Final Report 8Th International Abinit
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Free and Open Source Software for Computational Chemistry Education
Free and Open Source Software for Computational Chemistry Education Susi Lehtola∗,y and Antti J. Karttunenz yMolecular Sciences Software Institute, Blacksburg, Virginia 24061, United States zDepartment of Chemistry and Materials Science, Aalto University, Espoo, Finland E-mail: [email protected].fi Abstract Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. We point out the existence of a variety of free and open source software (FOSS) packages for computational chemistry that offer a wide range of functionality all the way from approximate semiempirical calculations with tight- binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. By their very definition, FOSS packages allow usage for whatever purpose by anyone, meaning they can also be used in industrial applications without limitation. Also, FOSS software has no limitations to redistribution in source or binary form, allowing their easy distribution and installation by third parties. Many FOSS scientific software packages are available as part of popular Linux distributions, and other package managers such as pip and conda. Combined with the remarkable increase in the power of personal devices—which rival that of the fastest supercomputers in the world of the 1990s—a decentralized model for teaching computational chemistry is now possible, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device 1 (BYOD) scheme. In addition to the programs’ use for various applications, open access to the programs’ source code also enables comprehensive teaching strategies, as actual algorithms’ implementations can be used in teaching. -
Accessing the Accuracy of Density Functional Theory Through Structure
This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. Letter Cite This: J. Phys. Chem. Lett. 2019, 10, 4914−4919 pubs.acs.org/JPCL Accessing the Accuracy of Density Functional Theory through Structure and Dynamics of the Water−Air Interface † # ‡ # § ‡ § ∥ Tatsuhiko Ohto, , Mayank Dodia, , Jianhang Xu, Sho Imoto, Fujie Tang, Frederik Zysk, ∥ ⊥ ∇ ‡ § ‡ Thomas D. Kühne, Yasuteru Shigeta, , Mischa Bonn, Xifan Wu, and Yuki Nagata*, † Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan ‡ Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany § Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States ∥ Dynamics of Condensed Matter and Center for Sustainable Systems Design, Chair of Theoretical Chemistry, University of Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany ⊥ Graduate School of Pure and Applied Sciences, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8571, Japan ∇ Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan *S Supporting Information ABSTRACT: Density functional theory-based molecular dynamics simulations are increasingly being used for simulating aqueous interfaces. Nonetheless, the choice of the appropriate density functional, critically affecting the outcome of the simulation, has remained arbitrary. Here, we assess the performance of various exchange−correlation (XC) functionals, based on the metrics relevant to sum-frequency generation spectroscopy. The structure and dynamics of water at the water−air interface are governed by heterogeneous intermolecular interactions, thereby providing a critical benchmark for XC functionals. -
Density Functional Theory
Density Functional Approach Francesco Sottile Ecole Polytechnique, Palaiseau - France European Theoretical Spectroscopy Facility (ETSF) 22 October 2010 Density Functional Theory 1. Any observable of a quantum system can be obtained from the density of the system alone. < O >= O[n] Hohenberg, P. and W. Kohn, 1964, Phys. Rev. 136, B864 Density Functional Theory 1. Any observable of a quantum system can be obtained from the density of the system alone. < O >= O[n] 2. The density of an interacting-particles system can be calculated as the density of an auxiliary system of non-interacting particles. Hohenberg, P. and W. Kohn, 1964, Phys. Rev. 136, B864 Kohn, W. and L. Sham, 1965, Phys. Rev. 140, A1133 Density Functional ... Why ? Basic ideas of DFT Importance of the density Example: atom of Nitrogen (7 electron) 1. Any observable of a quantum Ψ(r1; ::; r7) 21 coordinates system can be obtained from 10 entries/coordinate ) 1021 entries the density of the system alone. 8 bytes/entry ) 8 · 1021 bytes 4:7 × 109 bytes/DVD ) 2 × 1012 DVDs 2. The density of an interacting-particles system can be calculated as the density of an auxiliary system of non-interacting particles. Density Functional ... Why ? Density Functional ... Why ? Density Functional ... Why ? Basic ideas of DFT Importance of the density Example: atom of Oxygen (8 electron) 1. Any (ground-state) observable Ψ(r1; ::; r8) 24 coordinates of a quantum system can be 24 obtained from the density of the 10 entries/coordinate ) 10 entries 8 bytes/entry ) 8 · 1024 bytes system alone. 5 · 109 bytes/DVD ) 1015 DVDs 2. -
