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Quantifying the U 5f covalence and degree of localization in U intermetallics

Physical Review Research 2024年第3期6卷 033068-033068页

作者： Andrea Marino Denise S. Christovam Daisuke Takegami Johannes Falke Miguel M. F. Carvalho Takaki Okauchi Chun-Fu Chang Simone G. Altendorf Andrea Amorese Martin Sundermann Andrei Gloskovskii Hlynur Gretarsson Bernhard Keimer Alexandr V. Andreev Ladislav Havela Andreas Leithe-Jasper Andrea Severing Jan Kuneš Liu Hao Tjeng Atsushi Hariki Nöthnitzer Straße 40 01187 Dresden Germany Institute of Solid State and Materials Physics Department of Applied Physics Institute of Physics II Department of Physics and Electronics PETRA III Heisenbergstraße 1 70569 Stuttgart Germany Department of Condensed Matter Physics Faculty of Mathematics and Physics Department of Condensed Matter Physics Faculty of Science

A procedure for quantifying the U 5f electronic covalency and degree of localization in U intermetallic compounds is presented. To this end, bulk sensitive hard and soft x-ray photoelectron spectroscopy were utilized in combination with density-functional theory (DFT) plus dynamical mean-field theory (DMFT) calculations. The energy dependence of the photoionization cross sections allows the disentanglement of the U 5f contribution to the valence band from the various other atomic subshells so the computational parameters in the DFT+DMFT can be reliably determined. Applying this method to UGa2 and UB2 as model compounds from opposite ends of the (de)localization range, we have achieved excellent simulations of the valence band and core-level spectra. The width in the distribution of atomic U 5f configurations contributing to the ground state, as obtained from the calculations, quantifies the correlated nature and degree of localization of the U 5f. The findings permit answering the longstanding question why different spectroscopic techniques give seemingly different numbers for the U 5f valence in intermetallic U compounds.

关键词： Heavy-fermion systems Strongly correlated systems Density functional theory Dynamical mean field theory X-ray photoelectron spectroscopy

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Sparse Estimation of High-Dimensional Inverse Covariance Matrices with Explicit Eigenvalue Constraints

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Journal of the Operations Research Society of China 2021年第3期9卷 543-568页

作者： Yun-Hai Xiao Pei-Li Li Sha Lu Institute of Applied Mathematics College of Mathematics and StatisticsHenan UniversityKaifeng 475000HenanChina School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China Department of Mathematics Nanning Normal UniversityNanning 530299GuangxiChina

Firstly,this paper proposes a generalized log-determinant optimization model with the purpose of estimating the high-dimensional sparse inverse covariance *** the normality assumption,the zero components in the inverse covariance matrices represent the conditional independence between pairs of variables given all the other *** generalized model considered in this study,because of the setting of the eigenvalue bounded constraints,covers a large number of existing estimators as special ***,rather than directly tracking the challenging optimization problem,this paper uses a couple of alternating direction methods of multipliers(ADMM)to solve its dual model where 5 separable structures are *** first implemented algorithm is based on a single Gauss–Seidel iteration,but it does not necessarily converge *** contrast,the second algorithm employs the symmetric Gauss–Seidel(sGS)based ADMM which is equivalent to the 2-block iterative scheme from the latest sGS decomposition ***,we do numerical simulations using the synthetic data and the real data set which show that both algorithms are very effective in estimating high-dimensional sparse inverse covariance matrix.

关键词： Non-smooth convex minimization Inverse covariance matrix Maximum likelihood estimation Augmented Lagrangian function Symmetric Gauss–Seidel iteration

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Effects of the initial perturbations on the Rayleigh–Taylor–Kelvin–Helmholtz instability system

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Frontiers of physics 2022年第3期17卷 77-89页

作者： Feng Chen Aiguo Xu Yudong Zhang Yanbiao Gan Bingbing Liu Shuang Wang School of Aeronautics Shan Dong Jiaotong UniversityJinan 250357China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsP.O.Box 8009-26Beijing 100088China HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mechanics and Safety Engineering Zhengzhou UniversityZhengzhou 450001China Hebei Key Laboratory of Trans-Media Aerial Underwater Vehicle North China Institute of Aerospace EngineeringLangfang 065000China Naval Architecture and Port Engineering College Shan Dong Jiaotong UniversityWeihai 264200China School of Science Shandong Jianzhu UniversityJinan 250101China

The effects of initial perturbations on the Rayleigh–Taylor instability (RTI), Kelvin–Helmholtz instability (KHI), and the coupled Rayleigh–Taylor–Kelvin–Helmholtz instability (RTKHI) systems are investigated using a multiple-relaxation-time discrete Boltzmann model. Six different perturbation interfaces are designed to study the effects of the initial perturbations on the instability systems. It is found that the initial perturbation has a significant influence on the evolution of RTI. The sharper the interface, the faster the growth of bubble or spike. While the influence of initial interface shape on KHI evolution can be ignored. Based on the mean heat flux strength D3,1, the effects of initial interfaces on the coupled RTKHI are examined in detail. The research is focused on two aspects: (i) the main mechanism in the early stage of the RTKHI, (ii) the transition point from KHI-like to RTI-like for the case where the KHI dominates at earlier time and the RTI dominates at later time. It is found that the early main mechanism is related to the shape of the initial interface, which is represented by both the bilateral contact angle θ_(1) and the middle contact angle θ_(2). The increase of θ_(1) and the decrease of θ_(2) have opposite effects on the critical velocity. When θ_(2) remains roughly unchanged at 90 degrees, if θ_(1) is greater than 90 degrees (such as the parabolic interface), the critical shear velocity increases with the increase of θ_(1), and the ellipse perturbation is its limiting case;If θ_(1) is less than 90 degrees (such as the inverted parabolic and the inverted ellipse disturbances), the critical shear velocities are basically the same, which is less than that of the sinusoidal and sawtooth disturbances. The influence of inverted parabolic and inverted ellipse perturbations on the transition point of the RTKHI system is greater than that of other interfaces: (i) For the same amplitude, the smaller the contact angle θ_(1), the later the transitio

关键词： discrete Boltzmann method hydrodynamic instability non-equilibrium characteristic initial perturbation

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Electrical analogue of one-dimensional and quasi-one-dimensional Aubry-André-Harper lattices

arXiv

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arXiv 2023年

作者： Ganguly, Sudin Maiti, Santanu K. Department of Physics School of Applied Sciences University of Science and Technology Meghalaya Ri-Bhoi793101 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata700108 India

The present work discusses the possibility to realize correlated disorder in electrical circuits and studies the localization phenomena in terms of two-port impedance. The correlated disorder is incorporated using the Aubry-André-Harper (AAH) model. One-dimensional and quasi-one-dimensional AAH structures are explored and directly mapped with their tight-binding analogues. Transitions from the high-conducting phase to the low-conducting one are observed for the circuits. © 2023, CC BY.

关键词： Hadrons

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Fracture Prediction for an Advanced High-Strength Steel Sheet Using the Fully Coupled Elastoplastic Damage Model with Stress-State Dependence

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Acta Mechanica Solida Sinica 2021年第2期34卷 263-273页

作者： Kai Zhang Mingchuan Wang Weijie Liu Jianlin Liu College of Pipeline and Civil Engineering China University of Petroleum(East China)Qingdao 266580China Sino-French Engineer School Nanjing University of Science and TechnologyNanjing 210094China Institute of Applied Physics and Computational Mathematics Beijing 10094China

In this study,the numerical simulations of sheet metal forming processes are performed based on a fully coupled elastoplastic damage *** effects of stress triaxiality and Lode angle are introduced into the damage evolution law to capture the loading-path-dependent *** proposed constitutive model is implemented into the finite element(FE)code ABAQUS/Explicit via the user-defined subroutine(VUMAT).Next,the identification procedure for DP780 based on the hybrid experimental-numerical method is presented in *** numerical results of simple tests are compared with the experiments,and obvious improvement is observed for the proposed model under various loading ***,the model is applied to predict the edge fracture during sheet blanking *** predicted global load–displacement responses and crack paths have a good agreement with the experimental results,indicating that the model holds great potentials in simulation of metal forming processes.

关键词： Ductile damage Full coupling Stress state Numerical simulation Edge fracture

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FLIGHTSCOPE: AN EXPERIMENTAL COMPARATIVE REVIEW OF AIRCRAFT DETECTION ALGORITHMS IN SATELLITE IMAGERY

arXiv

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arXiv 2024年

作者： Ghazouali, Safouane EL Venturini, Francesca Rüegsegger, Michael Gucciardi, Arnaud Venturi, Nicola Michelucci, Umberto Machine Learning Research & Development TOELT LLC AI lab Winterthur Switzerland Institute of Applied Mathematics & Physics ZHAW - Zurich University of Applied Sciences Winterthur Switzerland Competence Center for AI and Simulation Armasuisse S+T Thun Switzerland Computer Science Department Lucerne University of Applied Science and Arts Luzern Switzerland

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-based taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. Using the large HRPlanesV2 dataset, together with a rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5 and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from scratch. This exhaustive training and validation study reveal YOLOv5 as the preeminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlight the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, Recall, and Intersection over Union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. The benchmark toolkit and codes, available via GitHub, aims to further exploration and innovation in the realm of remote sensing object detection, paving the way for improved analytical methodologies in satellite imagery applications. © 2024, CC BY.

关键词： Satellite imagery

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Lung Cancer Diagnosis Based on Object Detection Technology 14th

Lung Cancer Diagnosis Based on Object Detection Technology

引用

14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Towards a Foundation Model for Neural Network Wavefunctions

arXiv

引用

arXiv 2023年

作者： Scherbela, Michael Gerard, Leon Grohs, Philipp Faculty of Mathematics University of Vienna Austria Research Network Data Science University of Vienna Austria Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Austria

Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schrödinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to optimize the wavefunction from scratch for each new system, thus requiring long optimization. In this work, we propose a novel neural network ansatz, which effectively maps uncorrelated, computationally cheap Hartree-Fock orbitals, to correlated, high-accuracy neural network orbitals. This ansatz is inherently capable of learning a single wavefunction across multiple compounds and geometries, as we demonstrate by successfully transferring a wavefunction model pre-trained on smaller fragments to larger compounds. Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model. Such a model could yield high-accuracy ab-initio energies using only minimal computational effort for fine-tuning and evaluation of observables. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Isomerization in highly excited states of triply charged carbon dioxide ions produced by intense laser fields

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Physical Review A 2024年第5期110卷 053110-053110页

作者： Ruichao Dong Ahai Chen Jinze Feng Shuai Zhang Wenbin Jiang Xiaoqing Hu Yong Wu Jianguo Wang Xincheng Wang Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China University of Chinese Academy of Sciences Beijing 100049 China Center for Transformative Science and School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China

It is commonly accepted that the isomerization process primarily occurs in lowly charged molecular states, while highly charged states typically favor a direct fragmentation pathway through Coulomb repulsion. In the current experiment with an intense 800-nm laser field, a three-body isomerization pathway leading to intermediate O22+ formation and subsequent dissociation is observed in highly excited states of CO23+. It is confirmed by the calculated potential energy curves that CO23+ molecular ions in highly excited states with a linear geometry could transition through conical intersections into low-lying states with a bond angle around 70∘ (e.g., 62A1→12A1, 102B1→12B1), finally leading to oxygen molecule ions formation via isomerization. The present findings demonstrate that isomerization-induced change in molecular structures is a universal and nonnegligible phenomenon, even in triply charged molecular ions, providing insights into the photoinduced ultrafast processes in Earth's primitive atmosphere and benefit the understanding of CO2-dominated planetary atmospheres.

关键词： Photodissociation

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An Extendable Cloud-Native Alloy Property Explorer

arXiv

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arXiv 2024年

作者： Li, Zhuoyuan Wen, Tongqi Zhang, Yuzhi Liu, Xinzijian Zhang, Chengqian Pattamatta, A.S.L. Subrahmanyam Gong, Xiaoguo Ye, Beilin Wang, Han Zhang, Linfeng Srolovitz, David J. Department of Mechanical Engineering The University of Hong Kong Hong Kong AI for Science Institute Beijing China DP Technology Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China

The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design. This depends on the availability of accurate descriptions of atomic bonding through various forms of interatomic potentials. We present an efficient, robust platform for calculating materials properties, i.e., APEX, the Alloy Property Explorer. APEX enables the rapid evolution of interatomic potential development and optimization, which is of particular importance in fine-tuning new classes of general AI-based foundation models to forms that are readily applicable to impacting materials development. APEX is an open-source, extendable, and cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization, web-based platforms and NoSQL database client. It is designed for expert and non-specialist users, lowers the barrier to entry for interdisciplinary research within the "AI for Materials" framework. We describe the foundation and use of APEX, as well as provide an example of its application to properties of titanium for a wide-range of bonding descriptions. Copyright © 2024, The Authors. All rights reserved.

关键词： Materials properties

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