Computer Physics Communications

We introduce BinPo, an open-source Python code to compute electronic properties of two-dimensional electron systems. A bulk tight binding Hamiltonian is constructed from relativistic density functional theory calculations represented in the basis of maximally localized Wannier functions. BinPo has a Schrödinger-Poisson solver, integrating an electric field-dependent relative permittivity to obtain self-consistently the confining electrostatic potential energy term in the derived tight binding slab system. The band structure, energy slices, and other properties, along with different projections and orientations can be computed. High resolution and publishable figures of the simulations can be generated. In BinPo, priority has been given to ease-of-use, efficiency, readability and modularity, therefore becoming suitable to produce reliable electronic structures simulations at low computational cost. Along with the code itself, we provide files from first-principles calculations for some materials, instructions of use, and detailed examples of its wide range of capabilities. The code was developed with a focus on the ABO3 perovskite structure-based systems, such as SrTiO3 and KTaO3, because of their increasing impact in the materials community. Some features, such as the projection onto orbital states, are restricted to calculations using the relevant t2g orbitals for this family of materials, yet it is possible to include more elements in the basis for the band structure determination of other systems. The use of a relativistic approach allows for the inspection of the role of spin-orbit coupling and the resulting Rashba effect on the systems. We detail the approaches used in the code, so that it can be further exploited and adapted to other problems, such as adding new materials and functionalities which can strength the initial code scopes.

Self-diffusion coefficient can be derived from molecular dynamics (MD) simulations by fitting the mean squared displacement (MSD) into the Einstein relation. However, the finite system size, nonfulfillment of the Brownian motion, and finite simulation time may bring in significant uncertainties that need to be estimated. We present a python module to facilitate the accurate determination of self-diffusion coefficient from the Einstein relation. We show that the ballistic stage can be clearly recognized and excluded to improve the accuracy and efficiency of self-diffusion coefficient calculation. The correct self-diffusion coefficient and its uncertainty can be conveniently obtained by taking the ensemble average of diffusion coefficients calculated at different time intervals. At the meantime, the module calculates viscosity that can correct the MD-derived self-diffusion coefficient to the thermodynamic limit.

BiFold calculates the density-dependent (DDM3Yn, BDM3Yn, CDM3Yn) or independent double-folded potentials between two colliding spherical nuclei. It is written in a Python package form to give the ability to use the potentials directly in a nuclear reaction/structure code. In addition to using Woods-Saxon/Fermi or Gaussian functions, the code also allows the definition of nuclear matter densities using pre-calculated densities in a data file. The manuscript provides an overview of the double folding model and the use of the code.

FITEVT is a FORTRAN program designed to analyze recorded arrival times of nuclear-decay events affected by dead-time. The primary goal of the program is precise and accurate determination of half-lives. Its method involves imposition of a known sufficiently long extending dead-time to the recorded event sequence, so that the original dead-time effects are completely obliterated and the remaining live-times of the survived events are known exactly. Upon completion of the analysis, the arrival-time spectrum and the live-time spectrum are predicted and compared to those constructed from the survived events.

We present the Fluid Transport Accelerated Solver, FluTAS, a scalable GPU code for multiphase flows with thermal effects. The code solves the incompressible Navier-Stokes equation for two-fluid systems, with a direct FFT-based Poisson solver for the pressure equation. The interface between the two fluids is represented with the Volume of Fluid (VoF) method, which is mass conserving and well suited for complex flows thanks to its capacity of handling topological changes. The energy equation is explicitly solved and coupled with the momentum equation through the Boussinesq approximation. The code is conceived in a modular fashion so that different numerical methods can be used independently, the existing routines can be modified, and new ones can be included in a straightforward and sustainable manner. FluTAS is written in modern Fortran and parallelized using hybrid MPI/OpenMP in the CPU-only version and accelerated with OpenACC directives in the GPU implementation. We present different benchmarks to validate the code, and two large-scale simulations of fundamental interest in turbulent multiphase flows: isothermal emulsions in HIT and two-layer Rayleigh-Bénard convection. FluTAS is distributed through a MIT license and arises from a collaborative effort of several scientists, aiming to become a flexible tool to study complex multiphase flows.