Octopus: a First-Principles Tool for Excited Electron-Ion Dynamics
octopus: a first-principles tool for excited electron-ion dynamics. ¡ Miguel A. L. Marques a, Alberto Castro b ¡ a c, George F. Bertsch c and Angel Rubio a aDepartamento de F´ısica de Materiales, Facultad de Qu´ımicas, Universidad del Pa´ıs Vasco, Centro Mixto CSIC-UPV/EHU and Donostia International Physics Center (DIPC), 20080 San Sebastian,´ Spain bDepartamento de F´ısica Teorica,´ Universidad de Valladolid, E-47011 Valladolid, Spain cPhysics Department and Institute for Nuclear Theory, University of Washington, Seattle WA 98195 USA Abstract We present a computer package aimed at the simulation of the electron-ion dynamics of finite systems, both in one and three dimensions, under the influence of time-dependent electromagnetic fields. The electronic degrees of freedom are treated quantum mechani- cally within the time-dependent Kohn-Sham formalism, while the ions are handled classi- cally. All quantities are expanded in a regular mesh in real space, and the simulations are performed in real time. Although not optimized for that purpose, the program is also able to obtain static properties like ground-state geometries, or static polarizabilities. The method employed proved quite reliable and general, and has been successfully used to calculate linear and non-linear absorption spectra, harmonic spectra, laser induced fragmentation, etc. of a variety of systems, from small clusters to medium sized quantum dots. Key words: Electronic structure, time-dependent, density-functional theory, non-linear optics, response functions PACS: 33.20.-t, 78.67.-n, 82.53.-k PROGRAM SUMMARY Title of program: octopus Catalogue identifier: Program obtainable from: CPC Program Library, Queen’s University of Belfast, N. -
1-DFT Introduction
MBPT and TDDFT Theory and Tools for Electronic-Optical Properties Calculations in Material Science Dott.ssa Letizia Chiodo Nano-bio Spectroscopy Group & ETSF - European Theoretical Spectroscopy Facility, Dipartemento de Física de Materiales, Facultad de Químicas, Universidad del País Vasco UPV/EHU, San Sebastián-Donostia, Spain Outline of the Lectures • Many Body Problem • DFT elements; examples • DFT drawbacks • excited properties: electronic and optical spectroscopies. elements of theory • Many Body Perturbation Theory: GW • codes, examples of GW calculations • Many Body Perturbation Theory: BSE • codes, examples of BSE calculations • Time Dependent DFT • codes, examples of TDDFT calculations • state of the art, open problems Main References theory • P. Hohenberg & W. Kohn, Phys. Rev. 136 (1964) B864; W. Kohn & L. J. Sham, Phys. Rev. 140 , A1133 (1965); (Nobel Prize in Chemistry1998) • Richard M. Martin, Electronic Structure: Basic Theory and Practical Methods, Cambridge University Press, 2004 • M. C. Payne, Rev. Mod. Phys.64 , 1045 (1992) • E. Runge and E.K.U. Gross, Phys. Rev. Lett. 52 (1984) 997 • M. A. L.Marques, C. A. Ullrich, F. Nogueira, A. Rubio, K. Burke, E. K. U. Gross, Time-Dependent Density Functional Theory. (Springer-Verlag, 2006). • L. Hedin, Phys. Rev. 139 , A796 (1965) • R.W. Godby, M. Schluter, L. J. Sham. Phys. Rev. B 37 , 10159 (1988) • G. Onida, L. Reining, A. Rubio, Rev. Mod. Phys. 74 , 601 (2002) codes & tutorials • Q-Espresso, http://www.pwscf.org/ • Abinit, http://www.abinit.org/ • Yambo, http://www.yambo-code.org • Octopus, http://www.tddft.org/programs/octopus more info at http://www.etsf.eu, www.nanobio.ehu.es Outline of the Lectures • Many Body Problem • DFT elements; examples • DFT drawbacks • excited properties: electronic and optical spectroscopies. -
The Impact of Density Functional Theory on Materials Research
