasistatic, uniaxial tensile deformation of Cu50Zr50 metallic glasses using Lennard-Jones (LJ) and embedded atom method (EAM) atomic interactions. We show that both the LJ and EAM models possess qualitatively similar stress σ versus strain γ curves. By specifying five parameters [ultimate strength, strain at ultimate strength, slopes of σ(γ) at γ=0 and at large strain, and strain at fracture where σ=0], we demonstrate that the spring network model can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu50Zr50, as well as recent experimental studies of several Zr-based metallic glasses.

关键词： Alloys Amorphous materials Glasses Packing & jamming problems Molecular dynamics

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BPS2025 - Systematic embedding of biomechanics within dynamical graph grammars with application to actin networks in dendritic spine heads

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Biophysical Journal 2025年第3期124卷 206a-206a页

作者： Eric Mjolsness Matthew Hur Thomas M. Bartol Terrence J. Sejnowski Departments of Computer Science and Mathematics University of California Irvine Irvine CA USA Mathematical Computational and Systems Biology Program University of California Irvine Irvine CA USA Computational Neurobiology Laborartory Salk Institute for Biological Studies San Diego CA USA

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Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

arXiv

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arXiv 2022年

作者： Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nyström approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications. © 2022, CC BY.

关键词： Remote sensing

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Correction: Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes

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Statistics and Computing 2024年第6期34卷 1-1页

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

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Generating large disordered stealthy hyperuniform systems with ultrahigh accuracy to determine their physical properties

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Physical Review Research 2023年第3期5卷 033190-033190页

作者： Peter K. Morse Jaeuk Kim Paul J. Steinhardt Salvatore Torquato Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute of Materials Princeton University Princeton New Jersey 08544 USA Princeton Center for Theoretical Science Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Hyperuniform many-particle systems are characterized by a structure factor S(k) that is precisely zero as |k|→0; and stealthy hyperuniform systems have S(k)=0 for the finite range 0<|k|≤K, called the “exclusion region.” Through a process of collective-coordinate optimization, energy-minimizing disordered stealthy hyperuniform systems of moderate size have been made to high accuracy, and their novel physical properties have shown great promise. However, minimizing S(k) in the exclusion region is computationally intensive as the system size becomes large. In this paper, we present an improved methodology to generate such states using double-double precision calculations on graphical processing units (GPUs) that reduces the deviations from zero within the exclusion region by a factor of approximately 1030 for system sizes more than an order of magnitude larger. We further show that this ultrahigh accuracy is required to draw conclusions about their corresponding characteristics, such as the nature of the associated energy landscape and the presence or absence of Anderson localization, which might be masked, even when deviations are relatively small.

关键词： Exotic phases of matter

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Virtual Flavonoids Screening Identifies Potent Compounds Against Enterovirus A71 and Coxsackievirus A16

SSRN

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SSRN 2023年

作者： Sripattarphan, Amita Sanachai, Kamonpan Chavasiri, Warinthorn Wolschann, Peter Langer, Thierry Boonyasuppayakorn, Siwaporn Rungrotmongkol, Thanyada Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Khon Kaen University Khon Kaen40002 Thailand Center of Excellence in Natural Products Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Pharmaceutical Chemistry Faculty of Life Sciences University of Vienna Althanstraße 14 ViennaA-1090 Austria Institute of Theoretical Chemistry University of Vienna Vienna1090 Austria Center of Excellence in Applied Medical Virology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