We present a coarse-grained Cα-based protein model that can be used to simulate structured, intrinsically disordered and partially disordered proteins. We use a Go-like potential for the structured parts and two different variants of a transferable potential for the disordered parts. The first variant uses dynamic structure-based (DSB) contacts that form and disappear quasi-adiabatically during the simulation. By using specific structural criteria we distinguish sidechain-sidechain, sidechain-backbone and backbone-backbone contacts. The second variant is a non-radial multi-body pseudo-improper-dihedral (PID) potential that does not include time-dependent terms but requires more computational resources. Our model can simulate in reasonable time thousands of residues on millisecond time scales.

We present an open-source software for simulation of observables in magnetic resonance experiments, including nuclear magnetic/quadrupole resonance NMR/NQR and electron spin resonance (ESR). Inspired by magnetic resonance protocols that emerged in the context of quantum information science (QIS), this software can assist experimental research in the design of new strategies for the investigation of fundamental quantum properties of materials. The package introduced here can simulate both standard NMR spectroscopic observables and the time-evolution of an interacting single-spin system subject to complex pulse sequences, i.e. quantum gates. The main purpose of this software is to facilitate the development of much needed novel NMR-based probes of emergent quantum order, which can be elusive to standard experimental probes. The software is based on a quantum mechanical description of nuclear spin dynamics in NMR/NQR experiments and has been widely tested on available theoretical and experimental results. Moreover, the structure of the software allows for basic experiments to be easily generalized to more sophisticated ones because it includes all the libraries required for the numerical simulation of generic spin systems. In order to make the program easily accessible to a large user base, we developed a user-friendly graphical interface, Jupyter notebooks, and fully-detailed documentation. Lastly, we portray several examples of the execution of the code that illustrate the prosepcts of a novel NMR paradigm, inspired by QIS, for efficient investigation of emergent phases in strongly correlated materials.

The Simple Python Ipywidgets Notebook interface to obtain the optoelectronic properties of materials (SPIN) is an open source graphical user interface that allows users to work with standard SIESTA files and perform end-to-end atomic level simulation processes. It contains the complete flow, from the construction and visualization of structures or systems until the pre-processing, execution, and post-processing of calculations such as structure optimization, electronic properties like band structure, density of states (DOS), and optical properties. SPIN is an easy-to-use and fast-learning solution written in Python and built from Ipywidgets. However, the end-user can use all available features without the need for Python language knowledge.

We present finite-element numerical algorithms for the identification of vortices in quantum fluids described by a macroscopic complex wave function. Their implementation using the free software FreeFem++ is distributed with this paper as a post-processing toolbox that can be used to analyse numerical or experimental data. Applications for Bose-Einstein condensates (BEC) and superfluid helium flows are presented. Programs are tested and validated using either numerical data obtained by solving the Gross-Pitaevskii equation or experimental images of rotating BEC. Vortex positions are computed as topological defects (zeros) of the wave function when numerical data are used. For experimental images, we compute vortex positions as local minima of the atomic density, extracted after a simple image processing. Once vortex centers are identified, we use a fit with a Gaussian to precisely estimate vortex radius. For vortex lattices, the lattice parameter (inter-vortex distance) is also computed. The post-processing toolbox offers a complete description of vortex configurations in superfluids. Tests for two-dimensional (giant vortex in rotating BEC, Abrikosov vortex lattice in experimental BEC) and three-dimensional (vortex rings, Kelvin waves and quantum turbulence fields in superfluid helium) configurations show the robustness of the software. The communication with programs providing the numerical or experimental wave function field is simple and intuitive. The post-processing toolbox can be also applied for the identification of vortices in superconductors.

Recent work [1], [2], using an effective field theory framework, have shown the number of possible couplings between nucleons and the dark-matter-candidate Weakly Interacting Massive Particles (WIMPs) is larger than previously thought. Inspired by an existing Mathematica script that computes the target response [2], we have developed a fast, modern Fortran code, including optional OpenMP parallelization, along with a user-friendly Python wrapper, to swiftly and efficiently explore many scenarios, with output aligned with practices of current dark matter searches. A library of most of the important target nuclides is included; users may also import their own nuclear structure data, in the form of reduced one-body density matrices. The main output is the differential event rate as a function of recoil energy, needed for modeling detector response rates, but intermediate results such as nuclear form factors can be readily accessed.