www.mrs.org/bulletin functional. This functional (i.e., a function whose argument is another function) de- scribes the complex kinetic and energetic interactions of an electron with other elec- Toward Computational trons. Although the form of this functional that would make the reformulation of the many-body Schrödinger equation exact is Materials Design: unknown, approximate functionals have proven highly successful in describing many material properties. Efficient algorithms devised for solving The Impact of the Kohn–Sham equations have been imple- mented in increasingly sophisticated codes, tremendously boosting the application of DFT methods. New doors are opening to in- Density Functional novative research on materials across phys- ics, chemistry, materials science, surface science, and nanotechnology, and extend- ing even to earth sciences and molecular Theory on Materials biology. The impact of this fascinating de- velopment has not been restricted to aca- demia, as DFT techniques also find application in many different areas of in- Research dustrial research. The development is so fast that many current applications could Jürgen Hafner, Christopher Wolverton, and not have been realized three years ago and were hardly dreamed of five years ago. Gerbrand Ceder, Guest Editors The articles collected in this issue of MRS Bulletin present a few of these suc- cess stories. However, even if the compu- Abstract tational tools necessary for performing The development of modern materials science has led to a growing need to complex quantum-mechanical calcula- tions relevant to real materials problems understand the phenomena determining the properties of materials and processes on are now readily available, designing a an atomistic level. -
Practice: Quantum ESPRESSO I
MODULE 2: QUANTUM MECHANICS Practice: Quantum ESPRESSO I. What is Quantum ESPRESSO? 2 DFT software PW-DFT, PP, US-PP, PAW FREE http://www.quantum-espresso.org PW-DFT, PP, PAW FREE http://www.abinit.org DFT PW, PP, Car-Parrinello FREE http://www.cpmd.org DFT PP, US-PP, PAW $3000 [moderate accuracy, fast] http://www.vasp.at DFT full-potential linearized augmented $500 plane-wave (FLAPW) [accurate, slow] http://www.wien2k.at Hartree-Fock, higher order correlated $3000 electron approaches http://www.gaussian.com 3 Quantum ESPRESSO 4 Quantum ESPRESSO Quantum ESPRESSO is an integrated suite of Open- Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Core set of codes, plugins for more advanced tasks and third party packages Open initiative coordinated by the Quantum ESPRESSO Foundation, across Italy. Contributed to by developers across the world Regular hands-on workshops in Trieste, Italy Open-source code: FREE (unlike VASP...) 5 Performance Small jobs (a few atoms) can be run on single node Includes determining convergence parameters, lattice constants Can use OpenMP parallelization on multicore machines Large jobs (~10’s to ~100’s atoms) can run in parallel using MPI to 1000’s of cores Includes molecular dynamics, large geometry relaxation, phonons Parallel performance tied to BLAS/LAPACK (linear algebra routines) and 3D FFT (fast Fourier transform) New GPU-enabled version available 6 Usability Documented online: -
The CECAM Electronic Structure Library and the Modular Software Development Paradigm
The CECAM electronic structure library and the modular software development paradigm Cite as: J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 Submitted: 06 May 2020 . Accepted: 08 June 2020 . Published Online: 13 July 2020 Micael J. T. Oliveira , Nick Papior , Yann Pouillon , Volker Blum , Emilio Artacho , Damien Caliste , Fabiano Corsetti , Stefano de Gironcoli , Alin M. Elena , Alberto García , Víctor M. García-Suárez , Luigi Genovese , William P. Huhn , Georg Huhs , Sebastian Kokott , Emine Küçükbenli , Ask H. Larsen , Alfio Lazzaro , Irina V. Lebedeva , Yingzhou Li , David López- Durán , Pablo López-Tarifa , Martin Lüders , Miguel A. L. Marques , Jan Minar , Stephan Mohr , Arash A. Mostofi , Alan O’Cais , Mike C. Payne, Thomas Ruh, Daniel G. A. Smith , José M. Soler , David A. Strubbe , Nicolas Tancogne-Dejean , Dominic Tildesley, Marc Torrent , and Victor Wen-zhe Yu COLLECTIONS Paper published as part of the special topic on Electronic Structure Software Note: This article is part of the JCP Special Topic on Electronic Structure Software. This paper was selected as Featured ARTICLES YOU MAY BE INTERESTED IN Recent developments in the PySCF program package The Journal of Chemical Physics 153, 024109 (2020); https://doi.org/10.1063/5.0006074 An open-source coding paradigm for electronic structure calculations Scilight 2020, 291101 (2020); https://doi.org/10.1063/10.0001593 Siesta: Recent developments and applications The Journal of Chemical Physics 152, 204108 (2020); https://doi.org/10.1063/5.0005077 J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 153, 024117 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp The CECAM electronic structure library and the modular software development paradigm Cite as: J. -
Accelerating Performance and Scalability with NVIDIA Gpus on HPC Applications
