Pub Date : 2025-12-30DOI: 10.1016/j.cpc.2025.110011
Ossiel Aguilar-Spíndola, Francisco Sánchez-Ochoa
<div><div>Many first-principles packages employ periodic and symmetry conditions to reduce the computational time and cost. The supercell (SC) method is useful to address periodic systems with different physical perturbations; however, the theoretical definition of a specific SC is a real challenge in Crystallography and Solid State Physics studies. In particular, whether the system is commensurable and made of several two-dimensional (2D) layers with different Bravais lattice, initial local stacking, and interlayer relative orientation. This work presents Nookiin (from the junction of Yucatec Maya words, Nook: ’<em>knit</em>’ or ’<em>wave</em>’; and iin: ’<em>me</em>’), an open-source Python code, designed for the efficient generation of commensurable SCs using geometric methods. Nookiin has an efficient algorithm that minimizes structural distortions at a geometric level, providing an optimized approach for representing 2D heterostructures with a reduced number of atoms. Its modular architecture facilitates adaptation to different problems. Its use through both an interactive console interface and programmatic implementation allows seamless integration into scientific workflows. Additionally, Nookiin offers tools for structural visualization and export of configurations compatible with first-principles codes such as the Vienna <em>ab initio</em> Simulation Package (VASP) code [17]. This report presents the theoretical foundations of the method, the computational implementation of the algorithm, and the results obtained that validate its effectiveness in generating commensurable SCs. With these characteristics, Nookiin establishes itself as a versatile and alternative resource for research in Solid State Physics and Materials Science. The software is openly available at <span><span>github.com/OssielAg/Nook-iin</span><svg><path></path></svg></span>, with a citable release archived at <span><span>doi.org/10.5281/zenodo.15706528</span><svg><path></path></svg></span>.</div><div><strong>Program Summary</strong></div><div><em>Program Title:</em> Nookiin</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/yvxpwg8sx6.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/OssielAg/Nook-iin</span><svg><path></path></svg></span>, <span><span>https://doi.org/10.5281/zenodo.14257396</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPL-3.0</div><div><em>Programming language:</em> Python</div><div><em>External routines/libraries:</em> numpy, matplotlib</div><div><em>Nature of problem:</em> Van der Waals heterostructures usually comprise many layers with different composition, initial local stacking, Bravais lattices and relative interlayer orientation due to weak dispersion forces. The definition of a periodic and commensurable common cell is a challenge, if physical properties are studied by first principles calculations within
{"title":"Nookiin: Python software to build commensurable multilayer heterostructures","authors":"Ossiel Aguilar-Spíndola, Francisco Sánchez-Ochoa","doi":"10.1016/j.cpc.2025.110011","DOIUrl":"10.1016/j.cpc.2025.110011","url":null,"abstract":"<div><div>Many first-principles packages employ periodic and symmetry conditions to reduce the computational time and cost. The supercell (SC) method is useful to address periodic systems with different physical perturbations; however, the theoretical definition of a specific SC is a real challenge in Crystallography and Solid State Physics studies. In particular, whether the system is commensurable and made of several two-dimensional (2D) layers with different Bravais lattice, initial local stacking, and interlayer relative orientation. This work presents Nookiin (from the junction of Yucatec Maya words, Nook: ’<em>knit</em>’ or ’<em>wave</em>’; and iin: ’<em>me</em>’), an open-source Python code, designed for the efficient generation of commensurable SCs using geometric methods. Nookiin has an efficient algorithm that minimizes structural distortions at a geometric level, providing an optimized approach for representing 2D heterostructures with a reduced number of atoms. Its modular architecture facilitates adaptation to different problems. Its use through both an interactive console interface and programmatic implementation allows seamless integration into scientific workflows. Additionally, Nookiin offers tools for structural visualization and export of configurations compatible with first-principles codes such as the Vienna <em>ab initio</em> Simulation Package (VASP) code [17]. This report presents the theoretical foundations of the method, the computational implementation of the algorithm, and the results obtained that validate its effectiveness in generating commensurable SCs. With these characteristics, Nookiin establishes itself as a versatile and alternative resource for research in Solid State Physics and Materials Science. The software is openly available at <span><span>github.com/OssielAg/Nook-iin</span><svg><path></path></svg></span>, with a citable release archived at <span><span>doi.org/10.5281/zenodo.15706528</span><svg><path></path></svg></span>.</div><div><strong>Program Summary</strong></div><div><em>Program Title:</em> Nookiin</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/yvxpwg8sx6.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/OssielAg/Nook-iin</span><svg><path></path></svg></span>, <span><span>https://doi.org/10.5281/zenodo.14257396</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPL-3.0</div><div><em>Programming language:</em> Python</div><div><em>External routines/libraries:</em> numpy, matplotlib</div><div><em>Nature of problem:</em> Van der Waals heterostructures usually comprise many layers with different composition, initial local stacking, Bravais lattices and relative interlayer orientation due to weak dispersion forces. The definition of a periodic and commensurable common cell is a challenge, if physical properties are studied by first principles calculations within ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110011"},"PeriodicalIF":3.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974178","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}
Modern heterogeneous high-performance computing (HPC) systems powered by advanced graphics processing unit (GPU) architectures enable accelerating computing with unprecedented performance and scalability. Here, we present a GPU-accelerated solver for the three-dimensional (3D) time-dependent Dirac Equation optimized for distributed HPC systems. The solver named GaDE is designed to simulate the electron dynamics in atoms induced by electromagnetic fields in the relativistic regime. It combines MPI with CUDA/HIP to target both NVIDIA and AMD GPU architectures. We discuss our implementation strategies in which the majority of the computations are carried out on GPUs, taking advantage of the GPU-aware MPI feature to optimize communication performance. We evaluate GaDE on the pre-exascale supercomputer LUMI, powered by AMD MI250X GPU and the HPE’s Slingshot interconnect. Single GPU performance on NVIDIA A100, GH200 and AMD MI250X shows comparable performance between A100 and MI250X in compute and memory bandwidth, with GH200 delivering higher performance. Weak scaling on LUMI demonstrates excellent scalability, achieving 85% parallel efficiency across 2048 GPUs, while strong scaling delivers a 16× speedup on 32 GPUs - 50% efficiency for a communication-intensive, time-dependent Dirac equation solver. These results demonstrate GaDE’s high scalability, making it suitable for exascale systems and enabling predictive simulations for ultra-intense laser experiments probing relativistic quantum effects.
{"title":"GaDE - GPU-acceleration of time-dependent Dirac equation for exascale","authors":"Johanne Elise Vembe , Marcin Krotkiewski , Magnar Bjørgve , Morten Førre , Hicham Agueny","doi":"10.1016/j.cpc.2025.110015","DOIUrl":"10.1016/j.cpc.2025.110015","url":null,"abstract":"<div><div>Modern heterogeneous high-performance computing (HPC) systems powered by advanced graphics processing unit (GPU) architectures enable accelerating computing with unprecedented performance and scalability. Here, we present a GPU-accelerated solver for the three-dimensional (3D) time-dependent Dirac Equation optimized for distributed HPC systems. The solver named GaDE is designed to simulate the electron dynamics in atoms induced by electromagnetic fields in the relativistic regime. It combines MPI with CUDA/HIP to target both NVIDIA and AMD GPU architectures. We discuss our implementation strategies in which the majority of the computations are carried out on GPUs, taking advantage of the GPU-aware MPI feature to optimize communication performance. We evaluate GaDE on the pre-exascale supercomputer LUMI, powered by AMD MI250X GPU and the HPE’s Slingshot interconnect. Single GPU performance on NVIDIA A100, GH200 and AMD MI250X shows comparable performance between A100 and MI250X in compute and memory bandwidth, with GH200 delivering higher performance. Weak scaling on LUMI demonstrates excellent scalability, achieving 85% parallel efficiency across 2048 GPUs, while strong scaling delivers a 16× speedup on 32 GPUs - 50% efficiency for a communication-intensive, time-dependent Dirac equation solver. These results demonstrate GaDE’s high scalability, making it suitable for exascale systems and enabling predictive simulations for ultra-intense laser experiments probing relativistic quantum effects.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110015"},"PeriodicalIF":3.4,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883378","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 : 2025-12-27DOI: 10.1016/j.cpc.2025.110009
Simone Bnà , Giuseppe Giaquinto , Ettore Fadiga , Tommaso Zanelli , Francesco Bottau
High Performance Computing (HPC) on hybrid clusters represents a significant opportunity for Computational Fluid Dynamics (CFD), especially when modern accelerators are utilized effectively. However, despite the widespread adoption of GPUs, programmability remains a challenge, particularly in open-source contexts. In this paper, we present SPUMA, a full GPU porting of OPENFOAM® targeting NVIDIA and AMD GPUs. The implementation strategy is based on a portable programming model and the adoption of a memory pool manager that leverages the unified memory feature of modern GPUs. This approach is discussed alongside several numerical tests conducted on two pre-exascale clusters in Europe, LUMI and Leonardo, which host AMD MI250X and NVIDIA A100 GPUs, respectively. In the performance analysis section, we present results related to memory usage profiling and kernel wall-time, the impact of the memory pool, and energy consumption obtained by simulating the well-known DrivAer industrial test case. GPU utilization strongly affects strong scalability results, reaching 65% efficiency on both LUMI and Leonardo when approaching a load of 8 million cells per GPU. Weak scalability results, obtained on 20 GPUs with the OpenFOAM native multigrid solver, range from 75% on Leonardo to 85% on LUMI. Notably, efficiency is no lower than 90% when switching to the NVIDIA AmgX linear algebra solver. Our tests also reveal that one A100 GPU on Leonardo is equivalent 200–300 Intel Sapphire Rapids cores, provided the GPUs are sufficiently oversubscribed (more than 10 million of cells per GPU). Finally, energy consumption is reduced by up to 82% compared to analogous simulations executed on CPUs.
