We present a program for solving exactly the general pairing Hamiltonian based on diagonalization. The program generates the seniority-zero shell-model-like basis vectors via the ‘01’ inversion algorithm. The Hamiltonian matrix is constructed in this seniority-zero space. The program evaluates all non-zero elements of the Hamiltonian matrix “on the fly” using the scattering operator and a search algorithm. The matrix is diagonalized by using the iterative Lanczos algorithm. The OpenMP parallel program thus developed, PairDiag, can efficiently calculate the ground-state eigenvalue and eigenvector of the general pairing Hamiltonian for both the even-mass and the odd-mass system. The program is packaged in a Fortran module, which makes it easy to use the program to replace the BCS approximation in standard self-consistent mean field calculations. For systems with dimension around 10^8, the calculation can be done within hours on standard desktop computers.
FeynMaster is a multi-tasking software for particle physics studies. By making use of already existing programs (FeynRules, QGRAF, FeynCalc), FeynMaster automatically generates Feynman rules, generates and draws Feynman diagrams, generates amplitudes, performs both loop and algebraic calculations, and fully renormalizes models. In parallel with this automatic character, FeynMaster allows the user to manipulate the generated results in Mathematica notebooks in a flexible and consistent way. It can be downloaded in https://porthos.tecnico.ulisboa.pt/FeynMaster/.
Inverse Ising inference is a method for inferring the coupling parameters of a Potts/Ising model based on observed site-covariation, which has found important applications in protein physics for detecting interactions between residues in protein families. We introduce Mi3-GPU (“mee-three”, for MCMC Inverse Ising Inference) software for solving the inverse Ising problem for protein-sequence datasets with few analytic approximations, by parallel Markov-Chain Monte Carlo sampling on GPUs. We also provide tools for analysis and preparation of protein-family Multiple Sequence Alignments (MSAs) to account for finite-sampling issues, which are a major source of error or bias in inverse Ising inference. Our method is “generative” in the sense that the inferred model can be used to generate synthetic MSAs whose mutational statistics (marginals) can be verified to match the dataset MSA statistics up to the limits imposed by the effects of finite sampling. Our GPU implementation enables the construction of models which reproduce the covariation patterns of the observed MSA with a precision that is not possible with more approximate methods. The main components of our method are a GPU-optimized algorithm to greatly accelerate MCMC sampling, combined with a multi-step Quasi-Newton parameter-update scheme using a “Zwanzig reweighting” technique. We demonstrate the ability of this software to produce generative models on typical protein family datasets for sequence lengths L ~ 300 with 21 residue types with tens of millions of inferred parameters in short running times.
Contributors:Andy Buckley, Philip Ilten, Dmitri Konstantinov, Leif Lönnblad, James Monk, Witold Pokorski, Tomasz Przedzinski, Andrii Verbytskyi
In high-energy physics, Monte Carlo event generators (MCEGs) are used to simulate the interactions of high energy particles. MCEG event records store the information on the simulated particles and their relationships, and thus reflect the simulated evolution of physics phenomena in each collision event.
We present the HepMC3 library, a next-generation framework for MCEG event record encoding and manipulation, which builds on the functionality of its widely-used predecessors to enable more sophisticated algorithms for event-record analysis. As compared to previous versions, the event record structure has been simplified, while adding the possibility to encode arbitrary information. The I/O functionality has been extended to support common input and output formats of various HEP MCEGs, including formats used in Fortran MCEGs, the formats established by the HepMC2 library, and binary formats such as ROOT; custom input or output handlers may also be used. HepMC3 is already supported by popular modern MCEGs and can replace the older HepMC versions in many others.
Contributors:Yunfei Huang, Gerhard Gompper, Benedikt Sabass
Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established method for quantifying this cell–substrate interaction is traction force microscopy (TFM). In spite of recent advancements, inference of the traction forces from measurements remains very sensitive to noise. However, suppression of the noise reduces the measurement accuracy and the spatial resolution, which makes it crucial to select an optimal level of noise reduction. Here, we present a fully automated method for noise reduction and robust, standardized traction-force reconstruction. The method, termed Bayesian Fourier transform traction cytometry, combines the robustness of Bayesian L2 regularization with the computation speed of Fourier transform traction cytometry. We validate the performance of the method with synthetic and real data. The method is made freely available as a software package with a graphical user-interface for intuitive usage.
