A fast, dense Chebyshev solver for electronic structure on GPUs

Matrix diagonalization is almost always involved in computing the density matrix needed in quantum chemistry calculations. In the case of modest matrix sizes ($\lesssim$ 5000), performance of traditional dense diagonalization algorithms on modern GPUs is underwhelming compared to the peak performance of these devices. This motivates the exploration of alternative algorithms better suited to these types of architectures. We newly derive, and present in detail, an existing Chebyshev expansion algorithm [W. Liang et al, J. Chem. Phys. 2003] whose number of required matrix multiplications scales with the square root of the number of terms in the expansion. Focusing on dense matrices of modest size, our implementation on GPUs results in large speed ups when compared to diagonalization. Additionally, we improve upon this existing method by capitalizing on the inherent task parallelism and concurrency in the algorithm. This improvement is implemented on GPUs by using CUDA and HIP streams via the MAGMA library and leads to a significant speed up over the serial-only approach for smaller ($\lesssim$ 1000) matrix sizes. Lastly, we apply our technique to a model system with a high density of states around the Fermi level which typically presents significant challenges.Comment: Submitted to Journal of Chemical Physics Communication

DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green's functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives

DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives

A Generalized Grid-Based Fast Multipole Method for Integrating Helmholtz Kernels

A grid-based fast multipole method (GB-FMM) for optimizing three-dimensional (3D) numerical molecular orbitals in the bubbles and cube double basis has been developed and implemented. The present GB-FMM method is a generalization of our recently published GB-FMM approach for numerically calculating electrostatic potentials and two-electron interaction energies. The orbital optimization is performed by integrating the Helmholtz kernel in the double basis. The steep part of the functions in the vicinity of the nuclei is represented by one-center bubbles functions, whereas the remaining cube part is expanded on an equidistant 3D grid The integration of the bubbles part is treated by using one-center expansions of the Helmholtz kernel in spherical harmonics multiplied with modified spherical Bessel functions of the first and second kind, analogously to the numerical inward and outward integration approach for calculating two-electron interaction potentials in atomic structure calculations. The expressions and algorithms for massively parallel calculations on general purpose graphics processing units (GPGPU) are described. The accuracy and the correctness of the implementation has been checked by performing Hartree-Fock self-consistent-field calculations (HF-SCF) on H-2, H2O, and CO. Our calculations show that an accuracy of 10(-4) to 10(-7) E-h can be reached in HF-SCF calculations on general molecules.Peer reviewe

Dolphin and whale: development, evaluation and application of novel bioinformatics tools for metabolite profiling in high throughput 1H-NMR analysis

El perfilat de metabòlits es la tasca més difícil dins l'anàlisi espectral de RMN. El seu objectiu es comprendre els processos biològics que tenen lloc en un moment concret mitjançant la identificació i quantificació dels metabòlits presents en mescles d' RMN complexes. Un espectre de RMN està compost per ressonàncies d'un gran nombre de metabòlits, i aquestes se solen solapar entre elles, canviar de posició depenent del pH de la mostra i poden quedar emmascarades per senyals de macromolècules. Tots aquests problemes compliquen la identificació i quantificació de metabòlits, pel que obtenir un perfil de metabòlits curat en una mostra pot ser un gran repte inclús per usuaris experts. En aquest context, la motivació d'aquesta tesi va néixer amb l'objectiu de donar automatismes i funcions fàcils de fer servir per al perfilat de metabòlits en RMN, millorant la qualitat dels resultats i reduint el temps d'anàlisi. Per fer-ho, es van implementar un conjunt d'algoritmes que van acabar empaquetats en dos programes, Dolphin i Whale.El perfilado de metabolitos es la tarea más difícil dentro del análisis espectral de RMN. Su objetivo es comprender los procesos biológicos que tienen lugar en un momento concreto a través de la identificación y cuantificación de los metabolitos presentes en mezclas de RMN complejas. Un espectro de RMN está compuesto por resonancias de un gran numero de metabolitos, y éstas a menudo se solapan entre ellas, cambian de posición dependiendo del pH de la muestra y pueden quedar enmascaradas por señales de macromoléculas. Todos estos problemas complican la identificación y cuantificación de metabolitos, por lo que obtener un perfilado de metabolitos curado en una muestra puede ser un gran reto incluso para usuarios expertos. En este contexto, la motivación de esta tesis nació con el objetivo de dar automatismos y funciones fáciles de usar para el perfilado de metabolitos en RMN, mejorando la calidad de los resultados y reduciendo el tiempo de análisis. Para hacerlo, se implementaron un conjunto de algoritmos que acabaron empaquetados en dos programas, Dolphin y Whale.Metabolite profiling is the most challenging approach in NMR spectral analysis. It aims to comprehend biological processes occurring in a certain moment through identifying and quantifying metabolites present in complex NMR mixtures. An NMR spectrum is composed by resonances of a huge number of metabolites, and these resonances often overlap between them, shift position depending on the sample pH and can be masked by macromolecules signals. All these drawbacks hinder metabolite identification and quantification, so obtaining a cured metabolite profile of a sample can be a very big issue even for expert users. In this context, the motivation of this thesis was born with the aim to provide automatisms and user-friendly interactive functions for NMR metabolite profiling, improving the quality of the results and reducing the time span of the analysis. To do so, several algorisms were implemented and embedded into two software packages, Dolphin and Whale

FIRST-PRINCIPLES INVESTIGATION OF THE INTERFACIAL PROPERTIES OF BORON NITRIDE

The interactions of nanomaterial surfaces with biological compounds, e.g. proteins, DNA, etc., unites the biological regime and nanomaterial world. Hybrid systems of boron-nitride nanotubes (BNNTs) and biological compounds are well-suited for a broad range of applications. First-principles methods are used to characterize the interface of these hybrid systems. Previous work has shown that the sensing capabilities of pristine BNNT are limited by long-ranged interactions. In this study the surfaces of pristine and functionalized BNNTs are investigated. The surfaces of the functionalized BNNTs give new properties to the tubes, which may enhance their sensing capabilities, while retaining their stability and chemical inertness. These simulations provide a fundamental understanding of these interaction. During the course of the investigation, two related projects were pursued. The first tangent characterized a new material that was found during the investigation of defects of BNNTs. The material is a B-N monolayer material (BN2) consisting of a network of extended hexagons. The distinguishable nature of the 2-D material is the presence of bonded N atoms (N-N) in the lattice. Analysis of the phonon dispersion curves suggests this phase of BN2 to be stable. The calculated elastic properties exhibit anisotropic mechanical properties that surpass graphene in the armchair direction. The second project investigated the effects of boron nitride substrates on the prop- erties of gold clusters. Experimentalists have deposited gold quantum dots onto boron-nitride nanotubes and were interested in a theoretical explanation for the dif- ferent 2D and 3D structures. For the calculations 2D and 3D, Au6, Au10, Au12, Au14 and Au16 clusters were selected. Their properties were analyzed in a free-standing configuration and on a substrate of h-BN

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC–MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice