Computer Physics Communications

We have developed a software MagGene to predict magnetic structures by using genetic algorithm. Starting from an atom structure, MagGene repeatedly generates new magnetic structures and calls first-principles calculation engine to get the most stable magnetic structure. This software is applicable to both collinear and noncollinear systems. It is particularly convenient for predicting the magnetic structures of atomic systems with strong spin–orbit couplings and/or strong spin frustrations.

Experimental thermodynamic measurements in multicomponent systems exhibit high complexity. Theoretical calculations by extrapolation of constitutive binary systems are an excellent tool to estimate the thermodynamic properties in ternary or quaternary systems. In this context, the Miedema and Bakker semi-empirical models are good to estimate the enthalpy of mixing or formation. This work presents a new software, MAAT (Materials Analysis Applying Thermodynamics), designed to calculate selected thermodynamic properties of binary and ternary systems. The MAAT is a free software that can be download from www.rpm.usm.cl. The MAAT software is a platform, written in MATLAB, which runs in 32/64 bits Windows systems. The main characteristics of the software are: i) calculation and plotting Gibbs free energy of mixing curves of random solid solutions, amorphous and intermetallic compounds, ii) calculation and plotting the activity of components in solid solutions, and iii) analysis of the effect of additional terms over the Gibbs free energy of mixing of random solid solutions, such as centrifugal field, grain size and dislocations. In this work, the thermodynamic calculations performed with MAAT are compared with experimental data in four cases: formation of solid solution (Cu-Mo-Cr system), formation of amorphous phase (Ti-Ta-Sn system), formation of intermetallic compound (Cu-Nb-Co system) and effect of centrifugal field on formation of solid solution (Cu-Cr system). For all systems analyzed, the calculations made using MAAT gave results that are comparable with experimental data.

We present OpenMP versions of FORTRAN programs for solving the Gross–Pitaevskii equation for a harmonically trapped three-component spin-1 spinor Bose–Einstein condensate (BEC) in one (1D) and two (2D) spatial dimensions with or without spin–orbit (SO) and Rabi couplings. Several different forms of SO coupling are included in the programs. We use the split-step Crank–Nicolson discretization for imaginary- and real-time propagation to calculate stationary states and BEC dynamics, respectively. The imaginary-time propagation programs calculate the lowest-energy stationary state. The real-time propagation programs can be used to study the dynamics. The simulation input parameters are provided at the beginning of each program. The programs propagate the condensate wave function and calculate several relevant physical quantities. Outputs of the programs include the wave function, energy, root-mean-square sizes, different density profiles (linear density for the 1D program, linear and surface densities for the 2D program). The imaginary- or real-time propagation can start with an analytic wave function or a pre-calculated numerical wave function. The imaginary-time propagation usually starts with an analytic wave function, while the real-time propagation is often initiated with the previously calculated converged imaginary-time wave function.

Positive geometries provide a modern approach for computing scattering amplitudes in a variety of physical models. In order to facilitate the exploration of these new geometric methods, we introduce a Mathematica package called “amplituhedronBoundaries” for calculating the boundary structures of three positive geometries: the amplituhedron, the momentum amplituhedron and the hypersimplex. The first two geometries are relevant for scattering amplitudes in planar N = 4 supersymmetric Yang–Mills theory, while the last one is a well-studied polytope appearing in many contexts in mathematics, and is closely related to the m = 2 momentum amplituhedron. The package includes an array of useful tools for the study of these positive geometries, including their boundary stratifications, drawing their boundary posets, and additional tools for manipulating combinatorial structures useful for positive Grassmannians.

The natural orbital functional theory (NOFT) has emerged as an alternative formalism to both density functional (DF) and wavefunction methods. In NOFT, the electronic structure is described in terms of the natural orbitals (NOs) and their occupation numbers (ONs). The approximate NOFs have proven to be more accurate than those of the density for systems with a significant multiconfigurational character, on one side, and scale better with the number of basis functions than correlated wavefunction methods, on the other side. A challenging task in NOFT is to efficiently perform orbital optimization. In this article we present DoNOF, our open source implementation based on diagonalizations that allows to obtain the resulting orbitals automatically orthogonal. The one-particle reduced-density matrix (1RDM) of the ensemble of pure-spin states provides the proper description of spin multiplets. The capabilities of the code are tested on the water molecule, namely, geometry optimization, natural and canonical representations of molecular orbitals, ionization potential, and electric moments. In DoNOF, the electron-pair-based NOFs developed in our group PNOF5, PNOF7 and PNOF7s are implemented. These JKL-only NOFs take into account most non-dynamic effects plus intrapair-dynamic electron correlation, but lack a significant part of interpair-dynamic correlation. Correlation corrections are estimated by the single-reference NOF-MP2 method that simultaneously calculates static and dynamic electron correlations taking as a reference the Slater determinant formed with the NOs of a previous PNOF calculation. The NOF-MP2 method is used to analyze the potential energy surface (PES) and the binding energy for the symmetric dissociation of the water molecule, and compare it with accurate wavefunction-based methods.

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that for a water system 12, 582, 912 of atoms, the GPU version can be 7 times faster than the CPU version under the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions.

We introduce the package SWANLOP to calculate scattering waves and corresponding observables for nucleon elastic collisions off spin-zero nuclei. The code is capable of handling local and nonlocal optical potentials superposed to long-range Coulomb interaction. Solutions to the implied Schrödinger integro-differential equation are obtained by solving an integral equation of Lippmann–Schwinger type for the scattering wavefunctions, ψ = ϕ_C + G_C U_S ψ, providing and exact treatment to the Coulomb force Arellano and Blanchon (2019). The package has been developed to handle potentials either in momentum or coordinate representations, providing flexible options under each of them. The code is fully self-contained, being dimensioned to handle any A >= 4 target for nucleon beam energies of up to 1.1 GeV. Accuracy and benchmark applications are presented and discussed.

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method.

Monte Carlo generation of high energy particle collisions is a critical tool for both theoretical and experimental particle physics, connecting perturbative calculations to phenomenological models, and theory predictions to full detector simulation. The generation of minimum bias events can be particularly computationally expensive, where non-perturbative effects play an important role and specific processes and fiducial regions can no longer be well defined. In particular scenarios, particle guns can be used to quickly sample kinematics for single particles produced in minimum bias events. CIMBA (Cubic Interpolation for Minimum Bias Approximation) provides a comprehensive package to smoothly sample predefined kinematic grids, from any general purpose Monte Carlo generator, for all particles produced in minimum bias events. These grids are provided for a number of beam configurations including those of the Large Hadron Collider.

Fluid flow simulation is a highly active area with applications in a wide range of engineering problems and interactive systems. Meshless methods like the Moving Particle Semi-implicit (MPS) are a great alternative to deal efficiently with large deformations and free-surface flow. However, mesh-based approaches can achieve higher numerical precision than particle-based techniques with a performance cost. This paper presents a numerically stable and parallelized system that benefits from advances in the literature and parallel computing to obtain an adaptable MPS method. The proposed technique can simulate liquids using different approaches, such as two ways to calculate the particles’ pressure, turbulent flow, and multiphase interaction. The method is evaluated under traditional tests cases presenting comparable results to recent techniques. This work integrates the previously mentioned advances into a single solution, which can switch on improvements, such as better momentum conservation and less spurious pressure oscillations, through a graphical interface. The code is entirely open-source under the GPLv3 free software license. The GPU-accelerated code reached speedups ranging from 3 to 43 times, depending on the total number of particles. The simulation runs at one fps for a case with approximately 200,000 particles.