Pub Date : 2024-11-26DOI: 10.1016/j.cpc.2024.109447
Byungkyun Kang , Patrick Semon , Corey Melnick , Mancheon Han , Seongjun Mo , Hoonkyung Lee , Gabriel Kotliar , Sangkook Choi
ComDMFT is a parallel computational package designed to study the electronic structure of correlated quantum materials from first principles. Our approach is based on the combination of first-principles methods and dynamical mean field theories. In version 2.0, we implemented fully-diagrammatic GW+EDMFT from first-principles self-consistently. In this approach, correlated electrons are treated within full GW+EDMFT and the rest are treated within full-GW, seamlessly. This implementation enables the electronic structure calculation of quantum materials with weak, intermediate, and strong electron correlation without prior knowledge of the degree of electron correlation.
{"title":"ComDMFT v.2.0: Fully self-consistent ab initio GW+EDMFT for the electronic structure of correlated quantum materials","authors":"Byungkyun Kang , Patrick Semon , Corey Melnick , Mancheon Han , Seongjun Mo , Hoonkyung Lee , Gabriel Kotliar , Sangkook Choi","doi":"10.1016/j.cpc.2024.109447","DOIUrl":"10.1016/j.cpc.2024.109447","url":null,"abstract":"<div><div>ComDMFT is a parallel computational package designed to study the electronic structure of correlated quantum materials <em>from first principles</em>. Our approach is based on the combination of <em>first-principles</em> methods and dynamical mean field theories. In version 2.0, we implemented fully-diagrammatic GW+EDMFT <em>from first-principles</em> self-consistently. In this approach, correlated electrons are treated within full GW+EDMFT and the rest are treated within full-GW, seamlessly. This implementation enables the electronic structure calculation of quantum materials with weak, intermediate, and strong electron correlation without prior knowledge of the degree of electron correlation.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"308 ","pages":"Article 109447"},"PeriodicalIF":7.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747818","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 : 2024-11-22DOI: 10.1016/j.cpc.2024.109444
Z. Ahmed , R.S. Evans , I. Goel , G.M. Huber , S.J.D. Kay , W.B. Li , L. Preet , A. Usman
There is increasing interest in deep exclusive meson production (DEMP) reactions, as they provide access to Generalized Parton Distributions over a broad kinematic range, and are the only means of measuring pion and kaon charged electric form factors at high . Such investigations are a particularly useful tool in the study of hadronic structure in QCD's transition regime from long-distance interactions described in terms of meson-nucleon degrees of freedom, to short-distance interactions governed by hard quark-gluon degrees of freedom. To assist the planning of future experimental investigations of DEMP reactions in this transition regime, such as at Jefferson Lab and the Electron-Ion Collider (EIC), we have written a special purpose event generator, DEMPgen. Currently, DEMPgen can generate the following reactions: t-channel , , and from a polarized 3He target. DEMPgen is modular in form, so that additional reactions can be added over time.
The generator produces kinematically-complete reaction events which are absolutely-normalized, so that projected event rates can be predicted, and detector resolution requirements studied. The event normalization is based on parameterizations of theoretical models, appropriate to the kinematic regime under study. Both fixed target modes and collider beam modes are supported. This paper presents the structure of the generator, the model parameterizations used for absolute event weighting, the kinematic distributions of the generated particles, some initial results using the generator, and instructions for its use.