Accelerating Performance and Scalability with NVIDIA GPUs on HPC Applications Pak Lui The HPC Advisory Council Update • World-wide HPC non-profit organization • ~425 member companies / universities / organizations • Bridges the gap between HPC usage and its potential • Provides best practices and a support/development center • Explores future technologies and future developments • Leading edge solutions and technology demonstrations 2 HPC Advisory Council Members 3 HPC Advisory Council Centers HPC ADVISORY COUNCIL CENTERS HPCAC HQ SWISS (CSCS) CHINA AUSTIN 4 HPC Advisory Council HPC Center Dell™ PowerEdge™ Dell PowerVault MD3420 HPE Apollo 6000 HPE ProLiant SL230s HPE Cluster Platform R730 GPU Dell PowerVault MD3460 10-node cluster Gen8 3000SL 36-node cluster 4-node cluster 16-node cluster InfiniBand Storage (Lustre) Dell™ PowerEdge™ C6145 Dell™ PowerEdge™ R815 Dell™ PowerEdge™ Dell™ PowerEdge™ M610 InfiniBand-based 6-node cluster 11-node cluster R720xd/R720 32-node GPU 38-node cluster Storage (Lustre) cluster Dell™ PowerEdge™ C6100 4-node cluster 4-node GPU cluster 4-node GPU cluster 5 Exploring All Platforms / Technologies X86, Power, GPU, FPGA and ARM based Platforms x86 Power GPU FPGA ARM 6 HPC Training • HPC Training Center – CPUs – GPUs – Interconnects – Clustering – Storage – Cables – Programming – Applications • Network of Experts – Ask the experts 7 University Award Program • University award program – Universities / individuals are encouraged to submit proposals for advanced research • Selected proposal will be provided with: – Exclusive computation time on the HPC Advisory Council’s Compute Center – Invitation to present in one of the HPC Advisory Council’s worldwide workshops – Publication of the research results on the HPC Advisory Council website • 2010 award winner is Dr. -
Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published
Best Practices: Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published • Abaqus • COSMO • HPCC • Nekbone • RFD tNavigator • ABySS • CP2K • HPCG • NEMO • SNAP • AcuSolve • CPMD • HYCOM • NWChem • SPECFEM3D • Amber • Dacapo • ICON • Octopus • STAR-CCM+ • AMG • Desmond • Lattice QCD • OpenAtom • STAR-CD • AMR • DL-POLY • LAMMPS • OpenFOAM • VASP • ANSYS CFX • Eclipse • LS-DYNA • OpenMX • WRF • ANSYS Fluent • FLOW-3D • miniFE • OptiStruct • ANSYS Mechanical• GADGET-2 • MILC • PAM-CRASH / VPS • BQCD • Graph500 • MSC Nastran • PARATEC • BSMBench • GROMACS • MR Bayes • Pretty Fast Analysis • CAM-SE • Himeno • MM5 • PFLOTRAN • CCSM 4.0 • HIT3D • MPQC • Quantum ESPRESSO • CESM • HOOMD-blue • NAMD • RADIOSS For more information, visit: http://www.hpcadvisorycouncil.com/best_practices.php 2 35 Applications Installation Best Practices Published • Adaptive Mesh Refinement (AMR) • ESI PAM-CRASH / VPS 2013.1 • NEMO • Amber (for GPU/CUDA) • GADGET-2 • NWChem • Amber (for CPU) • GROMACS 5.1.2 • Octopus • ANSYS Fluent 15.0.7 • GROMACS 4.5.4 • OpenFOAM • ANSYS Fluent 17.1 • GROMACS 5.0.4 (GPU/CUDA) • OpenMX • BQCD • Himeno • PyFR • CASTEP 16.1 • HOOMD Blue • Quantum ESPRESSO 4.1.2 • CESM • LAMMPS • Quantum ESPRESSO 5.1.1 • CP2K • LAMMPS-KOKKOS • Quantum ESPRESSO 5.3.0 • CPMD • LS-DYNA • WRF 3.2.1 • DL-POLY 4 • MrBayes • WRF 3.8 • ESI PAM-CRASH 2015.1 • NAMD For more information, visit: http://www.hpcadvisorycouncil.com/subgroups_hpc_works.php 3 HPC Advisory Council HPC Center HPE Apollo 6000 HPE ProLiant -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Quantum Chemistry (QC) on Gpus Feb
Quantum Chemistry (QC) on GPUs Feb. 2, 2017 Overview of Life & Material Accelerated Apps MD: All key codes are GPU-accelerated QC: All key codes are ported or optimizing Great multi-GPU performance Focus on using GPU-accelerated math libraries, OpenACC directives Focus on dense (up to 16) GPU nodes &/or large # of GPU nodes GPU-accelerated and available today: ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS- Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012, LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, OpenMM, PolyFTS, SOP-GPU* & more Quantum Espresso/PWscf, QUICK, TeraChem* Active GPU acceleration projects: CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* = application where >90% of the workload is on GPU 2 MD vs. QC on GPUs “Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp) Simulates positions of atoms over time; Calculates electronic properties; chemical-biological or ground state, excited states, spectral properties, chemical-material behaviors making/breaking bonds, physical properties Forces calculated from simple empirical formulas Forces derived from electron wave function (bond rearrangement generally forbidden) (bond rearrangement OK, e.g., bond energies) Up to millions of atoms Up to a few thousand atoms Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically