Computer Physics Communications

Density functional theory (DFT) remains the most widely used electronic structure method. Although exact in principle, in practice, it relies on approximations to the exchange-correlation (XC) functional, which is known to be a unique functional of the electron density. Despite 50 years of active research, existing XC approximations remain far from general purpose chemical accuracy of various thermochemical and materials properties. In that light, the inverse DFT problem, of finding the exact XC potential corresponding to an accurate groundstate density, offers an insightful tool to understand the nature of the XC functional as well as aid in the development of more accurate functionals. However, solving the inverse DFT problem is fraught with several numerical challenges, such as non-uniqueness or spurious oscillations in the solution and non-convergence. We present invDFT as an open-source framework to address the outstanding challenges in inverse DFT and computed XC potentials solely from a target density. We do so by use of a systematically convergent differential finite-element basis—higher-order finite-elements for the Kohn-Sham orbitals and linear finite-elements for the XC potential—which together render the inverse DFT problem well-posed. We also employ necessary asymptotic corrections to the target density to avoid any unphysical oscillations in the resulting XC potential. We also employ several numerical and high-performance computing (HPC) advances that affords both efficiency and parallel scalability, on CPU-GPU hybrid architectures. We demonstrate the accuracy and scalability of invDFT using accurate full-configuration interaction (FCI) densities as well as model densities, ranging up to 100 electrons and spanning both weakly and strongly correlated molecules.

The occurrence of thermal transport phenomena is widespread in nature and plays a pivotal role in the functionality of diverse electronic and thermo-electric energy-conversion devices. The traditional first-principles theory governing the thermal and thermodynamic properties of insulators relies on the perturbative treatment of interatomic potential and ad hoc displacement of atoms within supercells. However, the limitations of these approaches for highly anharmonic and weakly bonded materials, along with discrepancies arising from not considering explicit finite temperature effects, highlight the necessity for a well-defined quasiparticle approach to the lattice vibrations. To address these limitations, we present a comprehensive numerical framework in this study, designed to compute the thermal and thermodynamic properties of crystalline semiconductors and insulators. The self-consistent phonon renormalization method we have devised reveals phonons as quasiparticles, diverging from their conventional characterization as bare normal modes of lattice vibration. The extension of renormalization effects to third- and fourth-order interatomic force constants (IFCs) is also implemented and demonstrated. For the comprehensive physical insights, we employed an iterative solution of the Peierls-Boltzmann transport equation (PBTE) to determine thermal conductivity and carry out Helmholtz free energy calculations, treating anharmonic effects up to the fourth order. We utilize our numerical framework to showcase its applicability through an investigation of phonon dispersion, phonon linewidth, anharmonic phonon scattering rates, and temperature-dependent lattice thermal conductivity in both highly anharmonic materials (NaCl and AgI) and weakly anharmonic materials (cBN and 3C-SiC). Our study reveals that neglecting higher-order phonon scattering processes, particularly four-phonon interactions, is not viable for materials with strong anharmonicity in their interatomic potential. Meanwhile, renormalization demonstrates a negligible impact on materials characterized by weak anharmonicity. We also investigate the effects of fourth-order anharmonicity on the Helmholtz free energy and pressure-dependent thermal conductivity of the NaCl crystal. The theoretical and computational framework developed in this work will help to understand the physical insight of phonons and phonon-driven thermal and thermodynamic properties of materials and offer valuable guidance for the strategic development of efficient thermal management techniques.

