DOCSLIB.ORG

A PILOT INITIATIVE for EGI-XSEDE COLLABORATION Laganà A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A PILOT INITIATIVE for EGI-XSEDE COLLABORATION Laganà A A PILOT INITIATIVE FOR EGI-XSEDE COLLABORATION Laganà A. Dept of Chemistry, Biology & Biotechnologies, PG, IT A PROPOSAL FROM THE CMMST (Chemistry, Molecular, Materials Science and Technologies) COMMUNITY: implement a Synergistic High- Throughput&Performance Universal Molecular Simulator as a service for scientists and technologists 1 EGI-InSPIRE RI-261323 www.egi.eu The goals of the EGI-CMMST community THE GOALS wImplement a SYNERGISTIC (collaborative/competitive) HW, SW, MW work-model for molecular sciences & technologies computing wOffer a SERVICE for new discoveries by means of extremely accurate (quantum), efficient and versatile multiscale real-like SIMULATOR operating on distributed platforms FOR THIS PURPOSE IN EGI WE ARE STRUCTURING theEuropean CMMST (Chemistry, Molecular, Materials Sciences & Techno-logies) VRC (Virtual Research Community) based on: - EGI National Grid infrastructures, resources and operations - EGI and COMMUNITY user support, tools and services 2 EGI-InSPIRE RI-261323 www.egi.eu 1st target: A WORLDWIDE DISTRIBUTED PLATFORM • CONNECT EGI distributed operations platform to XSEDE (PRACE, etc.) network and consortia of resource providers by placing allocation requests in an appropriate form 3 EGI-InSPIRE RI-261323 www.egi.eu 2nd TARGET: A WORLDWIDE RESEARCH FEDERATION • FEDERATE CMMST communities around the world (EU, US, CANADA, PACIFIC ASIA, …) for reciprocal collaboration, support and servicing 4 EGI-InSPIRE RI-261323 www.egi.eu 3rd TARGET: QUALITY EVALUATION • IMPLEMENT proper quality evaluation criteria in order to - select HTC, HPC, specialized HW - chain in workflows programs, packages, data - specialize portals and gateways - reward active users providing shareable software - build a shared governance for the community - standardize (at least de facto) molecular data 5 EGI-InSPIRE RI-261323 www.egi.eu THE STARTING COMMUNITY AND PARTNERS • VIRTUAL ORGANIZATIONS: COMPCHEM, CHEM.VO.IBERGRID, GAUSSIAN • PROJECTS: MoSGRID and SCALALIFE • ASSOCIATIONS: Virtual Education Community of ECTN (150 Chemistry Departments), EUCHEMS Computational chemistry division (1000 users) • TECHNICAL PARTNERS: IGI-UNIPG, MTA-SZTAKI, UB, MoSGRID • NGIs: IGI, IBERGRID, PL-GRID, MetaCentrum, NGI-HU, NGI-LT, GRNET • RESOURCE PROVIDERS: CINECA, CESGA, XSEDE • SME CLUSTER: MASTER-UP srl (Perugia, IT), Krebbs srl (Vienna, A), Exact lab (Trieste, IT), Arctur (Nova Gorica, SL), Polymechanon (Thessaloniki, GR) 6 EGI-InSPIRE RI-261323 www.egi.eu DATA AND WORKFLOW OF THE SIMULATOR System input Q5Cost Interaction D5Cost Dynamics various Statistics Virtual Monitors EGI-InSPIRE RI-261323 www.egi.eu THE SIMULATOR WORKFLOW NO NO NO Are ab initio Are ab initio INTERACTION Is there a suitable PES? calculations calculations available? feasible? YES YES YES SUPSIM Are NO Take Force fields from dynamics FITTING databases Import the calculations PES routine direct? YES YES DYNAMICS NO NO Exact NO Approximate Semiclassical quantum quantum calculations? calculations? calculations? YES YES YES QDYN APPROXIMATE SEMICLASSICAL QUASI- TD / TI QDYN SC_IVR CLASSICAL OBSERVABLES NO NO NO Fixed Fixed J and Energy Fixed Temperature Energy YES YES YES Scalar and vector Thermal Cross-sections Thermodynamic properties EGI-InSPIRE RI-261323 correlations Rate coefficients www.egi.eu THE UNSUSPECTED KILLER WHY TRAJECTORIES FAIL TO PREDICT HIGH REACTIVITY OF M + M’H → MM’ + H AT LOW TEMPERATURE? (Astrochemistry, cold fusion, drugs action, etc.) EGI-InSPIRE RI-261323 www.egi.eu Long range The Grid distribution model interactions and THE SIMULATOR reactivity of HC N+N2 r BC SRampinoetal r HA AB HB Fukuoka GEMS’ Dyn module ICCSA 2010 Distribute AI runs and get the PES Outline q5 ! q5 q5 q5 q5 q5 q5 using the TI ABC program Motivation d5 Interaction module ! each fixed-E calculation is Anewpotential energy surface independent Theory and ! computing details distribute and gather results to The A + BC reaction J-shifting get observables Grid distribution Distribute scattering E’s and get reactive P’s ML4LJ dynamics Dynamics module and kinetics E On a grid of 160 ’s Acknowledgments ! 