Optimization and Parallelization of a force field for silicon using OpenMP

The force field by Lenosky and coworkers is the latest force field for silicon which is one of the most studied materials. It has turned out to be highly accurate in a large range of test cases. The optimization and parallelization of this force field using OpenMp and Fortan90 is described here. The optimized program allows us to handle a very large number of silicon atoms in large scale simulations. Since all the parallelization is hidden in a single subroutine that returns the total energies and forces, this subroutine can be called from within a serial program in an user friendly way.Comment: The program can be obtained upon request from the author ([email protected]

A Customized 3D GPU Poisson Solver for Free Boundary Conditions

A 3-dimensional GPU Poisson solver is developed for all possible combinations of free and periodic boundary conditions (BCs) along the three directions. It is benchmarked for various grid sizes and different BCs and a significant performance gain is observed for problems including one or more free BCs. The GPU Poisson solver is also benchmarked against two different CPU implementations of the same method and a significant amount of acceleration of the computation is observed with the GPU version.Comment: 10 pages, 5 figure

The solution of multi-scale partial differential equations using wavelets

Wavelets are a powerful new mathematical tool which offers the possibility to treat in a natural way quantities characterized by several length scales. In this article we will show how wavelets can be used to solve partial differential equations which exhibit widely varying length scales and which are therefore hardly accessible by other numerical methods. As a benchmark calculation we solve Poisson's equation for a 3-dimensional Uranium dimer. The length scales of the charge distribution vary by 4 orders of magnitude in this case. Using lifted interpolating wavelets the number of iterations is independent of the maximal resolution and the computational effort therefore scales strictly linearly with respect to the size of the system

Density Functional Theory calculation on many-cores hybrid CPU-GPU architectures

The implementation of a full electronic structure calculation code on a hybrid parallel architecture with Graphic Processing Units (GPU) is presented. The code which is on the basis of our implementation is a GNU-GPL code based on Daubechies wavelets. It shows very good performances, systematic convergence properties and an excellent efficiency on parallel computers. Our GPU-based acceleration fully preserves all these properties. In particular, the code is able to run on many cores which may or may not have a GPU associated. It is thus able to run on parallel and massive parallel hybrid environment, also with a non-homogeneous ratio CPU/GPU. With double precision calculations, we may achieve considerable speedup, between a factor of 20 for some operations and a factor of 6 for the whole DFT code.Comment: 14 pages, 8 figure

Global minimum determination of the Born-Oppenheimer surface within density functional theory

We present a novel method, which we call dual minima hopping method (DMHM), that allows us to find the global minimum of the potential energy surface (PES) within density functional theory for systems where a fast but less accurate calculation of the PES is possible. This method can rapidly find the ground state configuration of clusters and other complex systems with present day computer power by performing a systematic search. We apply the new method to silicon clusters. Even though these systems have already been extensively studied by other methods, we find new configurations that are lower in energy than the previously found.Comment: 4 pages, 3 figures, minor changes, more structures are presented no

Combining multigrid and wavelet ideas to construct more efficient multiscale algorithms for the solution of Poisson's equation

International audienceIt is shown how various ideas that are well established for the solution of Poisson's equation using plane wave and multigrid methods, can be combined with wavelet concepts. The combination of wavelet concepts and multigrid techniques turns out to be particularly fruitful. We propose a modified multigrid V cycle scheme that is not only much simpler, but also more efficient than the standard V cycle. Whereas in the traditional V cycle the residue is passed to the coarser grid levels, this new scheme does not require the calculation of a residue. Instead it works with copies of the charge density on the different grid levels that were obtained from the underlying charge density on the finest grid by wavelet transformations. This scheme is not limited to the pure wavelet setting, where it is faster than the preconditioned conjugate gradient method, but equally well applicable for finite difference discretizations

Crystal structure prediction using the Minima Hopping method

A structure prediction method is presented based on the Minima Hopping method. Optimized moves on the configurational enthalpy surface are performed to escape local minima using variable cell shape molecular dynamics by aligning the initial atomic and cell velocities to low curvature directions of the current minimum. The method is applied to both silicon crystals and binary Lennard-Jones mixtures and the results are compared to previous investigations. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.Comment: 9 pages, 6 figures, novel approach in structure prediction, submitted to the Journal of Chemical Physic

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the total energy. This prevents for instance an accurate description of the energetics of systems where long range charge transfer is important as well as of ionized systems. We propose therefore not to target directly with machine learning methods the total energy but an intermediate physical quantity namely the charge density, which then in turn allows to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e. errors of less than a milli Hartree per atom compared to the reference density functional results. The introduction of physically motivated quantities which are determined by the short range atomic environment via a neural network leads also to an increased stability of the machine learning process and transferability of the potential.Comment: 4 figure