RHEA-X is an open-source solver for the direct numerical simulation of complex multiphysics turbulent flows on modern accelerated architectures. It reconciles physical accuracy with computational efficiency, enabling simulations ranging from ideal gases and particle-laden turbulence to high-pressure transcritical fluids and external flow aerodynamics. At its core lies a modular C++ architecture built on MPI and OpenACC that combines low-dissipation spatial discretizations, strong-stability-preserving time integration, advanced thermodynamic formulations up to real-fluid equations of state and complex correlation-based transport-property models. In addition, RHEA-X incorporates immersed boundary methods and Lagrangian point particles to solve external and particle-laden flows, respectively. Through persistent GPU data residency, GPU-aware MPI communication, and portable design, RHEA-X achieves reproducible and scalable performance across diverse systems and flow configurations. Validation against canonical and multiphysics benchmarks demonstrates its ability to capture near-wall turbulence, vortex-dominated separation, and pseudo-boiling thermodynamics with accuracy consistent with reference data and published results. Performance analyses on multiple supercomputing platforms demonstrate that 97% of ideal strong-scaling behavior is maintained up to 64 GPUs, while weak-scaling efficiencies remain above 90%, and time-to-solution reductions exceeding one order of magnitude compared to CPU execution (up to approximately 70 × ). Both CPU and GPU runs exhibit consistent relative scalability, supporting deployment from academic clusters towards exascale systems. RHEA-X yields an open, extensible environment for high-fidelity computational fluid dynamics, where physical rigor, computational performance, and scientific reproducibility meet within a single, coherent framework.

Although various methods exist for modelling non-spherical particles in DEM, particles’ shapes are usually treated as immutable. However, particles often change shape gradually, e.g., due to abrasion or accrued plastic deformation. This manner of shape evolution has largely been neglected in DEM, even though it can significantly influence bulk-scale behaviour. The following introduces an extendable framework for modelling the gradual and permanent evolution of particle shapes in DEM, focusing on abrasion. By extending the LAMMPS rigid-body implementation, a comprehensive novel wear model is adapted to simulate the abrasion of arbitrarily shaped particles. Abradable particles are represented as hollow shells of discrete spheres which compute forces via standard pair interactions. These spheres form the nodes of a triangulated mesh used to calculate well-defined local surface areas and normals. Following an impact exceeding a material yield criterion, spheres are displaced inwards along their associated normals. The result is a reduction in volume and a permanent change in shape. Each abraded particle’s moment of inertia is then recomputed to resolve future rigid-body dynamics. Thus, particle-level changes in shape affect the system’s bulk dynamics, which in turn informs subsequent abrasion. Results exhibit shape evolution in agreement with a variety of abrasion scenarios in literature and showcase the consequent effect on their bulk dynamics. By linking microscale abrasion mechanisms to macroscale system behaviour, the present research has widespread applications in both natural processes and industrial particle-handling systems. Furthermore, the outlined framework can be readily adapted to other sources of mechanistic particle shape evolution in DEM.

Twistronics is an emerging field in condensed matter physics and materials science. However, accurate and efficient calculations of the electronic structures of twisted systems remains a significant challenge. To address this issue, we have developed MoireStudio, a universal Python-based computational package for twisted electronic structures. Its functionalities include commensurate-structure search, structure generation, parameterization, and construction of tight-binding models and continuum models, and the precise incorporation of full relaxation effects. The package is applicable to arbitrary combinations of two-dimensional materials, including rectangular lattices and heterostructures. MoireStudio is user-friendly, supports parallel large-scale computations, provides visualization capabilities, and offers interfaces with third-party software. It is designed to serve as a convenient and powerful tool for researchers in twistronics fields.

We present an open-source software package IRSSG for investigating magnetic systems with spin space groups (SSGs). The package works within the density functional theory (DFT) framework and requires wavefunctions from DFT codes, such as VASP, Quantum ESPRESSO, as well as any other code that has an interface to Wannier90. We introduce a set of compact SSG international symbols by combining non-crystallographic point groups with the 230 crystallographic space groups. The program first identifies all SSG operations and determines the SSG international symbol for a given magnetic system. It then generates the SSG character tables of little groups at any k point. Finally, it computes the traces of matrix representations of SSG operations and assigns irreducible corepresentation labels to magnetic energy bands. The program is not only timely but also essential for advancing research on the study of magnons, altermagnetism, magnetic topology, and novel high-degeneracy excitations in SSG systems.

Recent advances in numerical modeling have improved the fidelity of coastal wave-propagation simulations, but the high computational solution cost of the governing partial differential equations has motivated parallel solvers on multicore CPUs and GPUs. Simultaneously, researchers are exploring machine-learning methods to achieve comparable efficiency gains, and automatic differentiation (AD) is being incorporated to enable optimization and inverse analyses. This paper introduces CelerisAI, a Python-based implementation of the Celeris nearshore wave model that delivers high-performance execution on multiple CPUs or a GPU and interoperates with machine-learning workflows. CelerisAI exposes the forward simulation to AD, yielding a differentiable solver suited to complex tasks such as inverse problems and data assimilation. We evaluate CelerisAI on standard benchmarks and demonstrate AD-enabled data-assimilation applications.

This paper introduces an improved version of our previous CUDA programs [1] for solving the dipolar Gross-Pitaevskii equation in three spatial dimensions, now incorporating a quantum fluctuation term [2, 3, 4]. This enhancement is vital for accurately modeling quantum dipolar droplets [5], [6] in dipolar Bose-Einstein condensates. Originally developed in C, the code has been transitioned to C++ to take advantage of its features. Speedup tests were conducted on two types of GPU cards – one commercial and one HPC-optimized – and the results were compared to earlier versions on GPUs and a modern HPC cluster. Both configurations showed an increase in speed.

Foam injection is a promising alternative for enhanced oil recovery strategies, as it can improve sweep efficiency in gas-assisted processes. Accurately predicting multiphase foam flow in porous media, however, remains challenging, and existing modeling approaches continue to evolve as new foam modeling insights and numerical strategies emerge. In this work, we develop ImpesFOAM, a computational framework for three-phase flow incorporating effects of foam using {Finite volume method} (FVM) formulation within the OpenFOAM framework and {IMplicit-Pressure Explicit-Saturation} (IMPES) method. Two foam formulations are incorporated: an implicit-texture model that captures dry-out, oil effects, and surfactant concentration, and a mechanistic population-balance model describing bubble generation and coalescence. Surfactant transport in the aqueous phase is modeled through an additional conservation equation that includes adsorption effects. The object-oriented high-level programming design of OpenFOAM facilitates the link between mathematical modeling and computational implementation, which makes the developed solver more flexible for further foam model implementations. The solver is validated against a comprehensive set of benchmark cases for two and three-phase flow, with and without foam, designed to isolate gravity, capillary pressure, well-driven flow with source and sink terms, and surfactant transport mechanisms. The results show good agreement with analytical, numerical, and experimental results. Moreover, through two three-dimensional field application cases, we demonstrate that foam-assisted {Enhanced Oil Recovery} (EOR) ultimately yields more economically attractive outcomes, including a substantial increase in oil recovery factor and a decrease in the gas-oil ratio production.

We present moljax, an open-source JAX library for GPU-accelerated method-of-lines simulation of stiff reaction-diffusion PDEs on structured grids. The library combines three capabilities absent from existing Python PDE tools: (i) JIT-compiled adaptive time stepping with accept/reject control flow on GPU; (ii) matrix-free Newton–Krylov solvers using AD for exact Jacobian–vector products; and (iii) FFT/DST/DCT spectral operators with physics-aware preconditioning. Controlled benchmarks on Gray–Scott, Schnakenberg, and Brusselator systems show IMEX and ETDRK4 integrators achieving 10–17 ×  speedup over explicit RK4 on the same GPU and spatial discretization. Work-precision analysis reveals that ETDRK4 is the only method achieving monotone pointwise convergence for pattern-forming systems. A tubular reactor benchmark demonstrates 18–40 ×  speedup over Diffrax using identical spatial discretization. Performance claims are supported by controlled comparisons that hold hardware and spatial discretization fixed where attribution is intended. Code: https://github.com/gogipav14/moljax.