10 E’s per CE’s References Observables module ! speed up of about 16 EGI-InSPIRE RI-261323 www.egi.eu 10 Antonio Lagan`aand Sergio Rampino Quantum dynamics vs experiment Li + FH (v=0, j) -> LiF + H 12 Li + FH (v = 0, j) → LiF + H 10 A. Laganà et al, Lecture j=0, J-shifting j=0, exact Notes in Computer Science, exp June 2014 8 σ (A2) 6 Both exact and J-shifting results con\irm the experimental trend 4 suggesting an ininite cross section at vanishing 2 scattering energies 0 0 0.02 0.04 0.06 0.08 0.1 Etr (eV) EGI-InSPIRE RI-261323 www.egi.eu Fig. 2. v =0,j =0(solidline)J-shifting excitation function plotted as a function of translation energy. Experimental data of Ref. [15] scaled to quantum values calculated around 0.08 eV are given as black circles. For comparison the value of the exact quan- tum (v =0,j = 0) cross section obtained including calculations at J<5 at a very low translational energy (empty squares at 0.00387 and 0.01000 eV) is also shown. A grid empowered virtual versus real experiment for the reaction Li + FH 9 THE SMOKING GUN Li + FH (v=0, j) -> LiF + H 1 j=0 0.8 0.6 j = 0 Li + FH (v = 0, j) → LiF + H 0.4 0.2 0 1 j=1 0.8 0.6 j = 1 0.4 0.2 At j=0 here is no threshold to reaction: 0 1 j=2 the reaction occurs also at no collision 0.8 0.6 energy and behaves, therefore, as 0.4 Probability j = 2 barrier-less 0.2 0 1 j=1.4 0.8 0.6 0.4 j = 1.4 0.2 exp 0 0 0.02 0.04 0.06 0.08 0.1 Etr (eV) EGI-InSPIRE RI-261323 Fig. 1. J =0,v =0,j =0(toppanel),j = 1 (higher central panel),www.egi.euj =2(lower central panel) state-specific reactive probabilities plotted as a function of translation energy. Values for ¯j =1.4 (bottom panel) corresponding to the model treatment of Ref. [15] are also shown. THE PROPOSED ROADMAP XSEDE – EGI-CMMST SET a joint Virtual Team with the mandate of defining a workplan for: • Establishing links among continental CMMST subcommunities (EU, US, ASIA, …..) • Establishing reciprocal access to the compute resources • Developping QoS redirection of jobs to the most proper machines • Activate quality schemes for rewarding active users 13 EGI- InSPIRE RI-261323 www.egi.eu A XRAS procedure for CMMST • developing a XRAS procedure for community accesses from EGI-CMMST - CMMST has already identified 2 US PI and can find more if needed - EGI-CMMST can guarantee definition of re- sources, authentication, allocation policies - EGI-CMMST can provide local accounting, custom submission UI, integration with OPEN XDMODE and certification of local users’ publications 14 EGI-InSPIRE RI-261323 www.egi.eu THE END if you wish to know more about the quality parameters there are more slides to follow 7/17/14 15 EGI-InSPIRE RI-261323 www.egi.eu AB INITIO PACKAGES • CADPAC Acad Fortran GTO • COLUMBUS Acad Fortran GTO • CRYSTAL Acad Fortran GTO • DALTON Acad Fortran GTO • DFT++ GPL C++ PW/Wavelet • DIRAC Acad F77, 90, C GTO • GAMESS Acad/Comm Fortran GTO • GAUSSIAN Comm Fortran GTO • MOLCAS Comm Fortran GTO • MOLPRO Comm Fortran GTO • NWChem ECLv2 F77/C GTO, PW • TERACHEM Comm C/CUDA GTO EGI-InSPIRE RI-261323 www.egi.eu QoS PARAMETERS • Accessibility • Integrity • Performance • Reliability • Security 7/17/14 17 EGI-InSPIRE RI-261323 www.egi.eu QoU PARAMETERS • Number of succesful compilations • Number of results retrieved • Grid efficiency • Number of feedbacks produced • …. 7/17/14 18 EGI-InSPIRE RI-261323 www.egi.eu For each job or subjob • 1. Username owning the job 2. Grid service name (wrapper) 3. Grid site name used 4. Job Type (e.g. Single, Parameter Study or Workflow) 5. Submission date 6. Ending date 7. Program name (if custom) 8. Input provided (if any) 7/17/14 19 EGI-InSPIRE RI-261323 www.egi.eu For each job or subjob • 9. Results address (e.g. Storage Element) 10. Computing Element address (e.g. queue name used for run) 11. Computing Element Exit status (e.g. 0 -> done with success; 1 -> CE error) 12. Cpu time 13. Wall time 14. Memory consumed 15. Additional job info 7/17/14 20 EGI-InSPIRE RI-261323 www.egi.eu For each Grid service • 16. Name 17. Description 18. Grid site name location 19. Maintainer / Manager 20. Number of Functions (e.g. # of Web Services composing the Grid Service) 21. Number of Errors 22. Elapsed (averaged) time for satisfying (initiating) a call 7/17/14 21 EGI-InSPIRE RI-261323 www.egi.eu For each Grid service • 23. Elapsed (averaged) TTR (Time-to- Repair) for repairing an error 24. QoS (calculated by us) 25. Cost (assigned by us) 26. Additional service info 7/17/14 22 EGI-InSPIRE RI-261323 www.egi.eu For each user • 27. Number of runs corresponding to those applications already made available by the Framework; 28. Number of runs corresponding to new programs provided by the user (if any); • 29. Number of results generated from 27) 30. Number of results generated from 28) 31. Number of results accessed from 27) 32. Number of results accessed from 28) 7/17/14 23 EGI-InSPIRE RI-261323 www.egi.eu For each user • 33. Average cpu time elapsed for runs generating results 34. Average cpu time elapsed for runs having results accessed • 35. Average wall time elapsed for runs generating results 36. Average wall time elapsed for runs having results accessed • 37. Average memory consumed for runs generating results 7/17/14 24 EGI-InSPIRE RI-261323 www.egi.eu For each user • 38. Average memory consumed for runs having results accessed • 39. Number of positive feedbacks 40.