{"title":"DEMPgen: Physics event generator for Deep Exclusive Meson Production at Jefferson Lab and the EIC","authors":"Z. Ahmed , R.S. Evans , I. Goel , G.M. Huber , S.J.D. Kay , W.B. Li , L. Preet , A. Usman","doi":"10.1016/j.cpc.2024.109444","DOIUrl":"10.1016/j.cpc.2024.109444","url":null,"abstract":"<div><div>There is increasing interest in deep exclusive meson production (DEMP) reactions, as they provide access to Generalized Parton Distributions over a broad kinematic range, and are the only means of measuring pion and kaon charged electric form factors at high <span><math><msup><mrow><mi>Q</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Such investigations are a particularly useful tool in the study of hadronic structure in QCD's transition regime from long-distance interactions described in terms of meson-nucleon degrees of freedom, to short-distance interactions governed by hard quark-gluon degrees of freedom. To assist the planning of future experimental investigations of DEMP reactions in this transition regime, such as at Jefferson Lab and the Electron-Ion Collider (EIC), we have written a special purpose event generator, DEMPgen. Currently, DEMPgen can generate the following reactions: <em>t</em>-channel <span><math><mi>p</mi><mo>(</mo><mi>e</mi><mo>,</mo><msup><mrow><mi>e</mi></mrow><mrow><mo>′</mo></mrow></msup><msup><mrow><mi>π</mi></mrow><mrow><mo>+</mo></mrow></msup><mo>)</mo><mi>n</mi></math></span>, <span><math><mi>p</mi><mo>(</mo><mi>e</mi><mo>,</mo><msup><mrow><mi>e</mi></mrow><mrow><mo>′</mo></mrow></msup><msup><mrow><mi>K</mi></mrow><mrow><mo>+</mo></mrow></msup><mo>)</mo><mi>Λ</mi><mo>[</mo><msup><mrow><mi>Σ</mi></mrow><mrow><mn>0</mn></mrow></msup><mo>]</mo></math></span>, and <span><math><mover><mrow><mi>n</mi></mrow><mrow><mo>→</mo></mrow></mover><mo>(</mo><mi>e</mi><mo>,</mo><msup><mrow><mi>e</mi></mrow><mrow><mo>′</mo></mrow></msup><msup><mrow><mi>π</mi></mrow><mrow><mo>−</mo></mrow></msup><mo>)</mo><mi>p</mi></math></span> from a polarized <sup>3</sup>He target. DEMPgen is modular in form, so that additional reactions can be added over time.</div><div>The generator produces kinematically-complete reaction events which are absolutely-normalized, so that projected event rates can be predicted, and detector resolution requirements studied. The event normalization is based on parameterizations of theoretical models, appropriate to the kinematic regime under study. Both fixed target modes and collider beam modes are supported. This paper presents the structure of the generator, the model parameterizations used for absolute event weighting, the kinematic distributions of the generated particles, some initial results using the generator, and instructions for its use.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"308 ","pages":"Article 109444"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722552","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 : 2024-11-22DOI: 10.1016/j.cpc.2024.109446
Kai Töpfer , Luis Itza Vazquez-Salazar , Markus Meuwly
<div><div>With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce <span>Asparagus</span>, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. <span>Asparagus</span> combines capabilities of initial data sampling with interfaces to <em>ab initio</em> calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of <span>Asparagus</span> is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>Asparagus</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/9w9xw7mp2h.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/MMunibas/Asparagus</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> Python</div><div><em>Supplementary material:</em> Access to Documentation at <span><span>https://asparagus-bundle.readthedocs.io</span><svg><path></path></svg></span></div><div><em>Nature of problem:</em> Constructing machine-learning (ML) based potential energy surfaces (PESs) for atomistic simulations is a multi-step process that requires a broad knowledge in quantum chemistry, nuclear dynamics and programming. So far, efforts mainly focused on developing and improving ML model architectures. However, there was less effort spent on providing tools for <em>consistent and reproducible workflows</em> that support the construction of ML-PES for a variety of chemical systems for the broader science community.