Computer Physics Communications最新文献

{"title":"TorchQC - A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control","authors":"Dimitris Koutromanos,&nbsp;Dionisis Stefanatos,&nbsp;Emmanuel Paspalakis","doi":"10.1016/j.cpc.2025.109505","DOIUrl":"10.1016/j.cpc.2025.109505","url":null,"abstract":"<div><div>Machine learning has been revolutionizing our world over the last few years and is also increasingly exploited in several areas of physics, including quantum dynamics and control. The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of exploiting these powerful computational methods for the efficient implementation of modern quantum technologies. The existing frameworks for quantum system simulations, such as QuTip and QuantumOptics.jl, even though they are very successful in simulating quantum dynamics, cannot be easily incorporated into the platforms used for the development of machine learning models, like for example PyTorch. The TorchQC framework introduced in the present work comes exactly to fill this gap. It is a new library written entirely in Python and based on the PyTorch deep learning library. PyTorch and other deep learning frameworks are based on tensors, a structure that is also used in quantum mechanics. This is the common ground that TorchQC utilizes to combine quantum physics simulations and deep learning models. TorchQC exploits PyTorch and its tensor mechanism to represent quantum states and operators as tensors, while it also incorporates all the tools needed to simulate quantum system dynamics. All necessary operations are internal in the PyTorch library, thus TorchQC programs can be executed in GPUs, substantially reducing the simulation time. We believe that the proposed TorchQC library has the potential to accelerate the development of deep learning models directly incorporating quantum simulations, enabling the easier integration of these powerful techniques in modern quantum technologies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109505"},"PeriodicalIF":7.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143933","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}

{"title":"FeynGrav 3.0","authors":"Boris Latosh","doi":"10.1016/j.cpc.2025.109508","DOIUrl":"10.1016/j.cpc.2025.109508","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We present the new version of the &lt;span&gt;FeynGrav&lt;/span&gt;. The package provides tools to operate with Feynman rules for quantum gravity within &lt;span&gt;FeynCalc&lt;/span&gt;. The latest version improves package efficiency and implements new physical models. We discover recurrent relations between metric factors that enhance computational efficiency. We discuss gravitational interaction with Horndeski gravity, quadratic gravity, and the simplest axion-like coupling. We implemented the massive graviton propagator and discussed the possibility of implementing massive gravity within the package.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program title:&lt;/em&gt; FeynGrav&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/rwnrbbdkkv.2&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;github.com/BorisNLatosh/FeynGrav&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Wolfram Mathematica 10 and higher&lt;/div&gt;&lt;div&gt;&lt;em&gt;Journal reference of previous version:&lt;/em&gt; [1,2]&lt;/div&gt;&lt;div&gt;&lt;em&gt;Does the new version supersede the previous version?:&lt;/em&gt; The new version supersedes the previous version. The new version implements new interaction rules and new commands for propagators, projection operators, and Nieuwenhuizen operators.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Reasons for the new version:&lt;/em&gt; Firstly, to improve the efficiency of the generation of interaction rules. Secondly, to implement more models relevant to the modified gravity and particle physics community.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Summary of revisions:&lt;/em&gt; The performance of the algorithm generating the interaction rules is increased. New interaction rules for Horndeski gravity, axion-like coupling to a single scalar field, and quadratic coupling are added. A realisation of the massive graviton propagator is implemented.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Perturbative quantum gravity is an effective theory that provides a framework to study quantum gravitational effects below the Planck scale. Due to its nature, the theory contains an infinite number of interaction vertices, each containing a large number of terms. Using a computer algebra system is essential to operate efficiently with the interaction rules in perturbative quantum gravity.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; FeynGrav provides a framework to operate with the interaction rules of perturbative quantum gravity within FeynCalc [3–5]. The package uses the theoretical framework, allowing for efficient computation of the interaction rules. The program also uses FORM [6] to further improve computational efficiency.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;References&lt;/h3&gt;&lt;div&gt;&lt;ul&gt;&lt;li&gt;&lt;span&gt;[1]&lt;/span&gt;&lt;span&gt;&lt;div&gt;B. Latosh, Class. Quantum Gravity 39 (16) (2022) 165006, &lt;span&gt;&lt;span&gt;https://doi.org/10.1088/1361-6382/ac7e15&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;arXiv:2201.06812 [hep-th]&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/di","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109508"},"PeriodicalIF":7.2,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143932","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}