{"title":"SPUMA: A minimally invasive approach to the GPU porting of OPENFOAM®","authors":"Simone Bnà , Giuseppe Giaquinto , Ettore Fadiga , Tommaso Zanelli , Francesco Bottau","doi":"10.1016/j.cpc.2025.110009","DOIUrl":"10.1016/j.cpc.2025.110009","url":null,"abstract":"<div><div>High Performance Computing (HPC) on hybrid clusters represents a significant opportunity for Computational Fluid Dynamics (CFD), especially when modern accelerators are utilized effectively. However, despite the widespread adoption of GPUs, programmability remains a challenge, particularly in open-source contexts. In this paper, we present SPUMA, a full GPU porting of OPENFOAM® targeting NVIDIA and AMD GPUs. The implementation strategy is based on a portable programming model and the adoption of a memory pool manager that leverages the unified memory feature of modern GPUs. This approach is discussed alongside several numerical tests conducted on two pre-exascale clusters in Europe, LUMI and Leonardo, which host AMD MI250X and NVIDIA A100 GPUs, respectively. In the performance analysis section, we present results related to memory usage profiling and kernel wall-time, the impact of the memory pool, and energy consumption obtained by simulating the well-known DrivAer industrial test case. GPU utilization strongly affects strong scalability results, reaching 65% efficiency on both LUMI and Leonardo when approaching a load of 8 million cells per GPU. Weak scalability results, obtained on 20 GPUs with the OpenFOAM native multigrid solver, range from 75% on Leonardo to 85% on LUMI. Notably, efficiency is no lower than 90% when switching to the NVIDIA AmgX linear algebra solver. Our tests also reveal that one A100 GPU on Leonardo is equivalent 200–300 Intel Sapphire Rapids cores, provided the GPUs are sufficiently oversubscribed (more than 10 million of cells per GPU). Finally, energy consumption is reduced by up to 82% compared to analogous simulations executed on CPUs.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110009"},"PeriodicalIF":3.4,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034991","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 : 2025-12-26DOI: 10.1016/j.cpc.2025.110012
Dandan Liao , Lei Ye , Pengfei Zhao , Qilong Ren , Nong Xiang
A second-order semi-implicit time integration method has been developed for solving the linearized collision operator in multi-species plasmas. A key feature of this method is that it treats the collision operator as a single entity, avoiding the operator splitting between its test particle and field particle components. This scheme employs an implicit trapezoidal time integration scheme for the isothermal test particle part (including pitch-angle scattering and energy diffusion) with a finite volume discretization in (v∥, μ) velocity coordinates, while the non-isothermal model term and field particle part are treated explicitly. This approach avoids the species cross-coupling required by fully implicit schemes, enabling the collision term to be computed efficiently with a banded/sparse-matrix solver. The method has been implemented in the gyrokinetic semi-Lagrangian code NLT and verified through multi-species relaxation tests and neoclassical transport simulations. Numerical benchmarks against explicit methods confirm its robustness, achieving order-of-magnitude improvements in the allowable time-step size, particularly in simulations of electron-ion collisions. Furthermore, the numerical discretization rigorously preserves particle number, momentum, and energy conservation, maintains the self-adjoint property of the collision operator, and satisfies Boltzmann’s H-theorem.
{"title":"Semi-implicit scheme for multi-species collision operators in Tokamak Plasma Simulations","authors":"Dandan Liao , Lei Ye , Pengfei Zhao , Qilong Ren , Nong Xiang","doi":"10.1016/j.cpc.2025.110012","DOIUrl":"10.1016/j.cpc.2025.110012","url":null,"abstract":"<div><div>A second-order semi-implicit time integration method has been developed for solving the linearized collision operator in multi-species plasmas. A key feature of this method is that it treats the collision operator as a single entity, avoiding the operator splitting between its test particle and field particle components. This scheme employs an implicit trapezoidal time integration scheme for the isothermal test particle part (including pitch-angle scattering and energy diffusion) with a finite volume discretization in (<em>v</em><sub>∥</sub>, <em>μ</em>) velocity coordinates, while the non-isothermal model term and field particle part are treated explicitly. This approach avoids the species cross-coupling required by fully implicit schemes, enabling the collision term to be computed efficiently with a banded/sparse-matrix solver. The method has been implemented in the gyrokinetic semi-Lagrangian code NLT and verified through multi-species relaxation tests and neoclassical transport simulations. Numerical benchmarks against explicit methods confirm its robustness, achieving order-of-magnitude improvements in the allowable time-step size, particularly in simulations of electron-ion collisions. Furthermore, the numerical discretization rigorously preserves particle number, momentum, and energy conservation, maintains the self-adjoint property of the collision operator, and satisfies Boltzmann’s H-theorem.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 110012"},"PeriodicalIF":3.4,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880486","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 : 2025-12-23DOI: 10.1016/j.cpc.2025.110010
Taeyoung Jeong , Kun Hee Ye , Seungjae Yoon , Dohyun Kim , Yunjae Kim , Cheol Seong Hwang , Jung-Hae Choi
<div><div>Multiscale modeling, which integrates material properties from <em>ab initio</em> calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while <em>ab initio</em> methods provide diffusion parameters specific to individual migration paths, continuum equations require a single effective set of parameters that captures the overall diffusion behavior. To address this issue, we present <em>VacHopPy</em>, an open-source Python package for vacancy hopping analysis based on molecular dynamics (MD). <em>VacHopPy</em> extracts an effective set of hopping parameters, including hopping distance, hopping barrier, number of effective paths, correlation factor, and attempt frequency, by statistically integrating energetic, kinetic, and geometric contributions across all paths. It also includes tools for tracking vacancy trajectories and for detecting phase transitions during MD simulations. The applicability of <em>VacHopPy</em> is demonstrated in three representative materials: face-centered cubic Al, rutile TiO<sub>2</sub>, and monoclinic HfO<sub>2</sub>. The extracted effective parameters reproduce temperature-dependent diffusion behavior and are in good agreement with previous experimental data. Provided in a simplified form, these parameters are well suited for continuum-scale models and remain valid over a wide temperature range spanning several hundred kelvins. Furthermore, <em>VacHopPy</em> inherently accounts for anisotropy in thermal vibrations, a factor often overlooked, making it suitable for simulating diffusion in complex crystals. Overall, <em>VacHopPy</em> establishes a robust bridge between atomic- and continuum-scale models, enabling more reliable multiscale simulations.</div><div><strong>Program Summary</strong></div><div><em>Program Title: VacHopPy</em></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/nfd44zrb24.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/TY-Jeong/VacHopPy</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT License</div><div><em>Programming language:</em> Python</div><div><em>Supplementary material:</em> Supplementary Figures (S1–S11), Supplementary Tables (S1–S6), and Supplementary Notes (1–4) are provided in a separate PDF file.</div><div><em>Nature of problem:</em> For modeling of vacancy-mediated diffusion, <em>ab initio</em> calculations provide path-specific diffusion parameters that are not directly compatible with continuum-scale models, which typically require a single set of effective parameters. Such incompatibility poses a significant challenge in accurately integrating atomistic diffusion behavior into multiscale simulation frameworks, particularly when multiple hopping paths exist in a material system.</div><div><em>Solution method:</em> Vacancy trajectories are identif
{"title":"VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations","authors":"Taeyoung Jeong , Kun Hee Ye , Seungjae Yoon , Dohyun Kim , Yunjae Kim , Cheol Seong Hwang , Jung-Hae Choi","doi":"10.1016/j.cpc.2025.110010","DOIUrl":"10.1016/j.cpc.2025.110010","url":null,"abstract":"<div><div>Multiscale modeling, which integrates material properties from <em>ab initio</em> calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while <em>ab initio</em> methods provide diffusion parameters specific to individual migration paths, continuum equations require a single effective set of parameters that captures the overall diffusion behavior. To address this issue, we present <em>VacHopPy</em>, an open-source Python package for vacancy hopping analysis based on molecular dynamics (MD). <em>VacHopPy</em> extracts an effective set of hopping parameters, including hopping distance, hopping barrier, number of effective paths, correlation factor, and attempt frequency, by statistically integrating energetic, kinetic, and geometric contributions across all paths. It also includes tools for tracking vacancy trajectories and for detecting phase transitions during MD simulations. The applicability of <em>VacHopPy</em> is demonstrated in three representative materials: face-centered cubic Al, rutile TiO<sub>2</sub>, and monoclinic HfO<sub>2</sub>. The extracted effective parameters reproduce temperature-dependent diffusion behavior and are in good agreement with previous experimental data. Provided in a simplified form, these parameters are well suited for continuum-scale models and remain valid over a wide temperature range spanning several hundred kelvins. Furthermore, <em>VacHopPy</em> inherently accounts for anisotropy in thermal vibrations, a factor often overlooked, making it suitable for simulating diffusion in complex crystals. Overall, <em>VacHopPy</em> establishes a robust bridge between atomic- and continuum-scale models, enabling more reliable multiscale simulations.</div><div><strong>Program Summary</strong></div><div><em>Program Title: VacHopPy</em></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/nfd44zrb24.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/TY-Jeong/VacHopPy</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT License</div><div><em>Programming language:</em> Python</div><div><em>Supplementary material:</em> Supplementary Figures (S1–S11), Supplementary Tables (S1–S6), and Supplementary Notes (1–4) are provided in a separate PDF file.</div><div><em>Nature of problem:</em> For modeling of vacancy-mediated diffusion, <em>ab initio</em> calculations provide path-specific diffusion parameters that are not directly compatible with continuum-scale models, which typically require a single set of effective parameters. Such incompatibility poses a significant challenge in accurately integrating atomistic diffusion behavior into multiscale simulation frameworks, particularly when multiple hopping paths exist in a material system.</div><div><em>Solution method:</em> Vacancy trajectories are identif","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 110010"},"PeriodicalIF":3.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880079","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 : 2025-12-21DOI: 10.1016/j.cpc.2025.110001
David Andrs , Zachary Hardy , Daryl Hawkins , Jim Morel , Dinh Quoc Dang Nguyen , Jean C. Ragusa
<div><div>OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates (S<sub><em>N</em></sub>) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy.</div><div>OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures.</div><div>Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence.</div><div>OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.</div><div><strong>PROGRAM SUMMARY</strong></div><div><strong>Program Title:</strong> OpenSn</div><div><strong>CPC Library link to program files:</strong> <span><span>https://doi.org/10.17632/gvrs69dzcv.1</span><svg><path></path></svg></span></div><div><strong>Developer’s repository link:</strong> <span><span>https://github.com/Open-Sn/OpenSn</span><svg><path></path></svg></span></div><div><strong>Licensing provisions:</strong> MIT license</div><div><strong>Programming language:</strong> C++ (core), Python (API)</div><div><strong>Supplementary material:</strong> User manual, theory documentation, and tutorial notebooks available at <span><span>https://open-sn.github.io/opensn/</span><svg><path></path></svg></span></div><div><strong>Nature of problem:</strong>Radiation transport simulations are central to numerous applications in physics and engineering, including reactor analysis, shielding, radiography, and detector modeling. Solving the linear Boltzmann transport equation in its discrete-ordinates form (S<sub><em>N</em></sub>) on complex geometries requires robust numerical methods and scalable parallel algorithms. Many existing codes are closed-source, lack support for polyhedral meshes, or do not efficiently exploit modern HPC systems. A flexible, open-source tool is needed to address these challenges while supporting methodological innovation and large-scale computation.</div><div><strong>Solution method:</strong>
OpenSn是一个开源的、大规模并行的确定性辐射输运代码，用于求解非结构化、任意多面体网格上玻尔兹曼输运方程的离散坐标（SN）形式。它支持高保真模拟涉及稳态，特征值，和伴随问题的中性粒子（例如，中子，光子，多粒子），使用多群近似的能量。OpenSn结合了离散坐标的角度离散化和空间中的不连续Galerkin有限元法（DGFEM），可以在任意多面体单元（包括局部细化的空间网格）上实现精确的传输物理分辨率。它包括多种角正交类型，包括局部精细角正交。OpenSn使用现代c++和Python API编写，可以在从笔记本电脑到超级计算机的各种平台上高效运行。传输扫描算法对每个角度使用基于任务的定向无循环图（DAG）方法实现，并支持跨数千个MPI等级的异步并行性。群集聚集提高了计算强度，合成加速技术（如扩散合成加速、二阶矩法）增强了求解器的收敛性。OpenSn已经在反应堆物理问题上进行了验证，并在超过32,768个过程中展示了出色的弱和强缩放性能，使其成为复杂几何结构中大规模传输模拟的通用和健壮的平台。项目简介项目名称：OpenSnCPC库链接到程序文件：https://doi.org/10.17632/gvrs69dzcv.1Developer的存储库链接：https://github.com/Open-Sn/OpenSnLicensing条款：MIT许可编程语言：c++（核心），Python （API）补充材料：用户手册，理论文档和教程笔记本可在https://open-sn.github.io/opensn/Nature的问题：辐射输运模拟是核心的许多应用在物理和工程，包括反应堆分析，屏蔽，射线照相，和探测器建模。求解复杂几何上离散坐标形式的线性玻尔兹曼输运方程需要鲁棒的数值方法和可扩展的并行算法。许多现有的代码是闭源的，缺乏对多面体网格的支持，或者不能有效地利用现代高性能计算系统。在支持方法创新和大规模计算的同时，需要一个灵活的开源工具来解决这些挑战。求解方法：OpenSn利用能量上的多群近似、空间上的不连续伽辽金有限元法（DGFEM）和角度上的配点法求解离散坐标玻尔兹曼输运方程[1]的稳态、特征值和伴随形式。它支持任意的非结构化多边形和多面体网格，以及角正交集。传输扫描使用基于有向无循环图（DAG）的任务执行模型实现，支持高度可扩展的基于mpi的并行性[2]。代码是用c++编写的，并提供了一个Python接口用于预处理和后处理。采用加速技术[3,4]，包括扩散合成加速（DSA）和基于第二矩的方法，以提高收敛性。OpenSn已经在数千个核心上进行了测试，并根据已知的基准进行了验证。其他评论包括限制和不寻常的功能：OpenSn被设计为一个研究级的，可扩展的高保真辐射传输模拟平台。它特别适合于对实验新的数值方法、网格类型和求解器加速策略感兴趣的用户。代码具有最小的外部依赖，使用CMake进行构建，并包含示例问题和教程。GPU加速正在开发中。对问题的大小没有特别的限制，但是大规模的模拟需要访问并行计算资源。李建军，李建军，李建军，中子输运的计算方法，原子物理学报，1993.2.J。I. C. Vermaak， J. C. Ragusa， M. L. Adams和J. E. Morel。，“基于循环依赖的网格的大规模并行传输扫描”，计算物理学报，42 (10):1098 - 992,2021.1 . m。L. Adams和E. W. Larsen，“离散坐标粒子输运计算的快速迭代方法”，硕士论文。诊断。能源学报，40(1):3-159,2002.4.B。李志刚，“二维任意多边形网格中SN输运的非连续扩散合成加速度，”计算物理学报，34(4):356-369,2014。
{"title":"OpenSn: A massively parallel, open-source simulation environment for discrete ordinates radiation transport","authors":"David Andrs , Zachary Hardy , Daryl Hawkins , Jim Morel , Dinh Quoc Dang Nguyen , Jean C. Ragusa","doi":"10.1016/j.cpc.2025.110001","DOIUrl":"10.1016/j.cpc.2025.110001","url":null,"abstract":"<div><div>OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates (S<sub><em>N</em></sub>) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy.</div><div>OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures.</div><div>Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence.</div><div>OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.</div><div><strong>PROGRAM SUMMARY</strong></div><div><strong>Program Title:</strong> OpenSn</div><div><strong>CPC Library link to program files:</strong> <span><span>https://doi.org/10.17632/gvrs69dzcv.1</span><svg><path></path></svg></span></div><div><strong>Developer’s repository link:</strong> <span><span>https://github.com/Open-Sn/OpenSn</span><svg><path></path></svg></span></div><div><strong>Licensing provisions:</strong> MIT license</div><div><strong>Programming language:</strong> C++ (core), Python (API)</div><div><strong>Supplementary material:</strong> User manual, theory documentation, and tutorial notebooks available at <span><span>https://open-sn.github.io/opensn/</span><svg><path></path></svg></span></div><div><strong>Nature of problem:</strong>Radiation transport simulations are central to numerous applications in physics and engineering, including reactor analysis, shielding, radiography, and detector modeling. Solving the linear Boltzmann transport equation in its discrete-ordinates form (S<sub><em>N</em></sub>) on complex geometries requires robust numerical methods and scalable parallel algorithms. Many existing codes are closed-source, lack support for polyhedral meshes, or do not efficiently exploit modern HPC systems. A flexible, open-source tool is needed to address these challenges while supporting methodological innovation and large-scale computation.</div><div><strong>Solution method:</strong>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 110001"},"PeriodicalIF":3.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920953","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 : 2025-12-20DOI: 10.1016/j.cpc.2025.110005
Jianqi Huang , Renhui Liu , Ye Zhang , Nguyen Tuan Hung , Huaihong Guo , Riichiro Saito , Teng Yang
We present an open-source program QR2-code that computes double-resonance Raman (DRR) spectra using first-principles calculations. QR2-code can calculate not only two-phonon DRR spectra but also single-resonance Raman spectra and defect-induced DRR spectra. For defect-induced DRR spectra, we simply assume that the electron-defect matrix element of elastic scattering is a constant. Hands-on tutorials for graphene are given to show how to run QR2-code for single-resonance, double-resonance, and defect-induced Raman spectra. We also compare the single-resonance Raman spectra by QR2-code with that by QERaman code. In QR2-code, the Raman spectrum is calculated by the time-dependent perturbation theory, in which the energy dispersions of electron and phonon are taken from Quantum ESPRESSO (QE) code and the electron-phonon matrix element is obtained from the modified Electron-Phonon-Wannier (EPW) code. All codes, examples, and scripts are available on the GitHub repository.
Program Summary
Program Title:QR2-code
CPC Library link to program files:https://doi.org/10.17632/vstc3hx5bs.1
Nature of problem:Single-resonance, double-resonance, and defect-induced Raman spectra with first-principles calculations.
Solution method:The Raman spectrum is calculated by the time-dependent perturbation theory, in which the energy dispersions of electron and phonon and electron-phonon matrix elements are obtained from the Quantum ESPRESSO and modified EPW codes. Supplementary material:http://qr2-code.com
{"title":"QR2-code: An open-source program for double resonance Raman spectra","authors":"Jianqi Huang , Renhui Liu , Ye Zhang , Nguyen Tuan Hung , Huaihong Guo , Riichiro Saito , Teng Yang","doi":"10.1016/j.cpc.2025.110005","DOIUrl":"10.1016/j.cpc.2025.110005","url":null,"abstract":"<div><div>We present an open-source program <span>QR<sup>2</sup>-code</span> that computes double-resonance Raman (DRR) spectra using first-principles calculations. <span>QR<sup>2</sup>-code</span> can calculate not only two-phonon DRR spectra but also single-resonance Raman spectra and defect-induced DRR spectra. For defect-induced DRR spectra, we simply assume that the electron-defect matrix element of elastic scattering is a constant. Hands-on tutorials for graphene are given to show how to run <span>QR<sup>2</sup>-code</span> for single-resonance, double-resonance, and defect-induced Raman spectra. We also compare the single-resonance Raman spectra by <span>QR<sup>2</sup>-code</span> with that by <span>QERaman</span> code. In <span>QR<sup>2</sup>-code</span>, the Raman spectrum is calculated by the time-dependent perturbation theory, in which the energy dispersions of electron and phonon are taken from <span>Quantum ESPRESSO</span> (<span>QE</span>) code and the electron-phonon matrix element is obtained from the modified Electron-Phonon-Wannier (<span>EPW</span>) code. All codes, examples, and scripts are available on the GitHub repository.</div><div><strong>Program Summary</strong></div><div><em>Program Title:</em> <span>QR<sup>2</sup>-code</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/vstc3hx5bs.1</span><svg><path></path></svg></span></div><div><em>Developer’s repository link:</em> <span><span>https://github.com/JoeyyHuang/QR2-code</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public Licence 3.0</div><div><em>Programming language:</em> Fortran</div><div><em>External routines</em>: <span>Quantum ESPRESSO v7.3.1</span>, <span>EPW v5.8.1</span></div><div><em>Nature of problem:</em>Single-resonance, double-resonance, and defect-induced Raman spectra with first-principles calculations.</div><div><em>Solution method:</em>The Raman spectrum is calculated by the time-dependent perturbation theory, in which the energy dispersions of electron and phonon and electron-phonon matrix elements are obtained from the <span>Quantum ESPRESSO</span> and modified <span>EPW</span> codes. <em>Supplementary material:</em> <span><span>http://qr2-code.com</span><svg><path></path></svg></span></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 110005"},"PeriodicalIF":3.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880068","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 : 2025-12-20DOI: 10.1016/j.cpc.2025.109998
Sherryn MacLeod , Klaudiusz Jakubowski , James Vohradsky , Daniel R. Franklin , Toshiro Sakabe , Akram Hamato , Masahiro Okamura , Susanna Guatelli , Mitra Safavi-Naeini
The increasing adoption of accelerator-based neutron sources (ABNS) for applications including neutron capture therapy (NCT) research has highlighted the need for accurate simulation tools. Precise modelling of the neutron production target is crucial to ensure that simulated predictions of neutron beam characteristics used for subsequent beam shaping assembly design are reliable. This work presents a comprehensive benchmarking of four widely-used Monte Carlo codes - Geant4, PHITS, FLUKA (CERN), and MCNP - for modelling low-energy neutron production target reactions. Using their recommended physics models and cross-section libraries, we evaluate each code’s performance in simulating four beam-target reactions: 7Li(p,n)7Be, 9Be(p,n)9B, 9Be(d,n)10B, and C(d,n)N. Predictions of neutron yield, angular distributions, and energy spectra are compared against available thick target experimental data. Results show varying levels of agreement between the codes depending on the reaction type, energy range, and beam characteristics. Geant4, MCNP and PHITS are the overall best performing codes for the simulation of total neutron yield and yield in the forward direction across most reactions. Across energies where experimental benchmarks exist, inter-code discrepancies in total and forward-directed yield are typically 10 to 30%, with larger deviations at near-threshold incident ion energies. PHITS provides the best overall reproduction of experimental spectra, particularly for the 9Be(p,n)9B reaction. Additionally, PHITS demonstrates superior computational performance for most reactions. These findings provide valuable guidance for ABNS design, highlighting the strengths and limitations of each code for the simulation of low-energy neutron production reactions.
