Computer Physics Communications

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.

We present QLBM, a Python software package designed to facilitate the development, simulation, and analysis of Quantum Lattice Boltzmann Methods (QBMs). QLBM 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.

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 Polar Shift, 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. Polar Shift 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 Polar Shift 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. Polar Shift is applied to eight different test cases of prototype organic electronics materials demonstrating its capabilities, and the results are compared with existing literature.

We present high-performance implementations of the two-dimensional Ising and Blume-Capel models for large-scale, multi-GPU simulations. Our approach takes full advantage of the NVIDIA GB200 NVL72 system, which features up to 72 GPUs interconnected via high-bandwidth NVLink, enabling direct GPU-to-GPU memory access across multiple nodes. By utilizing Fabric Memory and an optimized Monte Carlo kernel for the Ising model, our implementation supports simulations of systems with linear sizes up to L = 2^23, corresponding to approximately 70 trillion spins. This allows for a peak processing rate of nearly 1.15 x 10^5 lattice updates per nanosecond—setting a new performance benchmark for Ising model simulations. Additionally, we introduce a custom protocol for computing correlation functions, which strikes an optimal balance between computational efficiency and statistical accuracy. This protocol enables large-scale simulations without incurring prohibitive runtime costs. Benchmark results show near-perfect strong and weak scaling up to 64 GPUs, demonstrating the effectiveness of our approach for large-scale statistical physics simulations.

In this work, we introduce PHOENIX, a highly optimized explicit open-source solver for two-dimensional nonlinear Schrödinger equations with extensions. The nonlinear Schrödinger equation and its extensions (Gross-Pitaevskii equation) are widely studied to model and analyze complex phenomena in fields such as optics, condensed matter physics, fluid dynamics, and plasma physics. It serves as a powerful tool for understanding nonlinear wave dynamics, soliton formation, and the interplay between nonlinearity, dispersion, and diffraction. By extending the nonlinear Schrödinger equation, various physical effects such as non-Hermiticity, spin-orbit interaction, and quantum optical aspects can be incorporated. PHOENIX is designed to accommodate a wide range of applications by a straightforward extendability without the need for user knowledge of computing architectures or performance optimization. The high performance and power efficiency of PHOENIX are demonstrated on a wide range of entry-class to high-end consumer and high-performance computing GPUs and CPUs. Compared to a more conventional MATLAB implementation, a speedup of up to three orders of magnitude and energy savings of up to 99.8% are achieved. The performance is compared to a performance model showing that PHOENIX performs close to the relevant performance bounds in many situations. The possibilities of PHOENIX are demonstrated with a range of practical examples from the realm of nonlinear (quantum) photonics in planar microresonators with active media including exciton-polariton condensates. Examples range from solutions on very large grids, the use of local optimization algorithms, to Monte Carlo ensemble evolutions with quantum noise enabling the tomography of the system's quantum state.

In this study, the Gaunt coefficients, Clebsch–Gordan coefficients, and the Wigner 3j and 6j symbols are expressed as the product of generalized hypergeometric functions with unit argument and a normalization coefficient. By exploiting the symmetry properties of generalized hypergeometric functions, these functions are transformed into numerically computable forms, and the normalization coefficients are fully expressed in terms of binomial coefficients. New mathematical expressions, in the form of a series of products of three Gaunt coefficients, are presented, which can be used to verify the accuracy of numerical calculations. An algorithm has been developed to compute binomial coefficients and generalized hypergeometric functions using recurrence relations, eliminating the need for factorial functions. Utilizing this algorithm and the derived analytical expressions, the Gaunt_CG_3j_and_6j Mathematica program, which numerically calculates the Gaunt coefficients, Clebsch–Gordan coefficients, and the Wigner 3j and 6j symbols, was written without relying on Mathematica’s built-in functions. The program can be easily adapted to other programming languages and run on all versions of Mathematica.