{"title":"SMIwiz-2.0: Extended functionalities for wavefield decomposition, linearized and nonlinear inversion","authors":"Zhengyu Ji,&nbsp;Pengliang Yang","doi":"10.1016/j.cpc.2025.109503","DOIUrl":"10.1016/j.cpc.2025.109503","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We extend the functionalities of SMIwiz open source software to include up-down wavefield separation, reflection waveform inversion, as well as linearized waveform inversion in data and image domain. The fundamental functionalities for 2D/3D wave modelling and imaging (reverse time migration and nonlinear full waveform inversion) are backward compatible with improvements in seismic imaging processing. Reproducible examples are supplied to verify these developments.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;New version program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; SMIwiz&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/tygszns27k.2&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/yangpl/SMIwiz&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GNU General Public License v3.0&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; C, Shell, Fortran&lt;/div&gt;&lt;div&gt;&lt;em&gt;Software dependencies:&lt;/em&gt; MPI [1], FFTW [2]&lt;/div&gt;&lt;div&gt;&lt;em&gt;Journal reference of previous version:&lt;/em&gt; Comput. Phys. Commun. 295 (2024) 109011. &lt;span&gt;&lt;span&gt;https://doi.org/10.1016/j.cpc.2023.109011&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Does the new version supersede the previous version?:&lt;/em&gt; Yes.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Seismic modelling and imaging (linearized and nonlinear waveform inversion).&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; Conjugate gradient (CGNR) method for linearized inversion, quasi-Newton LBFGS and line search for nonlinear optimization.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Summary of revisions:&lt;/em&gt; The following new features (specified by a &lt;span&gt;mode&lt;/span&gt; parameter) are added to extend the functionalities of SMIwiz:&lt;ul&gt;&lt;li&gt;&lt;span&gt;1.&lt;/span&gt;&lt;span&gt;&lt;div&gt;Up-going and down-going wavefield separation (&lt;span&gt;mode=10&lt;/span&gt;)&lt;/div&gt;&lt;div&gt;We have implemented decomposition of up-going and down-going wavefield following [3,4]. The method relies on the use of Hilbert transform for constructing analytic wavefield. To avoid huge storage requirement and expensive computation, the Hilbert transform was applied to the wavelet or source time function. By simultaneously propagating the wavefield excited by a complex-valued analytic source time function (its imaginary part is the Hilbert transform of the real part), the analytic wavefield (with real and imaginary parts stored separately) is then obtained by time stepping algorithms. The up-going wavefield and down-going wavefields are then computed by a filtering process in the frequency-wavenumber domain (based on the sign of the frequency and the wavenumber) [3,4].&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;2.&lt;/span&gt;&lt;span&gt;&lt;div&gt;Improved reverse time migration (RTM, &lt;span&gt;mode=2&lt;/span&gt;)&lt;/div&gt;&lt;div&gt;We have improved RTM by switching from cross-correlation imaging condition to an impedance kernel. This change was motivated by the fact that classic cross-correlation imaging condition suffers from the low-frequency noises. These low-frequency noises","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109503"},"PeriodicalIF":7.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127851","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}

{"title":"Research on hybrid data-driven method for predicting the remaining useful life of lithium-ion batteries","authors":"Yuanjiang Li ,&nbsp;Liping Li ,&nbsp;Lei Li ,&nbsp;Xinyu Huang ,&nbsp;Guodong Sun ,&nbsp;Yina Wang ,&nbsp;Jinglin Zhang","doi":"10.1016/j.cpc.2025.109500","DOIUrl":"10.1016/j.cpc.2025.109500","url":null,"abstract":"<div><div>The instability and inconsistency of lithium-ion batteries (LIBs) may lead to sudden battery failures that cause serious accidents, hence the safety and reliability of the battery can be ordinarily effectively improved via improving the accuracy and uncertainty of the remaining useful life (RUL). Nevertheless, capacity data of LIBs display significant nonlinearity and are plagued by problems such as capacity regeneration (CR) and difficult to precise uncertainty. In order to address this issue, the improved northern goshawk optimization (INGO) algorithm and the variational mode decomposition (VMD) algorithm are combined in this article to present a unique hybrid driven by data prediction technique that adaptively breaks down the nonlinear, non-smooth initial battery capacity sequence into several trend subsequences and fluctuating subsequences. Its goal is to make the battery capacity sequence less complicated. Additionally, the deconstructed fluctuation subsequence is summed into a reconstructed sequence to optimize the computational process. Ordered neurons-long short-term memory attention mechanism (ONLSTM-AM) architectures and Tensor transfer learning-deep neural network (TTL-DNN) are employed to forecast the trending subsequence and rebuilt sequences, respectively. By doing this, the quantity of data that needs to be predicted is decreased and the training process is expedited. In this paper, the method is experimentally validated using the NASA dataset and the CALCE dataset, and the accuracy is compared with several common machine learning algorithms. The experiment's findings show that the proposed strategy produces the lowest RMSE values of 0.0055 Ah in the NASA dataset and 0.0061 Ah in the CALCE dataset, displaying high prediction accuracy, strong long-term prediction ability and high generalization ability. Our source code is available at <span><span>https://github.com/Mmabc333/A-hybrid-method</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109500"},"PeriodicalIF":7.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128593","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}