</div><div><em>Solution method:</em> <span>Asparagus</span> is a program package written in Python that provides a streamlined and extensible workflow with a user-friendly command structure to support the construction of ML-PESs. This is achieved by bundling and linking data generation and sampling techniques, data management, model training, testing and evaluation tools into one modular, comprehensive workflow including interfaces to other simulation packages for the application of
随着机器学习(ML)技术在科学界的应用,构建 ML 势能面(ML-PES)已成为物理和化学领域的标准流程。迄今为止,ML-PES 模型构建的改进都是独立进行的,这给新用户带来了难以克服的初始障碍,并使结果的可重复性变得更加复杂。为了降低广泛使用 ML-PES 的门槛,我们推出了 Asparagus 软件包,该软件包将不同部分整合为一个统一的实施方案,允许在用户指导下自主构建 ML-PES 模型。Asparagus 将初始数据采样功能与 ab initio 计算程序接口、ML 模型训练、模型评估及其在 ASE 或 CHARMM 等其他代码中的应用相结合。该代码的功能在不同的示例中进行了说明,包括小分子动力学、有机金属化合物中反应势的表示以及周期性表面结构上的原子扩散。Asparagus 的模块化框架允许简单实现更多与 ML 相关的方法和模型,从而为用户提供最先进 ML 技术的持续友好访问:AsparagusCPC Library 程序文件链接:https://doi.org/10.17632/9w9xw7mp2h.1Developer's repository 链接:https://github.com/MMunibas/AsparagusLicensing provisions:编程语言:MITPython补充材料:Access to Documentation at https://asparagus-bundle.readthedocs.ioNature 问题:为原子模拟构建基于机器学习(ML)的势能面(PES)是一个多步骤过程,需要量子化学、核动力学和编程方面的广泛知识。迄今为止,人们主要致力于开发和改进 ML 模型架构。然而,在为更广泛的科学界提供一致且可重现的工作流程工具,以支持构建各种化学系统的 ML-PES 方面,所做的努力却较少:Asparagus 是一个用 Python 编写的程序包,它提供了一个简化的、可扩展的工作流程和用户友好的命令结构,以支持 ML-PES 的构建。它将数据生成和采样技术、数据管理、模型训练、测试和评估工具捆绑并连接到一个模块化的综合工作流程中,包括与其他模拟软件包的接口,从而实现 ML-PESs 的应用。Asparagus 降低了入门门槛,特别是对新用户而言,它支持生成和调整 ML-PES,从而使用户更加关注化学系统的物理化学评估或分子动力学模拟应用:Asparagus 是用 Python 编写的模块化软件包,为进一步扩展和维护提供了底层结构。目前,基于使用 PyTorch Python 软件包的消息传递神经网络(NN)模型的方法已经可用。可以添加新的模型,并为已实施的模块提供接口。神经网络架构和超参数存储在一个全局配置模块中,并以 json 文件形式保存。除了必要的输入信息外,如果没有特别定义,则使用默认输入参数,这样不仅可以快速设置构建 ML-PES 模型,还可以根据具体需要进行微调。
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Pub Date : 2024-11-22DOI: 10.1016/j.cpc.2024.109442
Jiayin Lu , Chris H. Rycroft
We present TriMe++, a multi-threaded software library designed for generating two-dimensional meshes for intricate geometric shapes using the Delaunay triangulation. Multi-threaded parallel computing is implemented throughout the meshing procedure, making it suitable for fast generation of large-scale meshes. Three iterative meshing algorithms are implemented: the DistMesh algorithm, the centroidal Voronoi diagram meshing, and a hybrid of the two. We compare the performance of the three meshing methods in TriMe++, and show that the hybrid method retains the advantages of the other two. The software library achieves significant parallel speedup when generating large-scale meshes containing between 104 to 107 points. TriMe++ can handle complicated geometries and generates adaptive meshes of high quality.
Program summary
Program title:TriMe++
CPC Library link to program files:https://doi.org/10.17632/jxcsxtywtw.1
Nature of problem: Multi-threaded geometry meshing in two dimension using the Delaunay triangulation
Solution method: The TriMe++ library is built around several C++ classes that follows a structured meshing pipeline. During initialization, the shape_2d class reads the geometry input and generates a signed distance field using a grid-based data structure to represent the shape. The sizing_2d class subsequently produces adaptive element sizing and density fields for the mesh. It uses an adaptive quad-tree data structure, enabling efficient refinement of sizing and density values in areas with complex geometries. In the meshing procedure, the parallel_meshing_2d class iteratively improves point positions in the mesh. In each meshing iteration, the multi-threaded Voro++ library generates the Delaunay triangulation of the points. Users can select from three meshing algorithms, the DistMesh algorithm in the mesh_alg_2d_dm class, the centroidal Voronoi diagram meshing algorithm in the mesh_alg_2d_cvd class, and a hybrid method of the two in the mesh_alg_2d_hybrid class Throughout this meshing workflow, we use OpenMP for multi-threaded parallel computations.
{"title":"TriMe++: Multi-threaded triangular meshing in two dimensions","authors":"Jiayin Lu , Chris H. Rycroft","doi":"10.1016/j.cpc.2024.109442","DOIUrl":"10.1016/j.cpc.2024.109442","url":null,"abstract":"<div><div>We present <span>TriMe++</span>, a multi-threaded software library designed for generating two-dimensional meshes for intricate geometric shapes using the Delaunay triangulation. Multi-threaded parallel computing is implemented throughout the meshing procedure, making it suitable for fast generation of large-scale meshes. Three iterative meshing algorithms are implemented: the DistMesh algorithm, the centroidal Voronoi diagram meshing, and a hybrid of the two. We compare the performance of the three meshing methods in <span>TriMe++</span>, and show that the hybrid method retains the advantages of the other two. The software library achieves significant parallel speedup when generating large-scale meshes containing between 10<sup>4</sup> to 10<sup>7</sup> points. <span>TriMe++</span> can handle complicated geometries and generates adaptive meshes of high quality.</div></div><div><h3>Program summary</h3><div><em>Program title:</em> <span>TriMe++</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/jxcsxtywtw.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/jiayinlu19960224/TriMe</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> BSD 3-clause</div><div><em>Programming language:</em> C++</div><div><em>External routines/libraries:</em> OpenMP, multi-threaded <span>Voro++</span></div><div><em>Nature of problem:</em> Multi-threaded geometry meshing in two dimension using the Delaunay triangulation</div><div><em>Solution method:</em> The <span>TriMe++</span> library is built around several C++ classes that follows a structured meshing pipeline. During initialization, the <span>shape_2d</span> class reads the geometry input and generates a signed distance field using a grid-based data structure to represent the shape. The <span>sizing_2d</span> class subsequently produces adaptive element sizing and density fields for the mesh. It uses an adaptive quad-tree data structure, enabling efficient refinement of sizing and density values in areas with complex geometries. In the meshing procedure, the <span>parallel_meshing_2d</span> class iteratively improves point positions in the mesh. In each meshing iteration, the multi-threaded <span>Voro++</span> library generates the Delaunay triangulation of the points. Users can select from three meshing algorithms, the DistMesh algorithm in the <span>mesh_alg_2d_dm</span> class, the centroidal Voronoi diagram meshing algorithm in the <span>mesh_alg_2d_cvd</span> class, and a hybrid method of the two in the <span>mesh_alg_2d_hybrid</span> class Throughout this meshing workflow, we use OpenMP for multi-threaded parallel computations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"308 ","pages":"Article 109442"},"PeriodicalIF":7.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162813","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 : 2024-11-21DOI: 10.1016/j.cpc.2024.109443
Federico Battisti , Marian Ivanov , Xianguo Lu
This study introduces a Kalman Filter tailored for homogeneous gas Time Projection Chambers (TPCs), adapted from the algorithm utilized by the ALICE experiment. In order to describe semi-circular paths in the plane perpendicular to the magnetic field, we introduce a novel mirror rotation technique into the Kalman Filter algorithm, enabling effective tracking of trajectories of varying lengths, including those with multiple circular paths within the detector, also known as “loopers”. Demonstrated relative improvements of up to 80% in electron momentum resolution and up to 50% in muon and pion momentum resolution underscore the significance of this enhancement. Significant improvements in the reconstruction efficiency for relatively short low momentum “looper” tracks are also shown. Such advancements hold promise not only for the future of the ALICE TPC but also for neutrino high-pressure gas TPCs, where loopers become significant owing to the randomness of production points and their relatively low energies in neutrino interactions. In particular, an improvement in low energy electron reconstruction, for which the production of “looping” tracks is likely and the impact of the new algorithm is directly demonstrated, could significantly impact the quality of flux determination, which in accelerator neutrino experiments relies on the measurement of electron scatterings.
本研究介绍了一种卡尔曼滤波器,它是为均质气体时间投影室(TPC)量身定制的,改编自 ALICE 实验所使用的算法。为了描述垂直于磁场平面的半圆形轨迹,我们在卡尔曼滤波算法中引入了一种新颖的镜面旋转技术,从而能够有效跟踪不同长度的轨迹,包括那些在探测器内有多个圆形轨迹的轨迹,也称为 "环形轨迹"。电子动量分辨率提高了 80%,μ介子和先驱动量分辨率提高了 50%,这些都证明了这一改进的重要性。此外,相对较短的低动量 "环形器 "轨道的重建效率也有显著提高。这种进步不仅为 ALICE TPC 的未来带来了希望,而且也为中微子高压气体 TPC 带来了希望,由于中微子相互作用中产生点的随机性及其相对较低的能量,环形轨道变得非常重要。特别是,低能电子重构的改进(可能产生 "循环 "轨迹,新算法的影响已得到直接证明)会极大地影响通量测定的质量,而通量测定在加速器中微子实验中依赖于νe电子散射的测量。
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Pub Date : 2024-11-20DOI: 10.1016/j.cpc.2024.109439
Xuejun Gong , Andrea Dal Corso
We present an alternative GPU acceleration for plane waves pseudopotentials electronic structure codes designed for systems that have small unit cells but require a large number of k points to sample the Brillouin zone as happens, for instance, in metals. We discuss the diagonalization of the Kohn and Sham equations and the solution of the linear system derived in density functional perturbation theory. Both problems take advantage from a rewriting of the routine that applies the Hamiltonian to the Bloch wave-functions to work simultaneously (in parallel on the GPU threads) on the wave-functions with different wave-vectors k, as many as allowed by the GPU memory. Our implementation is written in CUDA Fortran and makes extensive use of kernel routines that run on the GPU (GLOBAL routines) or can be called from inside the GPU kernel (DEVICE routines). We compare our method with the CPUs only calculation and with the approach currently implemented in Quantum ESPRESSO that uses GPU accelerated libraries for the FFT and for the linear algebra tasks such as the matrix-matrix multiplications as well as OpenACC directives for loop parallelization. We show in a realistic example that our method can give a significant improvement in the cases for which it has been designed.
我们介绍了平面波伪势电子结构代码的另一种 GPU 加速方法,该方法专为具有小单元但需要大量 k 点来采样布里渊区的系统而设计,例如在金属中发生的情况。我们讨论了 Kohn 和 Sham 方程的对角化问题,以及密度泛函扰动理论推导出的线性系统的求解问题。这两个问题都利用了将哈密顿应用于布洛赫波函数的例程重写,以同时(在GPU线程上并行)处理具有不同波矢k的波函数,波矢k的数量在GPU内存允许的范围内。我们的实现是用 CUDA Fortran 编写的,并广泛使用了在 GPU 上运行的内核例程(GLOBAL 例程)或可从 GPU 内核内部调用的例程(DEVICE 例程)。我们将我们的方法与只使用 CPU 的计算方法以及目前在 Quantum ESPRESSO 中实施的方法进行了比较,后者将 GPU 加速库用于 FFT 和线性代数任务,如矩阵-矩阵乘法以及用于循环并行化的 OpenACC 指令。我们在一个现实的例子中表明,我们的方法可以在其设计的情况下带来显著的改进。
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Pub Date : 2024-11-20DOI: 10.1016/j.cpc.2024.109440
Leran Lu, Wei Cao, Romain Botella
With the increasing attention to 2D materials for photocatalytic applications, as well as to data science, there is a need for high-throughput computation of adsorption states for experimentally or theoretically discovered structures in order to study (photo-) catalytic mechanism. Despite numerous progresses in high-throughput methods for adsorption study, a general search algorithm is lacking. In this work, SEFFO (Screener and Enumerator with Force-Field Optimization) algorithm is developed for the automation of adsorption study on 2D material surface. Graph theory is utilized to create the descriptors of the adsorption configurations, which are later input for geometry construction by numerical optimization. The configuration screening process is combining the use of graphs with structural similarity comparison of configurations density functional theory (DFT) produced configurations. The algorithm is validated through four case studies, involving water and carbon dioxide molecules as adsorbates, molybdenum sulfide and carbon nitride as substrate counterparts. The results are consistent with literature while proposing alternative configurations. Additionally, SEFFO can show the evolution between configurations during the process. This method enables the high throughput study of adsorption behavior on 2D materials, and paves the way for future surface studies involving other substrate/adsorbates pairs.
随着二维材料在光催化应用以及数据科学领域的日益受到关注,需要对实验或理论发现的结构进行高通量吸附状态计算,以研究(光)催化机理。尽管用于吸附研究的高通量方法取得了诸多进展,但仍缺乏一种通用的搜索算法。在这项工作中,我们开发了 SEFFO(Screener and Enumerator with Force-Field Optimization)算法,用于自动化二维材料表面的吸附研究。利用图论创建吸附配置的描述符,然后通过数值优化将其输入到几何构造中。配置筛选过程结合了图形的使用和配置密度泛函理论(DFT)生成配置的结构相似性比较。该算法通过四个案例研究进行了验证,涉及作为吸附剂的水分子和二氧化碳分子,以及作为基质的硫化钼和氮化碳。研究结果与文献一致,同时提出了其他配置方案。此外,SEFFO 还能显示过程中配置之间的演变。这种方法可以对二维材料上的吸附行为进行高通量研究,并为未来涉及其他基底/吸附剂对的表面研究铺平了道路。
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Pub Date : 2024-11-19DOI: 10.1016/j.cpc.2024.109434
Wei Bai , Huasheng Xie , Chenchen Wu , Yanxu Pu , Pengcheng Yu
The observation of superthermal plasma distributions in space reveals a multitude of distributions with high-energy tails, and the kappa-Maxwellian distribution is a type of non-Maxwellian distribution that exhibits this characteristic. However, accurately determining the multiple roots of the dispersion relation for superthermal plasma waves propagating obliquely presents a challenge. To tackle this issue, we have developed a comprehensive solver, BO-KM, utilizing an innovative numerical algorithm that eliminates the need for initial value iteration. The solver offers an efficient approach to simultaneously compute the roots of the kinetic dispersion equation for oblique propagation in magnetized plasmas. It can be applied to magnetized superthermal plasma with multi-species, characterized by anisotropic drifting kappa-Maxwellian, bi-Maxwellian distributions, or a combination of the two. The rational and J-pole Padé expansions of the dispersion relation are equivalent to solving a linear system's matrix eigenvalue problem. This study presents the numerical findings for kappa-Maxwellian plasmas, bi-Maxwellian plasmas, and their combination, demonstrating the solver's outstanding performance through benchmark analyses.
Program summary
Program Title: BO-KM
CPC Library link to program files:https://doi.org/10.17632/pr9cvjrvfv.1
Licensing provisions: BSD 3-clause
Programming language: Matlab
Nature of problem: To efficiently solve for multiple roots of the kinetic dispersion relation in superthermal plasma distributions with high-energy tails observed in space, we have developed BO-KM, a novel and comprehensive solver that employs a unified framework for computing uprathermal (or thermal) waves and instabilities. This solver is applicable to magnetized multi-species collisionless plasmas with anisotropic drift kappa-Maxwellian, bi-Maxwellian distributions, or a combination of both. Furthermore, BO-KM incorporates a submodule dedicated to the perpendicular propagation dispersion relation of bi-Kappa plasmas, thereby significantly improving computational efficiency at high κ values.
Solution method: The method converts the kinetic plasma dispersion relation based on rational expansion (for the kappa-Maxwellian model) and J-pole Padé expansion (for the bi-Maxwellian model) into an equivalent linear eigenvalue system. This transformation effectively turns the root-finding task into an eigenvalue problem, enabling the simultaneous determination of roots using standard eigenvalue libraries.
Additional comments including restrictions and unusual features: Kinetic relativistic effects are not included in the present version yet.