{"title":"Benchmarking Monte Carlo codes for the modelling of low-energy neutron production target reactions","authors":"Sherryn MacLeod , Klaudiusz Jakubowski , James Vohradsky , Daniel R. Franklin , Toshiro Sakabe , Akram Hamato , Masahiro Okamura , Susanna Guatelli , Mitra Safavi-Naeini","doi":"10.1016/j.cpc.2025.109998","DOIUrl":"10.1016/j.cpc.2025.109998","url":null,"abstract":"<div><div>The increasing adoption of accelerator-based neutron sources (ABNS) for applications including neutron capture therapy (NCT) research has highlighted the need for accurate simulation tools. Precise modelling of the neutron production target is crucial to ensure that simulated predictions of neutron beam characteristics used for subsequent beam shaping assembly design are reliable. This work presents a comprehensive benchmarking of four widely-used Monte Carlo codes - Geant4, PHITS, FLUKA (CERN), and MCNP - for modelling low-energy neutron production target reactions. Using their recommended physics models and cross-section libraries, we evaluate each code’s performance in simulating four beam-target reactions: <sup>7</sup>Li(p,n)<sup>7</sup>Be, <sup>9</sup>Be(p,n)<sup>9</sup>B, <sup>9</sup>Be(d,n)<sup>10</sup>B, and C(d,n)N. Predictions of neutron yield, angular distributions, and energy spectra are compared against available thick target experimental data. Results show varying levels of agreement between the codes depending on the reaction type, energy range, and beam characteristics. Geant4, MCNP and PHITS are the overall best performing codes for the simulation of total neutron yield and yield in the forward direction across most reactions. Across energies where experimental benchmarks exist, inter-code discrepancies in total and forward-directed yield are typically 10 to 30%, with larger deviations at near-threshold incident ion energies. PHITS provides the best overall reproduction of experimental spectra, particularly for the <sup>9</sup>Be(p,n)<sup>9</sup>B reaction. Additionally, PHITS demonstrates superior computational performance for most reactions. These findings provide valuable guidance for ABNS design, highlighting the strengths and limitations of each code for the simulation of low-energy neutron production reactions.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 109998"},"PeriodicalIF":3.4,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923459","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 : 2025-12-19DOI: 10.1016/j.cpc.2025.110003
Stephen Sanderson , Sobin Alosious , Debra J. Searles
<div><div>Recently, we proposed a method for calculating per-atom and per-direction degrees of freedom (DoF) in the presence of geometric constraints, enabling fine-grained local kinetic temperature calculations. Here, we discuss relevant implementation details for various constraint geometries, including those which feature kinematic loops (e.g. benzene with rigid bond lengths). Furthermore, by analyzing the effects of deformation of semi-rigid molecules on the DoF of each constituent atom, we gain insight into conditions under which atomic DoF may vary significantly during a simulation. This provides some guidance towards cases where local DoF should be calculated dynamically to obtain reliable local temperature measurements, and cases where using the atomic DoF of the equilibrium geometry as a constant throughout the simulation would be sufficient. We have implemented the presented algorithms in an open-source C library, <span>dofulator</span>, which can be used on its own or through a Python interface that includes compatibility with the popular MDAnalysis package.</div><div><strong>Program summary</strong> <em>Program Title:</em> <span>dofulator</span> <em>CPC Library link to program files:</em> (to be added by Technical Editor) <em>Developer’s repository link:</em> <span><span>https://github.com/CTCMS-UQ/dofulator</span><svg><path></path></svg></span> <em>Licensing provisions:</em> MPL-2.0 <em>Programming language:</em> C, Python</div><div><em>Nature of problem:</em> In molecular simulations with geometry constraints, determining the degrees of freedom (DoF) associated with a local kinetic temperature measurement can become non-trivial when constraints include atoms both inside and outside the local subset of interest [1]. The (fractional) DoF of atoms in a rigid body depends on their masses and the molecular geometry. If constraints do not form a rigid body, but instead a semi-rigid fragment, then the partitioning of atomic DoF can vary as the fragment deforms. Furthermore, if directional kinetic temperatures are required, DoF along each direction must be determined, which additionally depend on the orientation of the rigid body or semi-rigid fragment.</div><div><em>Solution method:</em> Atomic DoF can be calculated by the relative contribution of each atom to the inertia of each mode of motion [1]. This software allows rigid bodies and semi-rigid fragments to be defined, from which a plan is constructed for determining said modes and contributions. Once constructed, a plan can be applied repeatedly to calculate atomic DoF on required frames of a molecular dynamics trajectory.</div><div><em>Additional comments including restrictions and unusual features:</em> The core <span>dofulator</span> library is provided as a C API, depending only on a BLAS and LAPACK implementation and suitable for direct integration with a molecular dynamics engine (possibly with some modifications). For convenience, a thin Python wrapper is also provided, and this