{"title":"Nonlinear model order reduction of engineering turbulence using data-assisted neural networks","authors":"Chuanhua Zhu ,&nbsp;Jinlong Fu ,&nbsp;Dunhui Xiao ,&nbsp;Jinsheng Wang","doi":"10.1016/j.cpc.2025.109501","DOIUrl":"10.1016/j.cpc.2025.109501","url":null,"abstract":"<div><div>Conducting repeated high-fidelity simulations of complex turbulent flows entails substantial computational costs in engineering applications. Reduced-order modeling (ROM) seeks to derive low-dimensional representations from full-order numerical systems, thereby facilitating rapid forecasting of future flow states. This study presents a novel data-assisted computational framework that employs deep neural networks for nonlinear ROM of engineering turbulent flows. Specifically, the Stacked Auto-Encoder (SAE) network is utilized for nonlinear dimensionality reduction and feature extraction; the resulting latent features subsequently serve as inputs to the Long Short-Term Memory (LSTM) network for predictive ROM of turbulent fluid dynamics. A comparative analysis is conducted between SAE and proper orthogonal decomposition regarding dimensionality reduction, and the performance of LSTM in time-series forecasting is also evaluated against dynamic mode decomposition, where two different training strategies are applied for LSTM within the reduced-order latent space. The proposed SAE-LSTM-based ROM approach is tested on two typical turbulent flow problems for non-intrusive model order reduction. The results demonstrate that the constructed surrogate models possess significant capability in predicting the evolution of turbulent flows by preserving essential nonlinear characteristics inherent in fluid dynamics. This innovative method shows great promise in addressing computational challenges associated with high-resolution numerical modeling applied to complex large-scale flow problems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109501"},"PeriodicalIF":7.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128591","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}

{"title":"DWR-drag: A new generation software for the double wall-ring interfacial shear rheometer's data analysis","authors":"Pablo Sanchez-Puga ,&nbsp;Miguel A. Rubio","doi":"10.1016/j.cpc.2025.109499","DOIUrl":"10.1016/j.cpc.2025.109499","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The double wall-ring (DWR) rotational configuration is nowadays the instrument of choice regarding interfacial shear rheometers (ISR) in rotational configurations. Complex numerical schemes must be used in the analysis of the output data in order to appropriately deal with the coupling between interfacial and bulk fluid flows, and to separate viscous and elastic contribution or the interfacial response. We present a second generation code for analyzing the interfacial shear rheology experimental results of small amplitude oscillatory measurements made with a DWR rotational rheometer. The package presented here improves significantly the accuracy and applicability range of the previous available software packages by implementing: i) a physically motivated iterative scheme based on the probe's equation of motion, ii) an increased user selectable spatial resolution, and iii) a second order approximation for the velocity gradients at the ring surfaces. Moreover, the optimization of the computational effort allows, in many cases, for on-the-fly execution during data acquisition in real experiments.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; DWR-Drag&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/vw8k79tmr6.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; MATLAB and Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; An additional document illustrating further software tests regarding flow structure and details about the numerical method used is provided as a Supplementary Material.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; How to determine the interfacial dynamic moduli of fluid–fluid interfaces from experimental data has been a challenge in the rheologists community because it requires i) to accurately separate the contributions of the drags exerted by the interface and the adjacent bulk phases, and ii) to accurately separate the viscous and elastic contributions to the interface response. Moreover, in most cases, the velocity profiles at the interface and the bulk phases are not linear and, consequently, simplifying hypothesis about the interfacial and bulk phases velocity fields are useless.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; The physical model includes the upper and lower bulk fluid phases, represented as Newtonian fluids (Navier-Stokes equations), the equilibrium of stresses at a viscoelastic interface under shear (Boussinesq-Scriven equation), and the probe's equation of motion. The hydrodynamic problem is solved using a second order centered finite differences scheme. The representation of the drag on the probe is much improved by implementing a selectable spatial resolution based on the ring's cross-section dimension and by a second order representation of the velocity gradient close to the ring's walls. An iterative scheme allows for obtaining the flow configuration that best matches the exp","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109499"},"PeriodicalIF":7.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143938","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}

{"title":"Numerical simulation of thermal counterflow in superfluid helium: Investigating the effect of rotation and heat flux on the surface of the cylinder","authors":"Hamid Yousefi,&nbsp;Hossein Afshin","doi":"10.1016/j.cpc.2024.109495","DOIUrl":"10.1016/j.cpc.2024.109495","url":null,"abstract":"<div><div>This study focuses on the numerical simulation of the thermal counterflow of helium superfluid around a cylinder. To model helium superfluid behavior, two-fluid equations, incorporating the Gorter-Mellink mutual friction, were employed. The simulation utilized the PIMPLE (Pressure Implicit with Splitting of Operator) algorithm, which couples the velocities of the normal and superfluid components with pressure, thereby enhancing numerical stability. This approach made it possible to simulate the thermal counterflow around the rotating cylinder and heat flux on its surface, including both heating and cooling effects. The research aims to explore the impact of rotation and heat flux on separation angles, drag and lift forces, and in principle, the overall pattern of the flow. The primary objective is to gain a more profound insight into the behavior of superfluid helium and optimize its applications in both research and industrial contexts. The findings reveal that, in contrast to classical fluids, the influences of various factors do not adhere to a consistent rule. Rotation and cooling/heating were observed to significantly affect separation and the aerodynamic forces. However, the nature of this impact can vary across different scenarios. In some cases, rotation increases the separation angle, while in others, it completely eliminates separation. Consequently, the effect of rotation on the drag force coefficient exhibits substantial variation depending on the specific problem at hand. For instance, in one problem, the drag force coefficient increases from approximately 0.7 to about 1.4 due to rotation, whereas in another, it decreases from around 0.8 to approximately 0.1. Additionally, rotation leads to a drag force coefficient of approximately 1.3 in one specific scenario. Furthermore, this study has demonstrated that the rotation of the cylinder induces asymmetry in the mass flow rates of the components. For instance, in one case, the cylinder's rotation resulted in approximately 86 % of the superfluid component passing from one side of the cylinder.</div><div>Overall, cooling tends to reduce the separation angle and drag force coefficient, while heating has the opposite effect and increases them. For example, in one scenario, cooling causes the drag force coefficient to drop from 0.8 to about 0.1, whereas heating elevates above 1.6.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109495"},"PeriodicalIF":7.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143934","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}

{"title":"LibRPA: A software package for low-scaling first-principles calculations of random phase approximation electron correlation energy based on numerical atomic orbitals","authors":"Rong Shi ,&nbsp;Min-Ye Zhang ,&nbsp;Peize Lin ,&nbsp;Lixin He ,&nbsp;Xinguo Ren","doi":"10.1016/j.cpc.2024.109496","DOIUrl":"10.1016/j.cpc.2024.109496","url":null,"abstract":"<div><div>LibRPA is a software package designed for efficient calculations of random phase approximation (RPA) electron correlation energies from first principles using numerical atomic orbital (NAOs). Leveraging a localized resolution of identity (LRI) technique, LibRPA achieves <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></math></span> or better scaling behavior, making it suitable for large-scale calculation of periodic systems. Implemented in C++ and Python with MPI/OpenMP parallelism, LibRPA integrates seamlessly with NAO-based density functional theory (DFT) packages through flexible file-based and API-based interfaces. In this work, we present the theoretical framework, algorithm, software architecture, and installation and usage guide of LibRPA. Performance benchmarks, including the parallel efficiency with respect to the computational resources and the adsorption energy calculations for <figure><img></figure> molecules on graphene, demonstrate its nearly ideal scalability and numerical reliability. LibRPA offers a useful tool for RPA-based calculations for large-scale extended systems.</div></div><div><h3>Program summary</h3><div><em>Program title:</em> LibRPA</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/kdwm5vzgk6.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/Srlive1201/LibRPA</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> LGPL</div><div><em>Programming language:</em> C++, Fortran, Python</div><div><em>Nature of problem:</em> Calculating RPA electron correlation energies is computationally expensive, typically scaling as <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></math></span> with system size, hindering its application to large-scale materials science problems.</div><div><em>Solution method:</em> LibRPA utilizes the Localized Resolution of Identity (LRI) technique, reducing computational scaling to <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></math></span> or better. Implemented in C++ and Python with MPI/OpenMP parallelization, it integrates with NAO-based DFT packages, facilitating efficient and accurate RPA calculations for large-scale periodic systems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109496"},"PeriodicalIF":7.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093085","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}

