Pub Date : 2024-06-25DOI: 10.1016/j.cpc.2024.109288
Bowen Han , Andrei T. Savici , Mingda Li , Yongqiang Cheng
Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. We expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.
Program summary
Program Title: INSPIRED
CPC Library link to program files:https://doi.org/10.17632/8g3s8f9n2p.1
Nature of problem: How to easily and quickly assess the expected INS spectra for a given crystal structure has been a major challenge in the INS user community. It is a main bottleneck affecting almost every stage of the workflow, from experimental design and steering to data analysis and interpretation. The widely used approach involving DFT calculations is time-consuming, requires advanced computing resources, and has a steep learning curve. With the growing power of neutron sources and more hig
非弹性中子散射(INS)在探测原子如何振动以及振动如何传播和相互作用方面具有独特的优势。这些动态信息对于了解各种材料特性至关重要,从热容量、热导率、相变、化学反应到更奇特的量子行为。对 INS 图谱的分析和解释通常从样品的模型结构开始,然后通过一系列计算获得模拟图谱,并与实验结果进行比较。进行此类计算的传统方法通常需要大量时间、计算资源和专业知识。在此,我们介绍一种名为 INSPIRED(非弹道中子散射瞬时结果和实验设计预测)的新程序,用户只需点击几下,就能在个人电脑上以几种不同的方式快速进行 INS 模拟,而晶体结构则是唯一的输入文件。具体来说,用户可以选择预先训练好的对称性感知神经网络(与自动编码器相结合)来预测任何给定结构的声子态密度(DOS)、一维 S(E) 和二维 S(|Q|,E) 光谱。还可以从数据库(包含 12,000 多种晶体)中选择现有的密度泛函理论(DFT)计算,快速获得单晶体和粉末的模拟 INS 光谱。还可以使用预先训练好的通用机器学习力场来松弛给定的晶体结构,计算声子色散和 DOS,进而计算 INS 光谱。所有这些功能都是通过 PyQt 图形用户界面实现的。我们希望这些新工具能惠及广大用户群体,并显著提高 INS 实验设计、执行和数据分析的效率:INSPIREDCPC 库与程序文件的链接:https://doi.org/10.17632/8g3s8f9n2p.1Developer's repository 链接:https://github.com/cyqjh/inspired(软件)、https://doi.org/10.5281/zenodo.11478889(数据库、模型文件和虚拟机设备文件)许可条款:MIT 编程语言:Python问题性质:如何方便快捷地评估给定晶体结构的预期 INS 光谱一直是 INS 用户社区面临的主要挑战。从实验设计和指导到数据分析和解释,它几乎是影响工作流程每个阶段的主要瓶颈。广泛使用的 DFT 计算方法耗时长,需要先进的计算资源,而且学习曲线陡峭。随着中子源和更多高通量 INS 实验的日益强大,迫切需要解决这一问题,最好是利用机器学习和人工智能的最新发展:我们采用数据驱动的方法来解决这个问题。我们训练了一个对称感知神经网络,从晶体结构直接预测一维光谱或潜在空间向量,然后解码重建二维光谱。用于训练的数据库包含一万多个晶体,也可用于计算单晶体和粉末的 INS 光谱。最近出现的通用机器学习力场为大幅加速模拟提供了另一个途径。所有这些解决方案都是通过图形用户界面实现的,因此没有建模/编程背景或无法使用强大计算机的用户也能轻松运行工作流程。
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Pub Date : 2024-06-21DOI: 10.1016/j.cpc.2024.109285
Francesco De Vanna , Giacomo Baldan
We present URANOS-2.0, the second major release of our massively parallel, GPU-accelerated solver for compressible wall flow applications. This latest version represents a significant leap forward in our initial tool, which was launched in 2023 (De Vanna et al. [1]), and has been specifically optimized to take full advantage of the opportunities offered by the cutting-edge pre-exascale architectures available within the EuroHPC JU. In particular, URANOS-2.0 emphasizes portability and compatibility improvements with the two top-ranked supercomputing architectures in Europe: LUMI and Leonardo. These systems utilize different GPU architectures, AMD and NVIDIA, respectively, which necessitates extensive efforts to ensure seamless usability across their distinct structures. In pursuit of this objective, the current release adheres to the OpenACC standard. This choice not only facilitates efficient utilization of the full potential inherent in these extensive GPU-based architectures but also upholds the principles of vendor neutrality, a distinctive characteristic of URANOS solvers in the CFD solvers' panorama. However, the URANOS-2.0 version goes beyond the goals of improving usability and portability; it introduces performance enhancements and restructures the most demanding computational kernels. This translates into a 2× speedup over the same architecture. In addition to its enhanced single-GPU performance, the present solver release demonstrates very good scalability in multi-GPU environments. URANOS-2.0, in fact, achieves strong scaling efficiencies of over 80% across 64 compute nodes (256 GPUs) for both LUMI and Leonardo. Furthermore, its weak scaling efficiencies reach approximately 95% and 90% on LUMI and Leonardo, respectively, when up to 256 nodes (1024 GPUs) are considered. These significant performance advancements position URANOS-2.0 as a state-of-the-art supercomputing platform tailored for compressible wall turbulence applications, establishing the solver as an integrated tool for various aerospace and energy engineering applications, which can span from direct numerical simulations, wall-resolved large eddy simulations, up to most recent wall-modeled large eddy simulations.
