Computer Physics Communications

We introduce UAMMD (Universally Adaptable Multiscale Molecular Dynamics), a novel software infrastructure tailored for mesoscale complex fluid simulations on GPUs. The UAMMD library encompasses a comprehensive range of computational schemes optimized for the GPU, spanning from molecular dynamics to immersed boundary fluctuating-hydrodynamics. Developed in CUDA/C++14, this header-only open-source software serves both as a simulation engine and as a library with a modular architecture, offering a vast array of independent modules, categorized as interactors (neighbor search, bonded, non-bonded and electrostatic interactions, etc.) and integrators (molecular dynamics, dissipative particle dynamics, smooth particle hydrodynamics, Brownian hydrodynamics and a rather complete array of Immersed Boundary -IB- schemes). UAMMD excels in schemes that couple particle-based elastic structures with continuum fields in different regions of the mesoscale. To that end, thermal fluctuations can be added in physically consistent ways, and fast modes can be eliminated to adapt UAMMD to different regimes (compressible or incompressible flow, inertial or Stokesian dynamics, etc.). Thus, UAMMD is extremely useful for coarse-grained simulations of nanoparticles, and soft and biological matter (from proteins to viruses and micro-swimmers). Importantly, all UAMMD developments are hand-to-hand validated against experimental techniques, and it has proven to quantitatively reproduce experimental signals from quartz-crystal microbalance, atomic force microscopy, magnetic sensors, optic-matter interaction and ultrasound.

Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel Python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains.

The library provides a set of C++/Python functions for computing cross sections of ultraperipheral collisions of high energy particles under the equivalent photons approximation. Cross sections are represented through multiple integrals over the phase space. The integrals are calculated through recurrent application of algorithms for one dimensional integration. The paper contains an introduction to the theory of ultraperipheral collisions, discusses the library approach and provides a few examples of calculations.

Recent advancements have established tensor network states (TNS) as formidable tools for exploring the complex realm of strongly-correlated many-particle systems in both one and two dimensions. To tackle the challenges presented by strongly-correlated fermion systems, various fermion tensor network states (f-TNS) methodologies have been developed. However, implementing f-TNS methods poses substantial challenges due to their particularly complex nature, making development efforts significantly difficult. This complexity is further exacerbated by the lack of underlying software packages that facilitate the development of f-TNS. Previously, we developed TNSPackage, a software package designed for TNS methods [1]. Initially, this package was only capable of handling spin and boson models. To confront the challenges presented by f-TNS, TNSPackage has undergone significant enhancements in its latest version, incorporating support for both symmetry and fermion tensors. This updated version provides a uniform interface for the consistent management of tensors across boson, fermion, and various symmetry types, maintaining its user-friendly and versatile nature. This greatly facilitates the development of programs based on f-TNS. The new TNSP framework consists of two principal components: a low-level tensor package named TAT, which supports sophisticated tensor operations, and a high-level interface package called tetragono that is built upon TAT. The tetragono package is designed to significantly simplify the development of complex physical models on square lattices. The TNSPackage framework enables users to implement a wide range of physical models with greater ease, without the need to pay close attention to the underlying implementation details.

We introduce a new open-source software package written in Python to design and model micro optical elements, such as diffractive lenses, holograms, as well as other components within the broad area of flat optics, and generate their corresponding (production-ready) lithography mask files. To this aim, the package provides functions to design a multitude of kinoform lenses, phase masks and holograms, but is versatile and the user can implement any arbitrary numerical or analytical optical component designs. For validating the designs, this package provides scalar diffraction propagation to simulate optical field propagation in different regimes covering near- and far-field regions (Fresnel, Fraunhofer and Rayleigh-Sommerfeld). Particularly, by implementing Rayleigh-Sommerfeld propagation, we demonstrate accurate field propagation within near- and far-field ranges, providing versatility and accuracy. Importantly, the package allows to directly export production-ready multilevel/binary lithography mask files of the designed optical components. Additionally, metasurface masks can conveniently be generated for any user-defined meta-element library given as input. Finally, the software package capabilities are illustrated with examples of mask design and modeling of diffractive lenses, holograms, and metasurfaces susceptible of being fabricated via lithography techniques. Beyond lithography, the package can also straightforwardly be used in other applications requiring mask generation, such as beam shaping, optical trapping and digital holography.

