Computer Physics Communications最新文献

{"title":"Goupil: A Monte Carlo engine for the backward transport of low-energy gamma-rays","authors":"Valentin Niess ,&nbsp;Kinson Vernet ,&nbsp;Luca Terray","doi":"10.1016/j.cpc.2025.109653","DOIUrl":"10.1016/j.cpc.2025.109653","url":null,"abstract":"<div><div><span>Goupil</span> is a software library designed for the Monte Carlo transport of low-energy gamma-rays, such as those emitted from radioactive isotopes. The library is distributed as a Python module. It implements a dedicated backward sampling algorithm that is highly effective for geometries where the source size largely exceeds the detector size. When used in conjunction with a conventional Monte Carlo engine (i.e., <span>Geant4</span>), the response of a scintillation detector to gamma-active radio-isotopes scattered over the environment is accurately simulated (to the nearest percent) while achieving events rates of a few kHz (with a ∼2.3<!--> <!-->GHz CPU).</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Goupil</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/r2m8mr9jnk.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/niess/goupil</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> LGPL-3.0</div><div><em>Programming language:</em> C, Python and Rust.</div><div><em>Nature of problem:</em> Backward Monte Carlo transport of gamma-rays that are emitted by mono-energetic sources distributed in space.</div><div><em>Solution method:</em> A simple modification to a previously presented backward Monte Carlo algorithm [1].</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>V. Niess, A. Barnoud, C. Cârloganu, E. Le Ménédeu, Comput. Phys. Commun. 229 (2018) 54–67, <span><span>https://doi.org/10.1016/j.cpc.2018.04.001</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109653"},"PeriodicalIF":7.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090395","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":"DLScanner: A parameter space scanner package assisted by deep learning methods","authors":"A. Hammad ,&nbsp;Raymundo Ramos","doi":"10.1016/j.cpc.2025.109659","DOIUrl":"10.1016/j.cpc.2025.109659","url":null,"abstract":"<div><div>In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points to the target space. To tackle the first issue, we use a similarity learning network that maps sampled points into a representation space. In this space, in-target points are grouped together while out-target points are effectively pushed apart. This approach enhances the scan convergence by refining the representation of sampled points. The second challenge is mitigated by integrating a dynamic sampling strategy. Specifically, we employ a VEGAS mapping to adaptively suggest new points for the DL network while also improving the mapping when more points are collected. Our proposed framework demonstrates substantial gains in performance and efficiency compared to other scanning methods.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109659"},"PeriodicalIF":7.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946995","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":"Dielectric functions, their properties and their relation to observables: Investigations using the Chapidif program for the case of aluminum","authors":"Maarten Vos ,&nbsp;Pedro L. Grande","doi":"10.1016/j.cpc.2025.109657","DOIUrl":"10.1016/j.cpc.2025.109657","url":null,"abstract":"<div><div>We introduce the program ‘Chapidif’ by describing a study of the properties of aluminum based on simple model dielectric functions. These are generally not available from first principle, and one is forced to describe them in terms of (a sum of) model dielectric functions. The Chapidif program is used to visualize these, check their sum rules and the mathematical relation between the real and imaginary part. In addition, several properties related to the interaction of charged particles (here either protons or electrons) with matter are derived and compared with experiment. By having a single program that can calculate a range of properties, it becomes easy to ensure that the model used is not just able to describe a single observable, but it is transferable, i.e. describes reasonably well a larger range of material properties. A reflection electron energy loss measurement is used as an example of how a comparison of calculated results with experiment can be used to improve the model and thus enhance the quality of the properties derived from the dielectric function.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Chapidif</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/7wmxg69v7x.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> CC BY NC 3.0</div><div><em>Programming language:</em> Python, C++</div><div><em>Nature of problem:</em> Frequency- and momentum-dependent dielectric functions can describe a wide variety of material properties. The quantity has many intricate mathematical properties and is subject to constraints due to sum rules. The Chapidif program can be used to visualize a dielectric function, check its sum rules, and calculate a wide range of quantities, in particular relating to the interaction of protons and electrons with matter. Details of how the classical and quantum-based dielectric functions are implemented are given elsewhere [1]. The program makes it easy to investigate if the assumed dielectric function has the required mathematical properties and how the choice of the model dielectric function and the corresponding parameters influences the calculated observables such as ion stopping and electron inelastic mean free path.</div><div><em>Solution method:</em> The program consists of a Python/Tkinter user interface and C++ backend that does the actual calculations. Results are displayed using Matplotlib library and, if desired, text-based output files containing the input parameters used and the calculated quantities can be generated.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>M. Vos, P.L. Grande, RPA dielectric functions: streamlined approach to relaxation effects, binding and high momentum dispersion, J. Phys. Chem. Solids 198 (2025) 112470, <span><span>https://doi.org/10.1016/j.jpcs.2024.112470</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109657"},"PeriodicalIF":7.