We present a package to simulate long-term diffusive mass transport in systems with atomic scale resolution. The implemented framework is based on a non-equilibrium statistical thermo-chemo-mechanical formulation of atomic systems where effective transport rates are computed using a kinematic diffusion law. Our implementation is built as an add-on to the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code, it is compatible with other LAMMPS’ functionalities, and shows a good parallel scalability and efficiency. In applications involving diffusive mass transport, this framework is able to simulate problems of technological interest for exceedingly large time scales using an atomistic description, which are not reachable with the state-of-the-art molecular dynamics techniques. Several examples, involving complex diffusive behavior in materials, are investigated with the framework. We found good qualitative and quantitative comparison with known theories and models, with Monte Carlo methods, as well as with experimental results. Thus, our implementation can be used as a tool to understand diffusive behavior in materials where experimental characterization is difficult to perform.
Contributors:A. Braz, L.G.S. Duarte, L.A.C.P. da Mota
In Duarte et al. (2016) and Avellar et al. (2019), we have developed a method (we call it S-function method) that is successful in treating certain classes of rational second order ordinary differential equations (rational 2ODEs) that are particularly ‘resistant’ to canonical Lie methods and to Darbouxian approaches (extensions of the Prelle–Singer method). In this present paper, we generalize the S-function method making it capable of dealing with a class of elementary 2ODEs. We also present a Maple implementation of the method in a computational package – S++ – that is designed to provide a set of tools to allow the user to analyze the intermediary steps of the generalized S-function method. Finally, we apply this method to a Duffing–Van der Pol forced oscillator, obtaining an entirely new class of first integrals.
A program to calculate the three-particle hyperspherical brackets is presented. Test results are listed and it is seen that the program is well applicable up to very high values of the hypermomentum and orbital momenta. The listed runs show that it is also very fast. Applications of the brackets to calculating interaction matrix elements and constructing hyperspherical bases for identical particles are described. Comparisons are done with the programs published previously.
Amphiphile-based aggregates are extensively used in numerous applications for encapsulation, storage, transport and delivery of toxic, active molecules due to the structural properties of the aggregates. The properties of the aggregates in turn are dictated by the molecular architecture of the amphiphiles. A complete understanding of the multiscale architecture–structure–function relationship for amphiphile-based aggregates requires the simultaneous resolution of the self-assembly of amphiphilic molecules along with an understanding of the role of various long range physical interactions including hydrodynamics. A multiscale computational approach such as the hybrid Molecular Dynamics–Lattice Boltzmann technique is able to fulfill most of those requirements. However, existing implementations only account for static coupling between the Molecular Dynamics technique and the Lattice Boltzmann method, and hence are unable to resolve the changes in the solvent-amphiphile interface during processes such as self-assembly and interfacial adsorption. In this study, a new implementation incorporating a dynamic coupling scheme between the Molecular Dynamics technique and the Lattice Boltzmann method is introduced so as to resolve dynamical changes in interfaces. The application of the new implementation to the self-assembly of phospholipids yields results which are in good agreement with computation, experiments and theory. In particular, we found the scaling exponent α of the cluster number (N(t) = C t^α) to be ~1.
The previous version of this program (AEPH_v1_0) may be found at http://dx.doi.org/10.1016/j.cpc.2013.03.024.
In this paper a simple, robust, and general purpose approach to implement the Incompressible Smoothed Particle Hydrodynamics (ISPH) method is proposed. This approach is well suited for implementation on CPUs and GPUs. The method is matrix-free and uses an iterative formulation to setup and solve the pressure-Poisson equation. A novel approach is used to ensure homogeneous particle distributions and improved boundary conditions. This formulation enables the use of solid wall boundary conditions from the weakly-compressible SPH schemes. The method is fast and runs on GPUs without the need for complex integration with sparse linear solvers. We show that this approach is sufficiently accurate and yet efficient compared to other approaches. Several benchmark problems that illustrate the robustness, performance, and wide range of applicability of the new scheme are demonstrated. An open source implementation is provided and the manuscript is fully reproducible.