Local geometrical features of a porous material such as the shape and size of a pore or the curvature of a solid ligament do often affect the macroscopic properties of the material, and their characterization is necessary to fully understand the structure-property relationships. In this contribution, we present an approach to automatically segment large porous structures into such local features. Our work takes inspiration from techniques available in Topological Data Analysis (TDA). In particular, using Morse theory, we generate Morse-Smale Complexes of our structures that segment the structure, and/or its porosity into individual features that can then be compared. We develop a tool written in C++ that is built on the topology toolkit (TTK) library, an open source platform for the topological analysis of scalar data, with which we can perform segmentation of these structures. Our tool takes a volumetric grid representation as an input, which can be generated from atomistic or mesh structure models and any function defined on such grid, e.g. the distance to the surface or the interaction energy with a probe. We demonstrate the applicability of the tool by two examples related with analysis of porosity in zeolite materials as well as analysis of ligaments in a porous metal structure. Specifically, by segmenting the pores in the structure we demonstrate some applications to zeolites such as assessing pore-similarity between structures or evaluating the accessible volume to a target molecule such as methane that can be adsorbed to its surface. Moreover, once the Morse-Smale complexes are generated, we can construct graph representations of the void space, replacing the entire pore structure by a simply connected graph. Similarly, the same tool is used to segment and generates graphs representing the solid structure and we show how they can be used to correlate structure and mechanical properties of the material. The code is published as open-source and can be accessed here: https://github.com/AMDatIMDEA/tda-segmentor

In this work we report on a new version of FeynCalc, a Mathematica package widely used in the particle physics community for manipulating quantum field theoretical expressions and calculating Feynman diagrams. Highlights of the new version include greatly improved capabilities for doing multiloop calculations, including topology identification and minimization, optimized tensor reduction, rewriting of scalar products in terms of inverse denominators, detection of equivalent or scaleless loop integrals, derivation of Symanzik polynomials, Feynman parametric as well as graph representation for master integrals and initial support for handling differential equations and iterated integrals. In addition to that, the new release also features completely rewritten routines for color algebra simplifications, inclusion of symmetry relations between arguments of Passarino–Veltman functions, tools for determining matching coefficients and quantifying the agreement between numerical results, improved export to Latex and first steps towards a better support of calculations involving light-cone vectors.

Light absorption and radiative recombination are two critical processes in optoelectronic materials that characterize the energy conversion efficiency. The absorption and radiative coefficients are thus key properties for device optimization and design. Here, we develop a python package named pyArc that allows rigorous computation of absorption and radiative coefficients from first principles. By integrating several interpolation strategies to augment k-point sampling in reciprocal space, our code is accurate yet highly efficient. In addition to evaluation of the coefficients, our code is capable of intuitive analysis of carrier distribution, facilitating a deeper understanding of the microscopic mechanisms underlying the radiative coefficients. Utilizing GaAs as a prototypical example, we demonstrate how to employ our package to compute absorption and radiative coefficients and to investigate the key features in the electronic structure that give rise to these coefficients.

In this paper, we present the KelbgLIP code to implement the previously obtained analytical density matrix that includes Coulomb long-range interactions. The method is based on the work of G. Kelbg, who derived a high temperature density matrix for the Coulomb potential. To include all long-range interactions in the density matrix, we use the Ewald technique, specifically the angular-averaged Ewald potential (AAEP). The solution of the Blöch equation within the AAEP has a direct analytic form that can be easily implemented in classical and quantum Monte Carlo or molecular dynamics codes, including exchange effects. The potential part of the density matrix remains finite at small distances, preventing the collapse of a two-component system. Using KelbgLIP, one can calculate the diagonal Kelbg-AAE pseudopotential and the pair density matrix. In the case of a hydrogen plasma, the code is able to calculate action, kinetic and potential energy in the path integral representation. We validated our approach by simulating a nondegenerate weakly coupled hydrogen plasma and obtained the thermodynamic limit in agreement with the Debye-Hückel approximation. In addition, we observe the agreement with classical simulations using the unbounded from below AAEP, which is possible in the weakly-coupled regime.

We develop a software package libEMMI_MGFD for 3D frequency-domain marine controlled-source electromagnetic (CSEM) modelling and inversion. It is the first open-source C program tailored for geometrical multigrid (GMG) CSEM simulation. An volumetric anisotropic averaging scheme has been employed to compute effective medium for modelling over uniform and nonuniform grid. The computing coordinate is aligned with acquisition geometry by rotation with the azimuth and dip angles, facilitating the injection of the source and the extraction of data with arbitrary orientations. Efficient nonlinear optimization is achieved using quasi-Newton scheme assisted with bisection backtracking line search. In constructing the modularized Maxwell solver and evaluating the misfit and gradient for 3D CSEM inversion, the reverse communication technique is the key to the compaction of the software while maintaining the computational performance. A number of numeric tests demonstrate the efficiency of the modelling while preserving the solution accuracy. A 3D marine CSEM inversion example has been examined for resistivity imaging.