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084696","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":"Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations","authors":"Attila Cangi ,&nbsp;Lenz Fiedler ,&nbsp;Bartosz Brzoza ,&nbsp;Karan Shah ,&nbsp;Timothy J. Callow ,&nbsp;Daniel Kotik ,&nbsp;Steve Schmerler ,&nbsp;Matthew C. Barry ,&nbsp;James M. Goff ,&nbsp;Andrew Rohskopf ,&nbsp;Dayton J. Vogel ,&nbsp;Normand Modine ,&nbsp;Aidan P. Thompson ,&nbsp;Sivasankaran Rajamanickam","doi":"10.1016/j.cpc.2025.109654","DOIUrl":"10.1016/j.cpc.2025.109654","url":null,"abstract":"<div><div>We present the Materials Learning Algorithms (<span>MALA</span>) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, <span>MALA</span> models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate <span>MALA</span>'s capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal <span>MALA</span>'s computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, <span>MALA</span> is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109654"},"PeriodicalIF":7.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935430","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":"DataPix4: A C++ framework for Timepix4 configuration and read-out","authors":"Viola Cavallini ,&nbsp;Nicolò Vladi Biesuz ,&nbsp;Riccardo Bolzonella ,&nbsp;Enrico Calore ,&nbsp;Massimiliano Fiorini ,&nbsp;Alberto Gianoli ,&nbsp;Xavier Llopart Cudie ,&nbsp;Sebastiano Fabio Schifano","doi":"10.1016/j.cpc.2025.109658","DOIUrl":"10.1016/j.cpc.2025.109658","url":null,"abstract":"<div><div>DataPix4 (Data Acquisition for Timepix4 Applications) is a new C++ framework for the management of Timepix4 ASIC, a multi-purpose hybrid pixel detector designed at CERN. Timepix4 consists of a matrix of 448×512 pixels that can be connected to several types of sensors, to obtain a pixelated detector suitable for different applications. DataPix4 can be used both for the full configuration of Timepix4 and its control board, and for the data read-out via <em>slow control</em> or <em>fast links</em>. Furthermore, it has a flexible architecture that allows for changes in the hardware, making it easy to adjust the framework to custom setups and exploit all classes with minimal modification.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109658"},"PeriodicalIF":7.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946996","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":"The cumulant Green's functions method for the single impurity Anderson model","authors":"T.M. Sobreira ,&nbsp;T.O. Puel ,&nbsp;M.A. Manya ,&nbsp;S.E. Ulloa ,&nbsp;G.B. Martins ,&nbsp;J. Silva-Valencia ,&nbsp;R.N. Lira ,&nbsp;M.S. Figueira","doi":"10.1016/j.cpc.2025.109651","DOIUrl":"10.1016/j.cpc.2025.109651","url":null,"abstract":"<div><div>Using the cumulant Green's functions method (CGFM), we study the single impurity Anderson model (SIAM). The CGFM starting point is the diagonalization of the SIAM Hamiltonian expressed in a semi-chain form containing <em>N</em> sites, viz., a correlated site (simulating an impurity) connected to the remaining <span><math><mi>N</mi><mo>−</mo><mn>1</mn></math></span> uncorrelated conduction-electron sites. An exact solution can be obtained since the complete system has few sites. That solution is employed to calculate the atomic Green's functions and the approximate cumulants used to obtain the impurity and conduction Green's functions for the SIAM, and no self-consistency loop is required.</div><div>We calculated the density of states, the Friedel sum rule, and the impurity occupation number, all benchmarked against results from the numerical renormalization group (NRG). One of the main insights obtained is that, at very low temperatures, only four atomic transitions contribute to generate the entire SIAM density of states, regardless of the number of sites in the chain and the model's parameters and different regimes: Empty orbital, mixed-valence, and Kondo. We also pointed out the possibilities of the CGFM as a valid alternative to describe strongly correlated electron systems like the Hubbard and <span><math><mi>t</mi><mo>−</mo><mi>J</mi></math></span> models, the periodic Anderson model, the Kondo and Coqblin-Schrieffer models, and their variants.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109651"},"PeriodicalIF":7.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935429","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":"Structural optimization of atomic clusters using iterated dynamic lattice search: With application to silver clusters","authors":"Xiangjing Lai ,&nbsp;Jin-Kao Hao ,&nbsp;Zhaolu Guo ,&nbsp;Quan Wen ,&nbsp;Zhang-Hua Fu","doi":"10.1016/j.cpc.2025.109655","DOIUrl":"10.1016/j.cpc.2025.109655","url":null,"abstract":"<div><div>Predicting the global minimum structures of atomic clusters has important practical implications in physics and chemistry. This is because the global minimum structures of their potential function theoretically correspond to their ground state structures, which determine some important physical and chemical properties of clusters. However, this prediction task is a very challenging global optimization problem due to the fact that the number of local minima on the potential energy surface of clusters increases exponentially with the cluster size. In this study, we propose an unbiased global optimization approach, called the iterated dynamic lattice search algorithm, to search for the global minimum structure of atomic clusters. Based on the iterated local search framework, the proposed algorithm employs the well-known monotonic basin-hopping method to improve the initial structures of clusters, a surface-based perturbation operator to randomly change the positions of selected surface atoms or central atom, a dynamic lattice search method to optimize the positions of surface atoms, and the Metropolis acceptance rule to accept the optimized new solutions. The performance of the algorithm is evaluated on the 300 widely studied silver clusters and experimental results show that the proposed algorithm is highly efficient compared to the existing algorithms. In particular, the proposed algorithm improves the best-known structures for 47 clusters and matches the best-known structures for the remaining clusters. Additional experiments are performed to analyze the key components of the algorithm and the landscape of the potential energy surface of several representative clusters.