最近，我们提出了一种在几何约束下计算单原子和单方向自由度（DoF）的方法，从而实现了细粒度的局部动力学温度计算。在这里，我们讨论了各种约束几何的相关实现细节，包括那些具有运动环的几何（例如具有刚性键长的苯）。此外，通过分析半刚性分子的变形对各组成原子的自由度的影响，我们深入了解了在模拟过程中原子自由度可能发生显著变化的条件。这为局部DoF应该动态计算以获得可靠的局部温度测量以及在整个模拟过程中使用平衡几何的原子DoF作为常数就足够的情况下提供了一些指导。我们已经在一个开源的C库dofulator中实现了所介绍的算法，dofulator可以单独使用，也可以通过Python接口使用，该接口包括与流行的MDAnalysis包的兼容性。程序摘要程序名称：dofulator CPC库链接到程序文件：（由技术编辑添加）开发人员存储库链接：https://github.com/CTCMS-UQ/dofulator许可条款：mpls -2.0编程语言：C， python问题性质：在具有几何约束的分子模拟中，当约束包括感兴趣的局部子集[1]内外的原子时，确定与局部动力学温度测量相关的自由度（DoF）可能变得非常重要。刚体中原子的（分数）自由度取决于它们的质量和分子几何形状。如果约束不形成刚体，而是半刚性碎片，那么原子自由度的划分可能随着碎片的变形而变化。此外，如果需要定向动力学温度，则必须确定沿每个方向的自由度，这还取决于刚体或半刚性碎片的方向。求解方法：原子自由度可以通过每个原子对每种运动模式惯性的相对贡献[1]来计算。该软件允许定义刚体和半刚体碎片，从中构建用于确定所述模式和贡献的计划。一旦建立，一个计划可以重复应用，以计算所需的框架的分子动力学轨迹的原子自由度。附加注释，包括限制和不寻常的功能：核心dofulator库作为C API提供，仅依赖于BLAS和LAPACK实现，适合与分子动力学引擎直接集成（可能需要进行一些修改）。为了方便起见，还提供了一个薄薄的Python包装器，它用于提供MDAnalysis的插件[2,3]，它提供了一种简单的方法来读取分子动力学轨迹并定义局部原子选择以进行局部DoF和温度的后处理。利益声明作者声明，他们没有已知的竞争经济利益或个人关系，可能会影响本文所报道的工作。引用文献[10]孙建军，李建军，李建军，基于分子动力学的局部温度测量方法，化学学报，20(23)(2024):1015 - 1024。doi: 10.1021 / acs.jctc。[c00957] m . michaod - agrawal， E. J. Denning， T. B. Woolf， O. Beckstein, m . analysis：一个分析分子动力学模拟的工具箱，计算化学32(10)(2011)2319-2327。doi: 10.1002 / jcc。[1787] R. Gowers， M. Linke， J. Barnoud， T. Reddy， M. Melo， S. Seyler， J. Domański， D. Dotson， S. Buchoux， I. Kenney， O. Beckstein， MDAnalysis：一个快速分析分子动力学模拟的Python包，第15届Python科学会议论文集，SciPy, 2016。doi: 10.25080 /改称- 629 - e541a - 00 - e。
{"title":"Dofulator: A tool for calculating degrees of freedom of atoms in molecules with geometry constraints","authors":"Stephen Sanderson , Sobin Alosious , Debra J. Searles","doi":"10.1016/j.cpc.2025.110003","DOIUrl":"10.1016/j.cpc.2025.110003","url":null,"abstract":"<div><div>Recently, we proposed a method for calculating per-atom and per-direction degrees of freedom (DoF) in the presence of geometric constraints, enabling fine-grained local kinetic temperature calculations. Here, we discuss relevant implementation details for various constraint geometries, including those which feature kinematic loops (e.g. benzene with rigid bond lengths). Furthermore, by analyzing the effects of deformation of semi-rigid molecules on the DoF of each constituent atom, we gain insight into conditions under which atomic DoF may vary significantly during a simulation. This provides some guidance towards cases where local DoF should be calculated dynamically to obtain reliable local temperature measurements, and cases where using the atomic DoF of the equilibrium geometry as a constant throughout the simulation would be sufficient. We have implemented the presented algorithms in an open-source C library, <span>dofulator</span>, which can be used on its own or through a Python interface that includes compatibility with the popular MDAnalysis package.</div><div><strong>Program summary</strong> <em>Program Title:</em> <span>dofulator</span> <em>CPC Library link to program files:</em> (to be added by Technical Editor) <em>Developer’s repository link:</em> <span><span>https://github.com/CTCMS-UQ/dofulator</span><svg><path></path></svg></span> <em>Licensing provisions:</em> MPL-2.0 <em>Programming language:</em> C, Python</div><div><em>Nature of problem:</em> In molecular simulations with geometry constraints, determining the degrees of freedom (DoF) associated with a local kinetic temperature measurement can become non-trivial when constraints include atoms both inside and outside the local subset of interest [1]. The (fractional) DoF of atoms in a rigid body depends on their masses and the molecular geometry. If constraints do not form a rigid body, but instead a semi-rigid fragment, then the partitioning of atomic DoF can vary as the fragment deforms. Furthermore, if directional kinetic temperatures are required, DoF along each direction must be determined, which additionally depend on the orientation of the rigid body or semi-rigid fragment.</div><div><em>Solution method:</em> Atomic DoF can be calculated by the relative contribution of each atom to the inertia of each mode of motion [1]. This software allows rigid bodies and semi-rigid fragments to be defined, from which a plan is constructed for determining said modes and contributions. Once constructed, a plan can be applied repeatedly to calculate atomic DoF on required frames of a molecular dynamics trajectory.</div><div><em>Additional comments including restrictions and unusual features:</em> The core <span>dofulator</span> library is provided as a C API, depending only on a BLAS and LAPACK implementation and suitable for direct integration with a molecular dynamics engine (possibly with some modifications). For convenience, a thin Python wrapper is also provided, and this","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"320 ","pages":"Article 110003"},"PeriodicalIF":3.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836433","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：