Program summary
Program title: Unsteady Robust All-around Navier-StOkes Solver (URANOS)
CPC Library link to program files:https://doi.org/10.17632/pw5hshn9k6.2
{"title":"URANOS-2.0: Improved performance, enhanced portability, and model extension towards exascale computing of high-speed engineering flows","authors":"Francesco De Vanna , Giacomo Baldan","doi":"10.1016/j.cpc.2024.109285","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109285","url":null,"abstract":"<div><p>We present URANOS-2.0, the second major release of our massively parallel, GPU-accelerated solver for compressible wall flow applications. This latest version represents a significant leap forward in our initial tool, which was launched in 2023 (De Vanna et al. <span>[1]</span>), and has been specifically optimized to take full advantage of the opportunities offered by the cutting-edge pre-exascale architectures available within the EuroHPC JU. In particular, URANOS-2.0 emphasizes portability and compatibility improvements with the two top-ranked supercomputing architectures in Europe: LUMI and Leonardo. These systems utilize different GPU architectures, AMD and NVIDIA, respectively, which necessitates extensive efforts to ensure seamless usability across their distinct structures. In pursuit of this objective, the current release adheres to the OpenACC standard. This choice not only facilitates efficient utilization of the full potential inherent in these extensive GPU-based architectures but also upholds the principles of vendor neutrality, a distinctive characteristic of URANOS solvers in the CFD solvers' panorama. However, the URANOS-2.0 version goes beyond the goals of improving usability and portability; it introduces performance enhancements and restructures the most demanding computational kernels. This translates into a 2× speedup over the same architecture. In addition to its enhanced single-GPU performance, the present solver release demonstrates very good scalability in multi-GPU environments. URANOS-2.0, in fact, achieves strong scaling efficiencies of over 80% across 64 compute nodes (256 GPUs) for both LUMI and Leonardo. Furthermore, its weak scaling efficiencies reach approximately 95% and 90% on LUMI and Leonardo, respectively, when up to 256 nodes (1024 GPUs) are considered. These significant performance advancements position URANOS-2.0 as a state-of-the-art supercomputing platform tailored for compressible wall turbulence applications, establishing the solver as an integrated tool for various aerospace and energy engineering applications, which can span from direct numerical simulations, wall-resolved large eddy simulations, up to most recent wall-modeled large eddy simulations.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> Unsteady Robust All-around Navier-StOkes Solver (URANOS)</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/pw5hshn9k6.2</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://github.com/uranos-gpu/uranos-gpu</span><svg><path></path></svg>, <span>https://github.com/uranos-gpu/uranos-gpu/tree/v2.0</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> BSD License 2.0</p><p><em>Programming language:</em> Modern Fortran, OpenACC, MPI</p><p><em>Nature of problem:</em> Solving the compressible Navier-Stokes equations in a three-dimensional Cartesian framework.</p><p><em>Solution method:</em> Convective terms ar","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S001046552400208X/pdfft?md5=7a6e04c9a2b65cdb6b3bf373bd81aed0&pid=1-s2.0-S001046552400208X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1016/j.cpc.2024.109284
Jiaxing Zhao , Shuzhe Shi
The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schrödinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and accuracy of this method, we also present an example of solving the Dirac equation under the presence of an external scalar potential and a constant magnetic field, with source code publicly available.
{"title":"A numerical algorithm for solving the coupled Schrödinger equations using inverse power method","authors":"Jiaxing Zhao , Shuzhe Shi","doi":"10.1016/j.cpc.2024.109284","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109284","url":null,"abstract":"<div><p>The inverse power method is a numerical algorithm to obtain the eigenvectors of a matrix. In this work, we develop an iteration algorithm, based on the inverse power method, to numerically solve the Schrödinger equation that couples an arbitrary number of components. Such an algorithm can also be applied to the multi-body systems. To show the power and accuracy of this method, we also present an example of solving the Dirac equation under the presence of an external scalar potential and a constant magnetic field, with source code publicly available.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1016/j.cpc.2024.109283
Guolin Wan , Yuhui Li , Ting Lai , Peixuan Li , Yongqian Zhu , Jingyu Yang , Yan-Fang Zhang , Jinbo Pan , Shixuan Du
Exploring magnetic configurations of magnets often involves utilizing the four-state method to obtain the magnetic interaction matrix, and Monte Carlo method to simulate spin textures and phase transition processes. However, computing the interaction matrix between magnetic atoms using the four-state method requires plenty of individual calculations. Despite manual simplifying the number of individual calculations based on material's symmetry is possible, there remains a necessity for an automated approach to streamline the process for high-throughput screening of magnetic materials. Meanwhile, the traditional sequential Monte Carlo simulation encounters challenges of low efficiency and long time consuming in dealing with large systems. Furthermore, the prior parallelism in the Heisenberg model was limited to parallel computation of the system's energy or run several replicas in parallel. Hence, in our pursuit of comprehensive parallelization for the Heisenberg model, we have introduced a novel adaptation of the checkerboard algorithm, enabling a fully parallelizable simulation of the Heisenberg model. To address these problems, we have developed Sym4state.jl, a program specifically designed to simplify the computation of magnetic interaction matrix and simulate spin textures under various environmental conditions. This program, available as a Julia package, can be freely accessed at https://github.com/A-LOST-WAPITI/Sym4state.jl.
Program summary
Program title: Sym4state.jl
CPC Library link to program files:https://doi.org/10.17632/s6dkmgrjfw.1
Nature of problem: Employing the four-state method to calculate magnetic interaction matrix for magnetic materials can be simplified based on material symmetry, however, there is a lack of automated approach to streamline the simplification. Additionally, the commonly used Metropolis method for simulating magnetic texture can only make parallel computation of the system's energy or run several replicas in parallel, which could hardly boost the performance when simulating the large-scale magnetic textures.
Solution method: We simplify the four-state method calculations by utilizing the principles of energy invariance under symmetry operations and time reversal operations. To enhance the efficiency of the Metropolis algorithm, we have designed a strategy to divide the entire 2D lattice into multiple domains. We then execute the Metropolis algorithm in parallel for each individual domain, thereby improving the overall computational efficiency.
Additional comments including restrictions and unusual features: While the
{"title":"Sym4state.jl: An efficient computation package for magnetic materials","authors":"Guolin Wan , Yuhui Li , Ting Lai , Peixuan Li , Yongqian Zhu , Jingyu Yang , Yan-Fang Zhang , Jinbo Pan , Shixuan Du","doi":"10.1016/j.cpc.2024.109283","DOIUrl":"10.1016/j.cpc.2024.109283","url":null,"abstract":"<div><p>Exploring magnetic configurations of magnets often involves utilizing the four-state method to obtain the magnetic interaction matrix, and Monte Carlo method to simulate spin textures and phase transition processes. However, computing the interaction matrix between magnetic atoms using the four-state method requires plenty of individual calculations. Despite manual simplifying the number of individual calculations based on material's symmetry is possible, there remains a necessity for an automated approach to streamline the process for high-throughput screening of magnetic materials. Meanwhile, the traditional sequential Monte Carlo simulation encounters challenges of low efficiency and long time consuming in dealing with large systems. Furthermore, the prior parallelism in the Heisenberg model was limited to parallel computation of the system's energy or run several replicas in parallel. Hence, in our pursuit of comprehensive parallelization for the Heisenberg model, we have introduced a novel adaptation of the checkerboard algorithm, enabling a fully parallelizable simulation of the Heisenberg model. To address these problems, we have developed <span>Sym4state.jl</span>, a program specifically designed to simplify the computation of magnetic interaction matrix and simulate spin textures under various environmental conditions. This program, available as a Julia package, can be freely accessed at <span>https://github.com/A-LOST-WAPITI/Sym4state.jl</span><svg><path></path></svg>.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> Sym4state.jl</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/s6dkmgrjfw.1</span><svg><path></path></svg></p><p><em>Developer's repository link:</em> <span>https://github.com/A-LOST-WAPITI/Sym4state.jl</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> MIT</p><p><em>Programming language:</em> Julia</p><p><em>Nature of problem:</em> Employing the four-state method to calculate magnetic interaction matrix for magnetic materials can be simplified based on material symmetry, however, there is a lack of automated approach to streamline the simplification. Additionally, the commonly used Metropolis method for simulating magnetic texture can only make parallel computation of the system's energy or run several replicas in parallel, which could hardly boost the performance when simulating the large-scale magnetic textures.</p><p><em>Solution method:</em> We simplify the four-state method calculations by utilizing the principles of energy invariance under symmetry operations and time reversal operations. To enhance the efficiency of the Metropolis algorithm, we have designed a strategy to divide the entire 2D lattice into multiple domains. We then execute the Metropolis algorithm in parallel for each individual domain, thereby improving the overall computational efficiency.</p><p><em>Additional comments including restrictions and unusual features:</em> While the","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109281
Prakash Pandey , Sudhir K. Pandey
Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach. The present version of the code is interfaced with the WIEN2k, Elk, and ABINIT packages. In order to benchmark the code, six different types of materials are considered, which include (i) FeSi, a well-known double-Weyl point; (ii) LiCaAs, a half-Heusler single-type-I Weyl topological phonon (TP); and (iii) ScZn, coexisting nodal-line and nodal-ring TPs; (iv) TiS, six pairs of bulk Weyl nodes; (v) CdTe, type-II Weyl phonons; (vi) CsTe, coexisting TP and quadratic contact TP. In FeSi, the node points are found at and R high symmetric points. Also, there are 21 energy values at which the node points are situated, corresponding to the full Brillouin zone. For LiCaAs, the previously reported literature claims that there is a node point along the W-X high symmetry direction between the highest longitudinal acoustic and the lowest transverse optical branch, while in our DFT calculations, a gap of 0.17 meV is found. Furthermore, ScZn hosts six nodal-ring TPs phonons at the boundary planes of the Brillouin zone in the vicinity of the M high-symmetric point. In addition to this, straight-line TPs are also found along the Γ-X and Γ-R high symmetric directions. Moreover, for TiS, six Weyl node points (WP1, WP2, WP3, WP4, WP5 and WP6) are found along H-K high-symmetric direction. In CdTe, it is found that Weyl points are located along the X-W high-symmetry direction. In the case of CsTe, a TP and a quadratic contact TP are found along the Γ-X direction and at the R high-symmetry point, respectively. The results obtained from the PH-NODE code are in good agreement with the experimentally and theoretically reported data for each material.
Program summary
Program title: PH-NODE
CPC Library link to program files:https://doi.org/10.17632/sjydzn49nw.1
Licensing provisions: GNU General Public License 3.0
Programming language: Python 3
External routines/libraries: Math, Time, NumPy, SciPy
Nature of problem: Searching for the phonon-node points corresponding to the given number of phonon-branch using Nelder-Mead's simplex approach.
Solution method: We present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material
{"title":"PH-NODE: A DFPT and finite displacement supercell based python code for searching nodes in topological phononic materials","authors":"Prakash Pandey , Sudhir K. Pandey","doi":"10.1016/j.cpc.2024.109281","DOIUrl":"https://doi.org/10.1016/j.cpc.2024.109281","url":null,"abstract":"<div><p>Exploring the topological physics of phonons is fundamentally important for understanding various practical applications. Here, we present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material through a first-principles approach. The present version of the code is interfaced with the WIEN2k, Elk, and ABINIT packages. In order to benchmark the code, six different types of materials are considered, which include (i) FeSi, a well-known double-Weyl point; (ii) LiCaAs, a half-Heusler single-type-I Weyl topological phonon (TP); and (iii) ScZn, coexisting nodal-line and nodal-ring TPs; (iv) TiS, six pairs of bulk Weyl nodes; (v) CdTe, type-II Weyl phonons; (vi) CsTe, coexisting TP and quadratic contact TP. In FeSi, the node points are found at <span><math><mi>Γ</mi><mo>(</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>)</mo></math></span> and R<span><math><mo>(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo>)</mo></math></span> high symmetric points. Also, there are 21 energy values at which the node points are situated, corresponding to the full Brillouin zone. For LiCaAs, the previously reported literature claims that there is a node point along the W-X high symmetry direction between the highest longitudinal acoustic and the lowest transverse optical branch, while in our DFT calculations, a gap of 0.17 meV is found. Furthermore, ScZn hosts six nodal-ring TPs phonons at the boundary planes of the Brillouin zone in the vicinity of the M high-symmetric point. In addition to this, straight-line TPs are also found along the Γ-X and Γ-R high symmetric directions. Moreover, for TiS, six Weyl node points (WP1, WP2, WP3, WP4, WP5 and WP6) are found along H-K high-symmetric direction. In CdTe, it is found that Weyl points are located along the X-W high-symmetry direction. In the case of CsTe, a TP and a quadratic contact TP are found along the Γ-X direction and at the R high-symmetry point, respectively. The results obtained from the PH-NODE code are in good agreement with the experimentally and theoretically reported data for each material.</p></div><div><h3>Program summary</h3><p><em>Program title:</em> PH-NODE</p><p><em>CPC Library link to program files:</em> <span>https://doi.org/10.17632/sjydzn49nw.1</span><svg><path></path></svg></p><p><em>Licensing provisions:</em> GNU General Public License 3.0</p><p><em>Programming language:</em> Python 3</p><p><em>External routines/libraries:</em> Math, Time, NumPy, SciPy</p><p><em>Nature of problem:</em> Searching for the phonon-node points corresponding to the given number of phonon-branch using Nelder-Mead's simplex approach.</p><p><em>Solution method:</em> We present a density-functional perturbation theory and finite displacement supercell based Python 3 software package called PH-NODE for efficiently computing phonon nodes present in real material","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109277
Ali Bavarchee
This article presents a novel machine learning approach for enhancing particle identification (PID) systems in high-energy physics (HEP) experiments. The proposed method utilizes a hybrid model that combines a deep neural network (DNN) and a random forest regressor (RFR), leveraging their complementary strengths. This approach achieves robust performance, leading to significantly improved particle discrimination and cleaner data for physics analysis. Our evaluation demonstrates a marked increase in PID system precision, highlighting the model's potential to optimize PID tasks in complex high-energy physics settings. By improving identification efficiency and reducing misidentification rates, this hybrid deep learning model offers valuable advancements for the field of particle physics.
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Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109279
Hareesh Chundayil , Vinay P. Majety , Armin Scrinzi
We present a new implementation of the hybrid antisymmetrized Coupled Channels (haCC) method in the framework of the tRecX (Scrinzi, 2022 [6]). The method represents atomic and molecular multi-electron functions by combining CI functions, Gaussian molecular orbitals, and a numerical single-electron basis. It is suitable for describing high harmonic generation and the strong-field dynamics of ionization. Fully differential photoemission spectra are computed by the tSurff method. The theoretical background of haCC is outlined and key improvements compared to its original formulation are highlighted. We discuss control of over-completeness resulting from the joint use of the numerical basis and Gaussian molecular orbitals by pseudo-inverses based on the Woodbury formula. Further new features of this tRecX release are the iSurff method, new input features, and the AMOS gateway interface. The mapping of haCC into the tRecX framework for solving the time-dependent Schrödinger equation is shown. Use, performance, and accuracy of haCC are discussed on the examples of high-harmonic generation and strong-field photo-emission by short laser pulses impinging on the Helium atom and on the linear molecules and CO.
Program summary
Program title: tRecX — time-dependent Recursive indeXing (tRecX=tSurff+irECS)
CPC Library link to program files:https://doi.org/10.17632/m9g2jc82sw.1
Journal Reference of previous version: A. Scrinzi, Comp. Phys. Comm., 270:108146, 2022.
Does the new version supersede the previous version: Yes
Reasons for the new version: Major new functionality: haCC — hybrid antisymmetrized coupled channels method
Summary of revisions: Main additions are haCC and iSurff. Code usage and compilation were improved.
Nature of problem: tRecX is a general solver for time-dependent Schrödinger-like problems, with applications mostly in strong field and attosecond physics. There are no technical restrictions on the spatial dimension of the problem with up to 6 spatial dimensions realized in the strong-field double ionization of Helium. Gaussian-based quantum chemical multi-electron atomic and molecular structure can be combined with the numerical basis. A selection of coordinate systems is available and any Hamiltonian involving up to second derivatives and arbitrary up to three dimensional potentials can be defined on input by simple scripts.
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Pub Date : 2024-06-12DOI: 10.1016/j.cpc.2024.109280
Ulrich D. Jentschura , Ludovico T. Giorgini
The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit of the summation from the partial sums of a given, slowly convergent input series. Here, we show that, for a particular choice of the abscissas used for the extrapolation, one can replace the recursive Neville scheme by a simple one-step transformation, while also obtaining access to subleading terms for the transformed series after convergence acceleration. The matrix-based, unified formulas allow one to estimate the rate of convergence of the partial sums of the input series to their limit. In particular, Bethe logarithms for hydrogen are calculated to 100 decimal digits. Generalizations of the method to series whose remainder terms can be expanded in terms of inverse factorial series, or series with half-integer powers, are also discussed.
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Pub Date : 2024-06-11DOI: 10.1016/j.cpc.2024.109278
Claudia Fevola , Sebastian Mizera , Simon Telen
We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Our algorithms build on the Julia package Landau.jl and are implemented in the new open-source package PLD.jl available at https://mathrepo.mis.mpg.de/PLD/.
Program summary
Program title:PLD.jl
CPC Library link to program files:https://doi.org/10.17632/7h5644mm4n.1
Supplementary material: The repository includes the source code with documentation (PLD_code.zip), a jupyter notebook tutorial providing installation and usage instructions (PLD_notebook.zip), a database containing the output of our algorithm on 114 examples of Feynman integrals (PLD_database.zip).
Nature of problem: A fundamental challenge in scattering amplitude is to determine the values of complexified kinematic invariants for which an amplitude can develop singularities. Bjorken, Landau, and Nakanishi wrote a system of polynomial constraints, nowadays known as the Landau equations. This project aims to rigorously revisit the Landau analysis of the singularity locus of Feynman integrals with a practical view towards explicit computations.
Solution method: We define the principal Landau determinant (PLD), which is a variety inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ). We conjecture that it provides a subset of the singularity locus, and we implement effective algorithms to compute its defining equation explicitly.
References: OSCAR [1], HomotopyContinuation.jl [2], Landau.jl [3]
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Pub Date : 2024-06-11DOI: 10.1016/j.cpc.2024.109275
Chen Fan, Muhammad Aamir Ali, Zhiyue Zhang
Many practical problems, including modeling composite materials, nuclear waste disposal, oil reservoir simulations, and flows in porous medium, commonly involve interface problems. However, the solution to interface problems with discontinuous coefficients of PDEs using fully decoupled numerical methods is challenging. The main objective is to solve the interface problems with fully decoupled numerical methods. This paper proposes an efficient decoupled numerical method for solving degenerate interface problems with double singularities. First, we divide the whole domain into singular and regular subdomains. Then, we use the Deep Neural Network (DNN) to find the solution on the singular subdomain and approximate the solution on the regular subdomain using the finite difference method. The scheme combines the solutions of singular and regular subdomains, which is an exciting idea. The key to the new approach is to split nonlinear degenerate partial differential equations with an interface into two independent boundary value problems based on deep learning. In this way, the expansion of the solution on the singular domain does not contain undetermined parameters, and two independent boundary value problems can be solved with any well-known traditional numerical methods. The main advantage of the proposed scheme is that we not only get the order of convergence of the degenerate interface problems on the whole domain, but we also can calculate VERY BIG jump ratio (such as or ) for the interface problems including degenerate and non-degenerate cases. Finally, with examples, we demonstrate the efficiency and accuracy of methods for 1 and 2D problems. It is also interesting that the proposed method is valid for the interface problems with degenerate and non-degenerate cases, we show it with some examples.
许多实际问题,包括复合材料建模、核废料处理、油藏模拟和多孔介质中的流动,通常都涉及界面问题。然而,使用完全解耦数值方法求解具有不连续 PDE 系数的界面问题具有挑战性。利用完全解耦数值方法解决界面问题是主要目标。本文提出了一种高效的解耦数值方法,用于求解具有双重奇点的退化界面问题。首先,我们将整个域划分为奇异子域和规则子域。然后,利用深度神经网络(DNN)求奇异子域的解,并用有限差分法近似求规则子域的解。该方案结合了奇异子域和规则子域的解,这是一个令人兴奋的想法。新方法的关键在于基于深度学习,将带有界面的非线性退化偏微分方程拆分为两个独立的边界值问题。这样,奇异域上的解的展开不包含未确定的参数,两个独立的边界值问题可以用任何著名的传统数值方法求解。所提方案的主要优势在于,我们不仅能得到退化界面问题在整个域上的收敛阶数,还能计算出包括退化和非退化情况在内的界面问题的 VERY BIG 跳跃比(如 1012:1 或 1:1012)。最后,我们通过实例展示了针对一维和二维问题的方法的效率和准确性。同样有趣的是,所提出的方法对于退化和非退化情况下的界面问题也是有效的,我们用一些例子来证明这一点。
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