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109655"},"PeriodicalIF":7.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942233","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":"Implementation of asymptotic preserving discrete velocity methods into the simulation code PICLas","authors":"Félix Garmirian,&nbsp;Marcel Pfeiffer","doi":"10.1016/j.cpc.2025.109648","DOIUrl":"10.1016/j.cpc.2025.109648","url":null,"abstract":"<div><div>The Bhatnagar-Gross-Krook (BGK) model of the Boltzmann equation allows for efficient flow simulations, especially in the transition regime between continuum and high rarefaction. However, ensuring efficient performances for multiscale flows, in which the Knudsen number varies by several orders of magnitude, is never straightforward. Discrete velocity methods as well as particle-based solvers can each reveal advantageous in different conditions, but not without compromises in specific regimes. This article presents a second-order asymptotic preserving discrete velocity method to solve the BGK equation, with the particularity of maintaining positivity when operations are conducted with the cell-local distribution function. With this procedure based on exponential differencing, it is therefore also possible to construct an adapted version of this second-order method using the stochastic particle approach, as presented in Pfeiffer et al. <span><span>[1]</span></span>. The deterministic variant and its implementation are detailed here and its performances are evaluated on several test cases. Combined to the probabilistic solver and with the possibility of a future coupling, our exponential differencing discrete velocity method provides a robust toolbox, useful for efficiently simulating multiscale gas phenomena.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109648"},"PeriodicalIF":7.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934608","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":"Redistribution of the post-reaction internal energies in DSMC using quantum-kinetic model","authors":"Chi-Ho Chou,&nbsp;Kuo-Long Pan","doi":"10.1016/j.cpc.2025.109641","DOIUrl":"10.1016/j.cpc.2025.109641","url":null,"abstract":"<div><div>The Direct Simulation Monte Carlo (DSMC) method has been largely adopted to analyze problems regarding hypersonic, non-equilibrium, and microscopic flows. In this study, we investigate the thermal-chemical effects on combustion at the microscopic scale using this particle collision-based method. It is realized that the existing Larsen-Borgnakke (L-B) model dealing with transfers of various internal energies cannot provide valid solutions for the reactions, and consequently the system fails to reach thermal equilibrium. To overcome this problem, we propose a modified quantum-kinetic (Q-K) model and corresponding redistribution algorithm to satisfy the required detailed balance, based on the solver dsmcFoam+ in the open-source software OpenFOAM. This allows a more straightforward way to handle post-energy redistribution in chemical reactions in comparison to those of the other methods, thus reducing the computational cost and manipulation. To verify the accuracy, spontaneous combustion of premixed <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> is simulated, which includes polyatomic reactions and non-equilibrium processes, followed by three-dimensional simulation for the Mars Pathfinder probe. Compared with the L-B redistribution method, substantial improvement and excellent solutions to the issues are demonstrated by using the new approach, paving the way for accurate and efficient studies of complex problems involving polyatomic chemical reactions and non-equilibrium processes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109641"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934609","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":"Generalizable models of magnetic hysteresis via physics-aware recurrent neural networks","authors":"Abhishek Chandra ,&nbsp;Taniya Kapoor ,&nbsp;Bram Daniels ,&nbsp;Mitrofan Curti ,&nbsp;Koen Tiels ,&nbsp;Daniel M. Tartakovsky ,&nbsp;Elena A. Lomonova","doi":"10.1016/j.cpc.2025.109650","DOIUrl":"10.1016/j.cpc.2025.109650","url":null,"abstract":"<div><div>Hysteresis is a ubiquitous phenomenon in magnetic materials; its modeling and identification are crucial for understanding and optimizing the behavior of electrical machines. Such machines often operate under uncertain conditions, necessitating modeling methods that can generalize across unobserved scenarios. Traditional recurrent neural architectures struggle to generalize hysteresis patterns beyond their training domains. This paper mitigates the generalization challenge by introducing a physics-aware recurrent neural network approach to model and generalize the hysteresis manifesting in sequentiality and history-dependence. The proposed method leverages ordinary differential equations (ODEs) governing the phenomenological hysteresis models to update hidden recurrent states. The effectiveness of the proposed method is evaluated by predicting generalized scenarios, including first-order reversal curves and minor loops. The results demonstrate robust generalization to previously untrained regions, even with noisy data, an essential feature that hysteresis models must have. The results highlight the advantages of integrating physics-based ODEs into recurrent architectures, including superior performance over traditional methods in capturing the complex, nonlinear hysteresis behaviors in magnetic materials. The codes and data related to the paper are at <span><span>github.com/chandratue/HystRNN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"314 ","pages":"Article 109650"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921718","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}