Computer Physics Communications

Accurate modeling of cardiac electrical activity is essential for developing diagnostic and therapeutic technologies. This study presents a parameter evaluation of a modified FitzHugh–Nagumo (FHN) model to reproduce the specific waveforms generated by different cardiac tissues, such as the sinoatrial node, atria, atrioventricular node, Purkinje fibers, and ventricles. Through a systematic sensitivity analysis, the influence of key parameters on waveform features such as amplitude, duration, and frequency is identified, allowing precise calibration for each tissue type. These parameter sets were then integrated into a multi-compartment model and implemented in a two-dimensional (2D) spatial domain using COMSOL Multiphysics, following the framework of Sovilj et al. The simulations successfully replicated electrocardiographic components—including the P wave, QRS complex, and T wave—by combining spatially distributed signals with physiologically representative dynamics. Rather than proposing a new model, this work validates a methodology for tuning and applying simplified excitable models to simulate realistic cardiac behavior efficiently. The approach offers potential applications in the design of low-power wearable devices and supports the development of personalized monitoring systems. Future work will extend this methodology to other excitable tissues and explore its use in modeling pathological conditions or structural constraints, providing a flexible platform for evaluating requirements in next-generation bioelectronic devices.

The FFT-based smooth particle mesh Ewald (PME) method is extended to non-periodic charge systems interacting via a radially symmetric kernel f(r). The proposed non-periodic PME (NPME) method begins by splitting the kernel f(r) into a short-range component f_s(r) and a smooth long-range component f_l(r). A Fourier extension for f_l(r) is computed numerically using discrete Fourier transform interpolation, enabling efficient treatment of anisotropic rectangular charge volume and offering additional flexibility in the choice of kernel splitting. A derivative-matched (DM) splitting is introduced for general radially symmetric kernels f(r), improving computational performance over traditional Ewald splitting methods. An optimized grid storage algorithm for NPME is proposed, reducing total grid memory by a factor of four. The NPME algorithm is implemented in a C++ library, npme, which supports both pre-defined kernels (e.g. 1/r, r^α, exp(ik_0 r)/r) and user-defined kernels via C++ classes. npme is benchmarked and compared to fmm3D on test systems in computational chemistry and computational electromagnetics. As a practical application, NPME is combined with Method of Moments (MoM) to form a hybrid MoM-NPME algorithm for calculating the radar cross section (RCS) of a perfect electric conductor (PEC). The MoM–NPME method is used to compute the bistatic RCS of a 1-meter PEC sphere at 37.8 GHz and the monostatic RCS of the NASA almond at 75 GHz.

Insulator-metal transitions in quantum materials have important potential applications in areas such as field-effect transistors and neuromorphic computing. Here we present an initial release of the Q-POP-IMT module, an open-source phase-field software for simulating mesoscopic, nonequilibrium processes of insulator-metal transitions in quantum materials. Q-POP-IMT solves the phase-field equations of evolution that describe insulator-metal transitions at the mesoscale using the finite element method. It currently utilizes the powerful FEniCS library to define and solve finite element problems. Thanks to the finite element method, the code can address general boundary conditions such as a complex integral boundary condition corresponding to one of the most common setups in experiments and applications. We demonstrate the usage of the code through simulating the neuron-like voltage self-oscillation phenomenon in a prototypical correlated material, vanadium dioxide.

Solid-state electro-aerodynamic propulsion systems are devices that utilize atmospheric pressure corona discharge and have been actively researched in recent years as a means of achieving silent drones. However, these systems contain multiple, widely disparate time and spatial scales. Therefore, the governing equations of the systems, a three-component plasma fluid model that considers the presence of electrons, positive ions, and negative ions, constitute a stiff non-linear system of partial differential equations, challenging to solve. Here, we have developed an ANEMONE simulator capable of numerically estimating the corona inception voltage and energy conversion efficiency in three-dimensional solid-state electro-aerodynamic propulsion systems. Specifically, on the basis of the governing equations, we adopted the method of characteristics and the perturbation method to obtain the sub-problems. Furthermore, we have successfully obtained the integral equations, making the sub-problems easier to solve. Finally, we validated the prediction results based on the theoretical results in a previous study. Remarkably, ANEMONE is the first simulator in the world which predicted the two representative performance (i.e., the corona inception voltage and energy conversion efficiency) of fully three-dimensional propulsion systems.

Modern atomic physics applications in science and technology pose ever higher demands on the precision of computations of properties of atoms and ions. Especially challenging is the modeling of electronic correlations within the configuration interaction (CI) framework, which often requires expansions of the atomic state in huge bases of Slater determinants or configuration state functions. This can easily render the problem intractable even for highly efficient atomic codes running on distributed supercomputer systems. Recently, we have successfully addressed this problem using a neural-network (NN) approach [1]. In this work, we present our Python code for performing NN-supported large-scale atomic CI using pCI [2] and other high-performance atomic codes.

We present quTARANG, a robust GPU-accelerated Python package developed for a comprehensive study of turbulence problems in Bose-Einstein condensates (BECs). It solves the mean-field Gross-Pitaevskii equation (GPE) using a Time-splitting pseudo-spectral (TSSP) scheme and ground state calculations are performed using a Backward Euler spectral (BESP) scheme. quTARANG also has post-processing tools that can compute different statistical properties of turbulent Bose-Einstein condensates, such as kinetic energy spectra, particle number spectrum and corresponding fluxes. This paper provides detailed descriptions of the code, along with specific examples for calculating the ground state and turbulent state of the condensate under different initial conditions for both 2-D and 3-D cases. We also present results on the dynamics of the GPE in 2-D and 3-D used to validate our code. Finally, we compare the performance of quTARANG on different GPUs to its performance on a CPU, demonstrating the speedup achieved on various GPU architectures.

We present an efficient algorithm for computing entanglement entropies in systems with a restricted Hilbert space or U(1) symmetry. For the case of a restricted Hilbert space, the algorithm is straightforward in that only a map table from physical states to indices of an intermediate matrix is needed. In systems with a U(1) symmetry, the reduced density matrix can be put into a block-diagonal form by properly grouping matrix elements according to the total charge in the subsystem, leading to a significant boost in the efficiency of entanglement entropy calculation.

The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.

This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory, including static Coulomb interactions derived from GW calculations, offering a fully ab initio approach to determine superconducting properties, such as the critical superconducting temperature (T_C) and the superconducting gap function (Δ). We validate IsoME by benchmarking it against various materials, demonstrating its versatility and performance across different theoretical levels. The findings indicate that the previously held assumption that Eliashberg theory overestimates T_C is no longer valid when μ^⁎ is appropriately adjusted to account for the finite Matsubara frequency cutoff. Furthermore, we conclude that the constant density of states (DOS) approximation remains accurate in most cases. By unifying multiple approximation schemes within a single framework, IsoME combines first-principles precision with computational efficiency, enabling seamless integration into high-throughput workflows through its T_C search mode. This makes IsoME a powerful and reliable tool for advancing superconductivity research.

The current work presents a novel COllaborative Open-source Lattice Boltzmann Method framework, so-called CooLBM. 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: https://gitlab.coria-cfd.fr/lbm/coolbm.