Computer Physics Communications最新文献

{"title":"Predicting radiation distribution via neural operator trained on basis function-generated data","authors":"Ankang Hu ,&nbsp;Kaiwen Li ,&nbsp;Rui Qiu ,&nbsp;Junli Li","doi":"10.1016/j.cpc.2025.109710","DOIUrl":"10.1016/j.cpc.2025.109710","url":null,"abstract":"<div><div>The prediction of radiation-related quantity distributions through the solution of the transport equation is a computationally intensive process that necessitates substantial computational resources. Neural networks hold promise for rapid prediction; however, their utility is constrained by the scarcity of training datasets. In this study, we introduce a method using the Fourier Neural Operator (FNO) to predict radiation distributions, mitigating the challenges associated with limited datasets by generating data through basis functions. Our numerical experiments use the prediction of photon-deposited energy distributions in PET-CT examinations as an example. FNOs trained on datasets generated by basis functions show performance comparable to those trained on data derived from CT images. Specifically, the Mean Absolute Errors (MAEs) of FNOs trained on basis function-generated datasets are less than 65% of the MAEs of 3D U-Nets trained on CT images, which are commonly utilized for dose distribution prediction in the field of nuclear medicine. The inference time of FNOs is approximately 0.1 seconds, which is significantly quicker than the time taken for Monte Carlo simulations. Our findings underscore the generalization abilities of FNOs trained on basis function-generated data. This indicates a practical approach for the rapid prediction of radiation fields. Moreover, it suggests that the strategy of generating datasets using basis functions can effectively overcome the limitations caused by the scarcity of available datasets.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109710"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271686","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":"An unconditionally stable adaptive finite difference scheme for the Allen–Cahn equation","authors":"Hyundong Kim ,&nbsp;Seokjun Ham ,&nbsp;Soobin Kwak ,&nbsp;Junseok Kim","doi":"10.1016/j.cpc.2025.109712","DOIUrl":"10.1016/j.cpc.2025.109712","url":null,"abstract":"<div><div>We propose an unconditionally stable adaptive finite difference scheme for the Allen–Cahn (AC) equation. The AC equation is a reaction-diffusion equation used to model phase separation in multi-component alloy systems. It describes the temporal evolution of the order parameter, which denotes different phases, and incorporates both diffusion and nonlinear reaction terms to capture the interfacial dynamics between phases. A fundamental aspect of the dynamics of the AC equation is motion by mean curvature, which implies that an initial interface shrinks as time progresses. Therefore, it is highly efficient to reduce the computational domain as the interface shrinks. We use an operator splitting technique with a finite difference method and a closed-form solution. We conduct computational tests to validate the effectiveness of the proposed approach. The computational tests demonstrate that the proposed algorithm is effective, reliable, and robust across various test cases.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109712"},"PeriodicalIF":7.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271687","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":"Machine learning-assisted identification of potential sources of bias in measurements of prompt-fission neutron spectra","authors":"Noah A.W. Walton ,&nbsp;Denise Neudecker ,&nbsp;Scott A. Vander Wiel ,&nbsp;Michael J. Grosskopf ,&nbsp;Keegan J. Kelly","doi":"10.1016/j.cpc.2025.109698","DOIUrl":"10.1016/j.cpc.2025.109698","url":null,"abstract":"<div><div>Unrecognized sources of uncertainty (USU) can bias the reported mean and/or covariance of experimental nuclear data. These biases, in turn, can propagate through evaluated nuclear data to application simulations or may poorly inform nuclear theory that is fitted to the experimental data. Such unknown sources of bias must be tied to the inherent physical constituents of the measurements such as the characteristics of a detector response or a background reduction technique. In this article, a sparse Bayesian learning model is used to support experts in their efforts to identify and characterize USU in experimental prompt fission neutron spectra (PFNS) for spontaneous fissioning of ²⁵²Cf by linking observed biases to features of the measurement system. Three different bias components were found. The first acts as a verification case for the algorithm as it identifies a bias coming from a well-known source related to the use of ⁶Li in the neutron detection system. The second two cases demonstrate how this method can benefit the evaluation of experimental nuclear data by identifying, quantifying, and relating unknown biases to potential causes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109698"},"PeriodicalIF":7.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263343","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":"CooLBM: A GPU-accelerated collaborative open-source reactive multi-phase/component simulation code via lattice Boltzmann method","authors":"R. Alamian ,&nbsp;A.K. Nayak ,&nbsp;M.S. Shadloo","doi":"10.1016/j.cpc.2025.109711","DOIUrl":"10.1016/j.cpc.2025.109711","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The current work presents a novel &lt;em&gt;COllaborative Open-source Lattice Boltzmann Method&lt;/em&gt; framework, so-called &lt;em&gt;CooLBM&lt;/em&gt;. The computational framework is developed for the simulation of single and multi-component multi-phase problems, along with a reactive interface and conjugate fluid-solid heat transfer problems. CooLBM utilizes a multi-CPU/GPU architecture to achieve high-performance computing (HPC), enabling efficient and parallelized simulations for large scale problems. The code is implemented in C++ and makes extensive use of the Standard Template Library (STL) to improve code modularity, flexibility, and re-usability. The developed framework incorporates advanced numerical methods and algorithms to accurately capture complex fluid dynamics and phase interactions. It offers a wide range of capabilities, including phase separation, interfacial tension, and mass transfer phenomena. The reactive interface simulation module enables the study of chemical reactions occurring at the fluid-fluid interface, expanding its applicability to reactive multi-phase systems. The performance and accuracy of CooLBM are demonstrated through various benchmark simulations, showcasing its ability to capture intricate fluid behaviors and interface dynamics. The modular structure of the code allows for easy customization and extension, facilitating the implementation of additional models and boundary conditions. Finally, CooLBM provides visualization tools for the analysis and interpretation of simulation results. Overall, CooLBM offers an efficient computational framework for studying complex multi-phase systems and reactive interfaces, making it a valuable tool for researchers and engineers in several fields including, but not limited to chemical engineering, materials science, and environmental engineering. CooLBM is available under open source initiatives for scientific communities in the gitlab repository: &lt;span&gt;&lt;span&gt;https://gitlab.coria-cfd.fr/lbm/coolbm&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;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; CooLBM: A GPU-Accelerated Collaborative Open-Source Reactive Multi-Phase/Component Simulation Code via Lattice Boltzmann Method&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/10.17632/79p5z26scz.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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://gitlab.coria-cfd.fr/lbm/coolbm&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 GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; C++&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Multi-phase and multi-component reactive flows are fundamental to numerous industrial and engineering applications, including combustion systems, energy storage, and environmental processes. These flows often exhibit complex interactions across multiple spatial and temporal scales, requiring numerical modeling to accurately capture the underlying physi","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109711"},"PeriodicalIF":7.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253344","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":"A GPU-based compact conservative characteristic finite volume parallel algorithm for advection diffusion equations","authors":"Guiyu Wang ,&nbsp;Linjie Zhang ,&nbsp;Shusen Xie ,&nbsp;Dong Liang ,&nbsp;Kai Fu","doi":"10.1016/j.cpc.2025.109693","DOIUrl":"10.1016/j.cpc.2025.109693","url":null,"abstract":"<div><div>This study introduces a novel parallel algorithm for efficiently solving two-dimensional advection-diffusion problems with constant diffusion coefficient, specifically designed for implementation on GPU. The algorithm is compact, conservative, and inherently parallel, offering second-order accuracy in time and fourth-order accuracy in space. It employs a second-order operator splitting method to decompose the two-dimensional problem into one-dimensional subproblems, significantly enhancing parallelism in computations. The convective term is addressed using the characteristic method, which ensures high accuracy in time and allows for larger time steps. The conservative interpolation technique is implemented for integration within the Lagrangian tracking cell. For the diffusion term, we average along the characteristic curves and derive the discrete fluxes that are continuous at the cell boundaries. Taking advantage of the compact scheme, only three cells are required for the unknowns to achieve spatial fourth order accuracy. The primary computational tasks are performed on the GPU, distributing the computational load evenly across multiple cores. Numerical experiments demonstrate the conservation property and convergence rates of the new algorithm and its effectiveness in solving problems with steep fronts. The results also indicate the algorithm's superior computational speed compared to traditional CPU computations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109693"},"PeriodicalIF":7.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242151","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":"PINTO: Physics-informed transformer neural operator for learning generalized solutions of partial differential equations for any initial and boundary condition","authors":"Sumanth Kumar Boya,&nbsp;Deepak N. Subramani","doi":"10.1016/j.cpc.2025.109702","DOIUrl":"10.1016/j.cpc.2025.109702","url":null,"abstract":"<div><div>Applications in physics, engineering, mechanics, and fluid dynamics necessitate solving nonlinear partial differential equations (PDEs) with different initial and boundary conditions. Operator learning, an emerging field, solves these PDEs by employing neural networks to map the infinite-dimensional input and output function spaces. These neural operators are trained using data (observations or simulations) and PDE residuals (physics loss). A key limitation of current neural methods is the need to retrain for new initial/boundary conditions and the substantial simulation data required for training. We introduce a physics-informed transformer neural operator (named PINTO) that generalizes efficiently to new conditions, trained solely with physics loss in a simulation-free setting. Our core innovation is the development of iterative kernel integral operator units that use cross-attention to transform domain points of PDE solutions into initial/boundary condition-aware representation vectors, supporting efficient and generalizable learning. The working of PINTO is demonstrated by simulating important 1D and 2D equations used in fluid mechanics, physics and engineering applications: advection, Burgers, and steady and unsteady Navier-Stokes equations (three flow scenarios). We show that under challenging unseen conditions, the relative errors compared to analytical or numerical (finite difference and volume) solutions are low, merely 20% to 33% of those obtained by other leading physics-informed neural operator methods. Furthermore, PINTO accurately solves advection and Burgers equations at time steps not present in the training points, an ability absent for other neural operators. The code is accessible at <span><span>https://github.com/quest-lab-iisc/PINTO</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109702"},"PeriodicalIF":7.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242150","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":"Semtex: Development and application of the solver methodology for incompressible flows with generalized Newtonian rheologies","authors":"H.M. Blackburn ,&nbsp;M. Rudman ,&nbsp;J. Singh","doi":"10.1016/j.cpc.2025.109694","DOIUrl":"10.1016/j.cpc.2025.109694","url":null,"abstract":"<div><div>The methodology for simulation of incompressible flows with generalized Newtonian viscosity models, for example shear-thinning rheologies, within the <em>Semtex</em> framework of open-source spectral-element/Fourier flow solvers [1,2] is outlined. Direction is given regarding the rheology models employed and how appropriate parameters are derived and supplied to the solver. Exponential spatial convergence of solutions is demonstrated for both Cartesian and cylindrical geometries. Other example applications deal with DNS of turbulent flows in pipes. We use <em>Semtex</em> to highlight the central importance of adequate rheology characterization for accurate simulation of turbulent flows of generalized Newtonian fluids.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109694"},"PeriodicalIF":7.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242149","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":"qlbm – A quantum lattice Boltzmann software framework","authors":"Călin A. Georgescu,&nbsp;Merel A. Schalkers,&nbsp;Matthias Möller","doi":"10.1016/j.cpc.2025.109699","DOIUrl":"10.1016/j.cpc.2025.109699","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We present &lt;span&gt;qlbm&lt;/span&gt;, a Python software package designed to facilitate the development, simulation, and analysis of Quantum Lattice Boltzmann Methods (QBMs). &lt;span&gt;qlbm&lt;/span&gt; is a modular framework that introduces a quantum component abstraction hierarchy tailored to the implementation of novel QBMs. The framework interfaces with state-of-the-art quantum software infrastructure to enable efficient simulation and validation pipelines, and leverages novel execution and pre-processing techniques that significantly reduce the computational resources required to develop quantum circuits. We demonstrate the versatility of the software by showcasing multiple QBMs in 2D and 3D with complex boundary conditions, integrated within automated benchmarking utilities. Accompanying the source code are extensive test suites, thorough online documentation resources, analysis tools, visualization methods, and demos that aim to increase the accessibility of QBMs while encouraging reproducibility and collaboration.&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; &lt;span&gt;qlbm&lt;/span&gt;&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/10.17632/28hkvsg7p2.1&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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/QCFD-Lab/qlbm&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; MPL-2.0&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; The documentation of is available at &lt;span&gt;&lt;span&gt;https://qcfd-lab.github.io/qlbm/&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;Nature of problem:&lt;/em&gt; The advent of quantum algorithms for computational fluid dynamics brings with it challenges that are new to the established field of computational physics. These challenges include the lack of standardized implementations of the still nascent quantum methods, the intense computational demands of developing and simulating quantum algorithms on hardware available today, and the absence of tools that integrate novel developments into established infrastructure. Because of these current limitations, physicists and mathematicians expend superfluous resources on tasks that more mature computational physics branches have surmounted long ago.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; QLBM is a software package that provides an end-to-end development environment for quantum lattice Boltzmann methods. The modular design and flexible quantum circuit library provide a base for extending and generalizing quantum algorithms. Performance enhancements exploit the paradigm of quantum computing simulations to accelerate the speed at which researchers can verify the validity of their methods. Its integration with state-of-the-art quantum computing software and visualization tools increases the algorithms' accessibility. These features allow QLBM to effectively generate, simulate, and analyze quantum circuits for 2D","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109699"},"PeriodicalIF":7.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242153","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":"Parallel diffusion operator for magnetized plasmas with improved spectral fidelity","authors":"Federico D. Halpern,&nbsp;Min-Gu Yoo,&nbsp;Brendan C. Lyons,&nbsp;Juan Diego Colmenares","doi":"10.1016/j.cpc.2025.109696","DOIUrl":"10.1016/j.cpc.2025.109696","url":null,"abstract":"<div><div>Diffusive transport processes in magnetized plasmas are highly anisotropic, with fast parallel transport along the magnetic field lines sometimes faster than perpendicular transport by orders of magnitude. This constitutes a major challenge for describing non-grid-aligned magnetic structures in Eulerian (grid-based) simulations. The present paper describes and validates a new method for parallel diffusion in magnetized plasmas based on the anti-symmetry representation [Halpern and Waltz, Phys. Plasmas 25, 060703 (2018)]. In the anti-symmetry formalism, diffusion manifests as a flow operator involving the logarithmic derivative of the transported quantity. Qualitative plane wave analysis shows that the new operator naturally yields better discrete spectral resolution compared to its conventional counterpart. Numerical simulations comparing the new method against existing finite difference methods are carried out, showing significant improvement. In particular, we find that combining anti-symmetry with finite differences in diagonally staggered grids essentially eliminates the so-called “artificial numerical diffusion” that affects conventional finite difference and finite volume methods.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109696"},"PeriodicalIF":7.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242256","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":"Polar Shift: Charge carrier polarization energies in organic electronic materials","authors":"K. Kaklamanis,&nbsp;D.G. Papageorgiou","doi":"10.1016/j.cpc.2025.109700","DOIUrl":"10.1016/j.cpc.2025.109700","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Electronic polarization of charge carriers in the solid state plays an important role in organic electronics, as it alters the gas phase energy levels associated with phenomena such as charge transport, molecular doping, charge injection and charge separation at interfaces. In this article we present P&lt;span&gt;olar&lt;/span&gt; S&lt;span&gt;hift&lt;/span&gt;, a software package for calculating the polarization energy of an electron or hole charge carrier in organic electronic materials. The software uses an atomistic approach employing the microelectrostatics model. Molecular charge distributions are represented by atomic point charges, while the molecular polarizability is divided into distributed atomic contributions. The electrostatic and inductive components of the polarization energy are calculated separately. For the electrostatic interactions we propose an efficient cutoff–based scheme that allows fast yet accurate evaluation of the relevant energy. For the induction part we use a self–consistent iterative method based on modified field interaction tensors in the framework of the Thole model. P&lt;span&gt;olar&lt;/span&gt; S&lt;span&gt;hift&lt;/span&gt; can be applied to ideal molecular crystals, thermally disordered crystalline packings or completely amorphous materials. Many additional features are implemented such as calculation of the molecular polarizability tensor, fitting of molecular polarizabilities to reference values, different schemes for computing induction energies, and extrapolation of induction energies to the bulk limit. Special attention has been paid to the interoperability with other software packages, so P&lt;span&gt;olar&lt;/span&gt; S&lt;span&gt;hift&lt;/span&gt; can obtain the required input from various widely used file types such as pdb, mol2 or even binary dcd files. The software is parallelized using the MPI standard thus exploiting a wide range of shared and distributed memory computer architectures. P&lt;span&gt;olar&lt;/span&gt; S&lt;span&gt;hift&lt;/span&gt; is applied to eight different test cases of prototype organic electronics materials demonstrating its capabilities, and the results are compared with existing literature.&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; P&lt;span&gt;olar&lt;/span&gt; S&lt;span&gt;hift&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;&lt;span&gt;https://doi.org/10.17632/26ck9stzh9.1&lt;/span&gt;&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;&lt;span&gt;http://cmsl.materials.uoi.gr/polar-shift&lt;/span&gt;&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; GPLv2&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Fortran 2008&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; User manual (45 pages), 22 annotated examples with reference output, input and output files for the eight test cases described in the paper.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Electronic polarization of charge carriers in organic electronic materials is responsible for altering key quantities from their gas phase coun","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"315 ","pages":"Article 109700"},"PeriodicalIF":7.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242152","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}