Worldwide outbreaks of hand, foot, and mouth disease (HFMD) are caused by enterovirus A71 (EV-A71) and coxsackievirus A16 (CV-A16). Since no anti-HFMD drugs are currently available, it is interesting to study potential viral inhibitors. Rupintrivir is a rhinovirus 3C protease (3Cpro) inhibitor with apparent inhibitory activity against HFMD. This study constructs pharmacophore models of the EV-A71 and CV-A16 3Cpro complexed with rupintrivir using molecular dynamics (MD) simulations. Both models had similar pharmacophore features, including hydrogen bond donors and acceptors and hydrophobic interactions. These pharmacophore models were then used as a template to screen 39 flavonoid compounds as potential novel inhibitors. Diosmin, epigallocatechin gallate (EGCG), and RTH-011 showed high binding affinities for EV-A71 and CV-A16 3Cpro. They formed hydrogen bonds with important surrounding residues in both proteins, including H40, L127, T142, A144, T145, H161, I162, G163, and G164. In addition, their effective concentrations against rhabdomyosarcoma (RD) cell infection by EV-A71 and CV-A16 were determined. EGCG had the highest half maximal effective concentration (EC50) of 12.86 ± 1.30 µM for EV-A71 and 15.54 ± 1.50 µM for CV-A16, while diosmin had EC50 of 21.01±1.57 µM for EV-A71 and 30.68 ± 3.25 µM for CV-A16. Both compounds were non-toxic in RD cells, with 50% cytotoxic concentrations of >100 µM for EGCG and >500 µM for diosmin. In addition, MD simulation analysis showed that EGCG had a higher binding affinity than diosmin, supported by its significantly lower solvated binding free energies and greater numbers of contact atoms and key binding residues. Moreover, previous studies reported EGCG’s inhibitory effect on other viruses, such as severe acute respiratory syndrome coronavirus 2. Therefore, our findings suggest that EGCG can effectively inhibit the EV-A71 and CV-A16 3Cpro. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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Consensus dynamics and coherence in hierarchical small-world networks

arXiv

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arXiv 2023年

作者： Liao, Yunhua Maama, Mohamed Aziz-Alaoui, M.A. Department of Mathematics Hunan University of Technology and Business Hunan Changsha410205 China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan Changsha410205 China Applied Mathematics and Computational Science Program KAUST Thuwal23955-6900 Saudi Arabia Normandie Univ UNIHAVRE LMAH FR-CNRS-3335 ISCN Le Havre76600 France

The hierarchical small-world network is a real-world network. It models well the benefit transmission web of the pyramid selling in China and many other countries. In this paper, by applying the spectral graph theory, we study three important aspects of the consensus problem in the hierarchical small-world network: convergence speed, communication time-delay robustness, and network coherence. Firstly, we explicitly determine the Laplacian eigenvalues of the hierarchical small-world network by making use of its treelike structure. Secondly, we find that the consensus algorithm on the hierarchical small-world network converges faster than that on some well-studied sparse networks, but is less robust to time delay. The closed-form of the first-order and the second-order network coherence are also derived. Our result shows that the hierarchical small-world network has an optimal structure of noisy consensus dynamics. Therefore, we provide a positive answer to two open questions of Yi et al. Finally, we argue that some network structure characteristics, such as large maximum degree, small average path length, and large vertex and edge connectivity, are responsible for the strong robustness with respect to external perturbations. Copyright © 2023, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Communicationmachine Learning-Based QSAR and LB-PaCS-MD Guided Design of SARs-CoV-2 Main Protease Inhibitors

SSRN

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SSRN 2024年

作者： Toopradab, Borwornlak Xie, Wanting Duan, Lian Hengphasatporn, Kowit Harada, Ryuhei Sinsulpsiri, Silpsiri Shigeta, Yasuteru Shi, Liyi Maitarad, Phornphimon Rungrotmongkol, Thanyada Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Research Center of Nano Science and Technology Department of Chemistry College of Science Shanghai University Shanghai200444 China University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Graduate School of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki305–8571 Japan Emerging Industries Institute Shanghai University Zhejiang Jiaxing314006 China

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an R2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design;with the potential to guide experimental synthesis and enzyme assay testing. © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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Vector encoding of phylogenetic trees by ordered leaf attachment

arXiv

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arXiv 2025年

作者： Richman, Harry Zhang, Cheng Matsen, Frederick A. Computational Biology Program Fred Hutchinson Cancer Center Seattle United States Mathematics Division National Center for Theoretical Sciences Taipei Taiwan School of Mathematical Sciences Center for Statistical Science Peking University Beijing China Department of Statistics University of Washington Seattle United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Center Seattle United States

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "ordered leaf attachment" (OLA) method for uniquely encoding a binary, rooted phylogenetic tree topology as an integer vector. OLA encoding and decoding take linear time in the number of leaf nodes, and the set of vectors corresponding to trees is a simply-described subset of integer sequences. The OLA encoding is unique compared to other existing encodings in having these properties. The integer vector encoding induces a distance on the set of trees, and we investigate this distance in relation to the NNI and SPR *** Codes 92B10, 05C05, 05C85, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Vectors

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular Engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular Engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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