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Pub Date : 2024-11-19DOI: 10.1016/j.cpc.2024.109437
M.D. Burke , J.P.F. LeBlanc
We present torchami, an advanced implementation of algorithmic Matsubara integration (AMI) that utilizes pytorch as a backend to provide easy parallelization and GPU support. AMI is a tool for analytically resolving the sequence of nested Matsubara integrals that arise in virtually all Feynman perturbative expansions. In this implementation we present a new AMI algorithm that creates a more natural symbolic representation of the Feynman integrands. In addition, we include peripheral tools that allow for import and labeling of simple graph structures and conversion to torchami input. The code is written in c++ with python bindings provided.
Program summary
Program Title: torchami
CPC Library link to program files:https://doi.org/10.17632/m79hnngy8s.1
Nature of problem: Feynman diagrams are pictorial representations of perturbative expansions often formulated in the imaginary frequency/time axis and involve a high-dimensional sequence of nested integral over spatial and temporal degrees of freedom.
Solution method:torchami provides a framework to symbolically generate and store the analytic solution to the temporal Matsubara sums through repeated application of multipole residue theorems. The solutions are stored using a tree structure for arbitrary products and sums of Fermi/Bose functions, and the evaluation functions provide both CPU and GPU support with automatic parallelization for batch sampling problems.
Additional comments including restrictions and unusual features: Requires C++17 standard, the boost graph library, as well as pytorch.
References
[1]
Amir Taheridehkordi, S. H. Curnoe, and J. P. F. LeBlanc, Algorithmic Matsubara integration for Hubbard-like models, Phys. Rev. B 99 035120 (2019).
[2]
H. Elazab, B. D. E. McNiven, and J. P. F. LeBlanc, LIBAMI: Implementation of algorithmic Matsubara integration, Computer Physics Communications 280, 108469 (2022)
我们介绍的 torchami 是算法松原积分(AMI)的高级实现,它利用 pytorch 作为后端,提供简便的并行化和 GPU 支持。AMI 是一种用于分析解决嵌套松原积分序列的工具,几乎所有费曼微扰展开中都会出现嵌套松原积分。在本实现中,我们介绍了一种新的 AMI 算法,它为费曼积分创建了一种更自然的符号表示。此外,我们还提供了外围工具,可以导入和标注简单的图结构,并将其转换为 torchami 输入。代码以 c++ 编写,并提供了 python 绑定。程序摘要程序标题:torchamiCPC 库程序文件链接:https://doi.org/10.17632/m79hnngy8s.1Developer's repository 链接:https://github.com/mdburke11/torchami/releases/tag/v1.0Licensing 规定:GPLv3 编程语言解决问题的方法:torchami 提供了一个框架,通过重复应用多极残差定理,以符号方式生成并存储时间松原和的解析解。解法采用树形结构存储,适用于任意乘积和费米/玻色函数之和,评估函数同时支持 CPU 和 GPU,并可自动并行处理批量采样问题:Requires C++17 standard, the boost graph library, as well as pytorch.References[1]Amir Taheridehkordi, S. H. Curnoe, and J. P. F. LeBlanc, Algorithmic Matsubara integration for Hubbard-like models, Phys. Rev. B 99 035120 (2019).[2]H. Elazab, B. D. Matsubara integration for Hubbard-like models, Phys. Rev. B 99 035120 (2019).Elazab, B. D. E. McNiven, and J. P. F. LeBlanc, LIBAMI: Implementation of algorithmic Matsubara integration, Computer Physics Communications 280, 108469 (2022).
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Pub Date : 2024-11-19DOI: 10.1016/j.cpc.2024.109438
Evgueni Dinvay
A novel method to integrate the time-dependent Schrödinger equation within the framework of multiresolution analysis is presented. The method is based on symplectic splitting algorithms to separate the kinetic and potential parts of the corresponding propagator. The semigroup associated with the free-particle Schrödinger operator is represented in a multiwavelet basis. The propagator is effectively discretised with a contour deformation technique, which overcomes the challenges presented by previous discretisation methods. The discretised operator is then employed in simple numerical simulations to test the validity of the implementation and to benchmark its precision.
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