{"title":"HTMPC: A heavily templated C++ library for large scale particle-based mesoscale hydrodynamics simulations using multiparticle collision dynamics","authors":"Elmar Westphal ,&nbsp;Segun Goh ,&nbsp;Roland G. Winkler ,&nbsp;Gerhard Gompper","doi":"10.1016/j.cpc.2024.109494","DOIUrl":"10.1016/j.cpc.2024.109494","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We present HTMPC, a Heavily Templated C++ library for large-scale simulations implementing multi-particle collision dynamics (MPC), a particle-based mesoscale hydrodynamic simulation method. The implementation is plugin-based, and designed for distributed computing over an arbitrary number of MPI ranks. By abstracting the hardware-dependent parts of the implementation, we provide an identical application-code base for various architectures, currently supporting CPUs and CUDA-capable GPUs. We have examined the code for a system of more than a trillion MPC particles distributed over a few thousand MPI ranks (GPUs), demonstrating the scalability of the implementation and its applicability to large-scale hydrodynamic simulations. As showcases, we examine passive and active suspension of colloids, which confirms the extensibility and versatility of our plugin-based implementation.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; HTMPC&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/xnxh68zhbt.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; MIT&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; C++17, CUDA C++ (optional), MPI (optional)&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; Supplementary Information, User Manual&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Complex fluids in soft, active, and living matter are characterized by a wide range of relevant length- and time-scales, from nanometers to millimeters, and from sub-microseconds to seconds. Their dynamics is often governed by the hydrodynamics of the embedding aqueous medium. Thus, it is essential for the numerical study of such systems to develop efficient simulation techniques and highly parallel computer codes, especially when large system sizes and emergent collective behavior are considered. Several mesoscale simulation techniques have been developed in the last decades for this purpose. Multi-particle collision dynamics (MPC), a particle-based hydrodynamics simulation technique, is a promising ansatz for such an endeavor. It is also important to develop an easy-to-extend implementation, so that the code can be adapted to various soft and living matter systems as desired.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; We develop an implementation of MPC that can exploit large-scale high-performance computing resources for hydrodynamic simulations of complex fluids. The code provides a C++ template library, which is plugin-based and can be extended by user-written plugins, implementing particles or objects interacting with the surrounding fluid. Calculations can be distributed over an arbitrary number of MPI ranks and accelerated with the current implementation supporting CUDA-capable GPUs. The code includes essential features of state-of-the-art MPC algorithms, e.g., thermostat, local angular-momentum conservation, and a variety of boundary conditions, such as periodic, no-slip (both also supporting shear flow) a","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109494"},"PeriodicalIF":7.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127924","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}

{"title":"All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides","authors":"Jean Furstoss ,&nbsp;Carlos R. Salazar ,&nbsp;Philippe Carrez ,&nbsp;Pierre Hirel ,&nbsp;Julien Lam","doi":"10.1016/j.cpc.2024.109480","DOIUrl":"10.1016/j.cpc.2024.109480","url":null,"abstract":"<div><div>To accurately identify local structures in atomic-scale simulations of complex materials is crucial for the study of numerous physical phenomena including dynamic plasticity, crystal nucleation and glass formation. In this work, we propose a data-driven method to characterize local atomic environments, and assign them to crystal phases or lattice defects. After constructing a reference database, our approach uses descriptors based on Steinhardt's parameters and a Gaussian mixture model to identify the most probable environment. This approach is validated against several test cases: polymorph identification in alumina, and dislocation and grain boundary analysis in the olivine structure.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"309 ","pages":"Article 109480"},"PeriodicalIF":7.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093087","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}