arXiv - PHYS - Computational Physics最新文献_第6页

The qspec Python package: A physics toolbox for laser spectroscopy qspec Python 软件包：激光光谱物理学工具箱

arXiv - PHYS - Computational Physics Pub Date : 2024-09-02 DOI: arxiv-2409.01417

Patrick Müller, Wilfried Nörtershäuser

引用次数: 0

Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials 高保真图深度学习原子间位势的数据高效构建

arXiv - PHYS - Computational Physics Pub Date : 2024-09-02 DOI: arxiv-2409.00957

Tsz Wai Ko, Shyue Ping Ong

{"title":"Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials","authors":"Tsz Wai Ko, Shyue Ping Ong","doi":"arxiv-2409.00957","DOIUrl":"https://doi.org/arxiv-2409.00957","url":null,"abstract":"Machine learning potentials (MLPs) have become an indispensable tool in\u0000large-scale atomistic simulations because of their ability to reproduce ab\u0000initio potential energy surfaces (PESs) very accurately at a fraction of\u0000computational cost. For computational efficiency, the training data for most\u0000MLPs today are computed using relatively cheap density functional theory (DFT)\u0000methods such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient\u0000approximation (GGA) functional. Meta-GGAs such as the recently developed\u0000strongly constrained and appropriately normed (SCAN) functional have been shown\u0000to yield significantly improved descriptions of atomic interactions for\u0000diversely bonded systems, but their higher computational cost remains an\u0000impediment to their use in MLP development. In this work, we outline a\u0000data-efficient multi-fidelity approach to constructing Materials 3-body Graph\u0000Network (M3GNet) interatomic potentials that integrate different levels of\u0000theory within a single model. Using silicon and water as examples, we show that\u0000a multi-fidelity M3GNet model trained on a combined dataset of low-fidelity GGA\u0000calculations with 10% of high-fidelity SCAN calculations can achieve accuracies\u0000comparable to a single-fidelity M3GNet model trained on a dataset comprising 8x\u0000the number of SCAN calculations. This work paves the way for the development of\u0000high-fidelity MLPs in a cost-effective manner by leveraging existing\u0000low-fidelity datasets.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Roadmap to Holographic Focused Ultrasound Approaches to Generate Thermal Patterns 全息聚焦超声方法生成热模式路线图

arXiv - PHYS - Computational Physics Pub Date : 2024-09-02 DOI: arxiv-2409.01323

Ceren Cengiz, Zekeriya Ender Eger, Pinar Acar, Wynn Legon, Shima Shahab

{"title":"A Roadmap to Holographic Focused Ultrasound Approaches to Generate Thermal Patterns","authors":"Ceren Cengiz, Zekeriya Ender Eger, Pinar Acar, Wynn Legon, Shima Shahab","doi":"arxiv-2409.01323","DOIUrl":"https://doi.org/arxiv-2409.01323","url":null,"abstract":"In therapeutic focused ultrasound (FUS), such as thermal ablation and\u0000hyperthermia, effective acousto-thermal manipulation requires precise targeting\u0000of complex geometries, sound wave propagation through irregular structures and\u0000selective focusing at specific depths. Acoustic holographic lenses (AHLs)\u0000provide a distinctive capability to shape acoustic fields into precise, complex\u0000and multifocal FUS-thermal patterns. Acknowledging the under-explored potential\u0000of AHLs in shaping ultrasound-induced heating, this study introduces a roadmap\u0000for acousto-thermal modeling in the design of AHLs. Three primary modeling\u0000approaches are studied and contrasted using four distinct shape groups for the\u0000imposed target field. They include pressure-based (BSC-TR and ITER-TR),\u0000temperature-based (IHTO-TR), and machine learning (ML)-based (GaN and Feat-GAN)\u0000methods. New metrics including image quality, thermal efficiency, control, and\u0000computational time are introduced. The importance of evaluating target pattern\u0000complexity, thermal and pressure requirements, and computational resources is\u0000highlighted for selecting the appropriate methods. For lightly heterogeneous\u0000media and targets with lower pattern complexity, BSC-TR combined with error\u0000diffusion algorithms provides an effective solution. As pattern complexity\u0000increases, ITER-TR becomes more suitable, enabling optimization through\u0000iterative forward and backward propagations controlled by different error\u0000metrics. IHTO-TR is recommended for highly heterogeneous media, particularly in\u0000applications requiring thermal control and precise heat deposition. GaN is\u0000ideal for rapid solutions that account for acousto-thermal effects, especially\u0000when model parameters and boundary conditions remain constant. In contrast,\u0000Feat-GaN is effective for moderately complex shape groups and applications\u0000where model parameters must be adjusted.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems 解决二维反向散射问题的多频神经博恩迭代法

arXiv - PHYS - Computational Physics Pub Date : 2024-09-02 DOI: arxiv-2409.01315

Daoqi Liu, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu

{"title":"Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems","authors":"Daoqi Liu, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu","doi":"arxiv-2409.01315","DOIUrl":"https://doi.org/arxiv-2409.01315","url":null,"abstract":"In this work, we propose a deep learning-based imaging method for addressing\u0000the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By\u0000combining deep learning technology with EM physical laws, we have successfully\u0000developed a multi-frequency neural Born iterative method (NeuralBIM), guided by\u0000the principles of the single-frequency NeuralBIM. This method integrates\u0000multitask learning techniques with NeuralBIM's efficient iterative inversion\u0000process to construct a robust multi-frequency Born iterative inversion model.\u0000During training, the model employs a multitask learning approach guided by\u0000homoscedastic uncertainty to adaptively allocate the weights of each\u0000frequency's data. Additionally, an unsupervised learning method, constrained by\u0000the physical laws of ISP, is used to train the multi-frequency NeuralBIM model,\u0000eliminating the need for contrast and total field data. The effectiveness of\u0000the multi-frequency NeuralBIM is validated through synthetic and experimental\u0000data, demonstrating improvements in accuracy and computational efficiency for\u0000solving ISP. Moreover, this method exhibits strong generalization capabilities\u0000and noise resistance. The multi-frequency NeuralBIM method explores a novel\u0000inversion method for multi-frequency EM data and provides an effective solution\u0000for the electromagnetic ISP of multi-frequency data.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electronvolt energy resolution with broadband ptychography 电子伏特能量分辨率与宽带层析成像技术

arXiv - PHYS - Computational Physics Pub Date : 2024-09-01 DOI: arxiv-2409.00703

Silvia Cipiccia, Wiebe Stolp, Luca Fardin, Ralf Ziesche, Ingo Manke, Matthieu Boone, Chris Armstrong, Joachim R. Binder, Nicole Bohn, Alessandro Olivo, Darren Batey

{"title":"Electronvolt energy resolution with broadband ptychography","authors":"Silvia Cipiccia, Wiebe Stolp, Luca Fardin, Ralf Ziesche, Ingo Manke, Matthieu Boone, Chris Armstrong, Joachim R. Binder, Nicole Bohn, Alessandro Olivo, Darren Batey","doi":"arxiv-2409.00703","DOIUrl":"https://doi.org/arxiv-2409.00703","url":null,"abstract":"Ptychography is a scanning coherent diffraction imaging technique\u0000successfully applied in the electron, visible and x-ray regimes. One of the\u0000distinct features of ptychography with respect to other coherent diffraction\u0000techniques is its capability of dealing with partial spatial and temporal\u0000coherence via the reconstruction algorithm. Here we focus on the temporal and\u0000clarify the constraints which affect the energy resolution limits of the\u0000ptychographic algorithms. Based on this, we design and perform simulations for\u0000a broadband ptychography in the hard x-ray regime, which enables an energy\u0000resolution down to 1 eV. We benchmark the simulations against experimental\u0000ptychographic data of an NMC battery cathode material, attaining an energy\u0000resolution of 5 eV. We review the results, discuss the limitations, and provide\u0000guidelines for future broadband ptychography experiments, its prospective\u0000application for single acquisition x-ray absorption near edge structure\u0000imaging, magnetic dichroism imaging, and potential impact on achieving\u0000diffraction limited resolutions.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants 通过机器学习辅助提取非谐波力常量，将声子热导率预测速度提高一个数量级

arXiv - PHYS - Computational Physics Pub Date : 2024-08-31 DOI: arxiv-2409.00360

Yagyank Srivastava, Ankit Jain

引用次数: 0

Physics-integrated Neural Network for Quantum Transport Prediction of Field-effect Transistor 用于场效应晶体管量子输运预测的物理集成神经网络

arXiv - PHYS - Computational Physics Pub Date : 2024-08-30 DOI: arxiv-2408.17023

Xiuying Zhang, Linqiang Xu, Jing Lu, Zhaofu Zhang, Lei Shen

{"title":"Physics-integrated Neural Network for Quantum Transport Prediction of Field-effect Transistor","authors":"Xiuying Zhang, Linqiang Xu, Jing Lu, Zhaofu Zhang, Lei Shen","doi":"arxiv-2408.17023","DOIUrl":"https://doi.org/arxiv-2408.17023","url":null,"abstract":"Quantum-mechanics-based transport simulation is of importance for the design\u0000of ultra-short channel field-effect transistors (FETs) with its capability of\u0000understanding the physical mechanism, while facing the primary challenge of the\u0000high computational intensity. Traditional machine learning is expected to\u0000accelerate the optimization of FET design, yet its application in this field is\u0000limited by the lack of both high-fidelity datasets and the integration of\u0000physical knowledge. Here, we introduced a physics-integrated neural network\u0000framework to predict the transport curves of sub-5-nm gate-all-around (GAA)\u0000FETs using an in-house developed high-fidelity database. The transport curves\u0000in the database are collected from literature and our first-principles\u0000calculations. Beyond silicon, we included indium arsenide, indium phosphide,\u0000and selenium nanowires with different structural phases as the FET channel\u0000materials. Then, we built a physical-knowledge-integrated hyper vector neural\u0000network (PHVNN), in which five new physical features were added into the inputs\u0000for prediction transport characteristics, achieving a sufficiently low mean\u0000absolute error of 0.39. In particular, ~98% of the current prediction residuals\u0000are within one order of magnitude. Using PHVNN, we efficiently screened out the\u0000symmetric p-type GAA FETs that possess the same figures of merit with the\u0000n-type ones, which are crucial for the fabrication of homogeneous CMOS\u0000circuits. Finally, our automatic differentiation analysis provides\u0000interpretable insights into the PHVNN, which highlights the important\u0000contributions of our new input parameters and improves the reliability of\u0000PHVNN. Our approach provides an effective method for rapidly screening\u0000appropriate GAA FETs with the prospect of accelerating the design process of\u0000next-generation electronic devices.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boundaries of universality of thermal collisions for atom-atom scattering 原子-原子散射热碰撞的普遍性边界

arXiv - PHYS - Computational Physics Pub Date : 2024-08-30 DOI: arxiv-2409.00273

Xuyang Guo, Kirk W. Madison, James L. Booth, Roman V. Krems

{"title":"Boundaries of universality of thermal collisions for atom-atom scattering","authors":"Xuyang Guo, Kirk W. Madison, James L. Booth, Roman V. Krems","doi":"arxiv-2409.00273","DOIUrl":"https://doi.org/arxiv-2409.00273","url":null,"abstract":"Thermal rate coefficients for some atomic collisions have been observed to be\u0000remarkably independent of the details of interatomic interactions at short\u0000range. This makes these rate coefficients universal functions of the long-range\u0000interaction parameters and masses, which was previously exploited to develop a\u0000self-defining atomic sensor for ambient pressure. Here, we employ rigorous\u0000quantum scattering calculations to examine the response of thermally averaged\u0000rate coefficients for atom-atom collisions to changes in the interaction\u0000potentials. We perform a comprehensive analysis of the universality, and the\u0000boundaries thereof, by treating the quantum scattering observables as\u0000probabilistic predictions determined by a distribution of interaction\u0000potentials. We show that there is a characteristic change of the resulting\u0000distributions of rate coefficients, separating light, few-electron atoms and\u0000heavy, polarizable atoms. We produce diagrams that illustrate the boundaries of\u0000the thermal collision universality at different temperatures and provide\u0000guidance for future experiments seeking to exploit the universality.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Nonlinear System with Machine Learning: Chua and Lorentz Circuits Analyzed 用机器学习探索非线性系统：蔡氏和洛伦兹电路分析

arXiv - PHYS - Computational Physics Pub Date : 2024-08-30 DOI: arxiv-2408.16972

Zhe Wang, Haixia Fan, Jiyuan Zhang, Xiao-Yun Wang

{"title":"Exploring Nonlinear System with Machine Learning: Chua and Lorentz Circuits Analyzed","authors":"Zhe Wang, Haixia Fan, Jiyuan Zhang, Xiao-Yun Wang","doi":"arxiv-2408.16972","DOIUrl":"https://doi.org/arxiv-2408.16972","url":null,"abstract":"Nonlinear circuits serve as crucial carriers and physical models for\u0000investigating nonlinear dynamics and chaotic behavior, particularly in the\u0000simulation of biological neurons. In this study, Chua's circuit and Lorentz\u0000circuit are systematically explored for the first time through machine learning\u0000correlation algorithms. Specifically, the upgraded and optimized SINDy-PI\u0000model, which is based on neural network and symbolic regression algorithm, is\u0000utilized to learn the numerical results of attractors generated by these two\u0000nonlinear circuits. Through error analysis, we examine the effects of the\u0000precision of input data and the amount of data on the algorithmic model. The\u0000findings reveal that when the input data quantity and data precision fall\u0000within a certain range, the algorithm model can effectively recognize and\u0000restore the differential equation expressions corresponding to the two\u0000circuits. Additionally, we test the anti-interference ability of different\u0000circuits and the robustness of the algorithm by introducing noise into the test\u0000data. The results indicate that under the same noise disturbance, the Lorentz\u0000circuit has better noise resistance than Chua's circuit, providing a starting\u0000point for further studying the intrinsic properties and characteristics of\u0000different nonlinear circuits. The above results will not only offer a reference\u0000for the further study of nonlinear circuits and related systems using deep\u0000learning algorithms but also lay a preliminary theoretical foundation for the\u0000study of related physical problems and applications.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exact scattering cross section for lattice-defect scattering of phonons 声子晶格缺陷散射的精确散射截面

arXiv - PHYS - Computational Physics Pub Date : 2024-08-30 DOI: arxiv-2408.17004

Zhun-Yong Ong

{"title":"Exact scattering cross section for lattice-defect scattering of phonons","authors":"Zhun-Yong Ong","doi":"arxiv-2408.17004","DOIUrl":"https://doi.org/arxiv-2408.17004","url":null,"abstract":"The use of structurally complex lattice defects, such as functional groups,\u0000embedded nanoparticles, and nanopillars, to generate phonon scattering is a\u0000popular approach in phonon engineering for thermoelectric applications.\u0000However, the theoretical treatment of this scattering phenomenon remains a\u0000formidable challenge, especially with regards to the determination of the\u0000scattering cross sections and rates associated with such lattice defects. Using\u0000the extended Atomistic Green's Function (AGF) method, we describe how the\u0000numerically exact mode-resolved scattering cross section sigma can be computed\u0000for a phonon scattered by a single lattice defect. We illustrate the generality\u0000and utility of the AGF-based treatment with two examples. In the first example,\u0000we treat the isotopic scattering of phonons in a harmonic chain of atoms . In\u0000the second example, we treat the more complex problem of phonon scattering in a\u0000carbon nanotube (CNT) containing an encapsulated C60 molecule which acts as a\u0000scatterer of the CNT phonons. The application of this method can enable a more\u0000precise characterization of lattice-defect scattering and result in the more\u0000controlled use of nanostructuring and lattice defects in phonon engineering for\u0000thermoelectric applications.","PeriodicalId":501369,"journal":{"name":"arXiv - PHYS - Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142204145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0