Load more

Recommended publications
  • 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.
    [Show full text]
  • Camcasp 5.9 Alston J. Misquitta† and Anthony J
    CamCASP 5.9 Alston J. Misquittay and Anthony J. Stoneyy yDepartment of Physics and Astronomy, Queen Mary, University of London, 327 Mile End Road, London E1 4NS yy University Chemical Laboratory, Lensfield Road, Cambridge CB2 1EW September 1, 2015 Abstract CamCASP is a suite of programs designed to calculate molecular properties (multipoles and frequency- dependent polarizabilities) in single-site and distributed form, and interaction energies between pairs of molecules, and thence to construct atom–atom potentials. The CamCASP distribution also includes the programs Pfit, Casimir,Gdma 2.2, Cluster, and Process. Copyright c 2007–2014 Alston J. Misquitta and Anthony J. Stone Contents 1 Introduction 1 1.1 Authors . .1 1.2 Citations . .1 2 What’s new? 2 3 Outline of the capabilities of CamCASP and other programs 5 3.1 CamCASP limits . .7 4 Installation 7 4.1 Building CamCASP from source . .9 5 Using CamCASP 10 5.1 Workflows . 10 5.2 High-level scripts . 10 5.3 The runcamcasp.py script . 11 5.4 Low-level scripts . 13 6 Data conventions 13 7 CLUSTER: Detailed specification 14 7.1 Prologue . 15 7.2 Molecule definitions . 15 7.3 Geometry manipulations and other transformations . 16 7.4 Job specification . 18 7.5 Energy . 23 7.6 Crystal . 25 7.7 ORIENT ............................................... 26 7.8 Finally, . 29 8 Examples 29 8.1 A SAPT(DFT) calculation . 29 8.2 An example properties calculation . 30 8.3 Dispersion coefficients . 34 8.4 Using CLUSTER to obtain the dimer geometry . 36 9 CamCASP program specification 39 9.1 Global data .
    [Show full text]
  • Jaguar 5.5 User Manual Copyright © 2003 Schrödinger, L.L.C
    Jaguar 5.5 User Manual Copyright © 2003 Schrödinger, L.L.C. All rights reserved. Schrödinger, FirstDiscovery, Glide, Impact, Jaguar, Liaison, LigPrep, Maestro, Prime, QSite, and QikProp are trademarks of Schrödinger, L.L.C. MacroModel is a registered trademark of Schrödinger, L.L.C. To the maximum extent permitted by applicable law, this publication is provided “as is” without warranty of any kind. This publication may contain trademarks of other companies. October 2003 Contents Chapter 1: Introduction.......................................................................................1 1.1 Conventions Used in This Manual.......................................................................2 1.2 Citing Jaguar in Publications ...............................................................................3 Chapter 2: The Maestro Graphical User Interface...........................................5 2.1 Starting Maestro...................................................................................................5 2.2 The Maestro Main Window .................................................................................7 2.3 Maestro Projects ..................................................................................................7 2.4 Building a Structure.............................................................................................9 2.5 Atom Selection ..................................................................................................10 2.6 Toolbar Controls ................................................................................................11
    [Show full text]
  • Thomas–Fermi–Dirac–Von Weizsäcker Models in Finite Systems Garnet Kin-Lic Chan, Aron J
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Caltech Authors - Main Thomas–Fermi–Dirac–von Weizsäcker models in finite systems Garnet Kin-Lic Chan, Aron J. Cohen, and Nicholas C. Handy Citation: 114, (2001); doi: 10.1063/1.1321308 View online: http://dx.doi.org/10.1063/1.1321308 View Table of Contents: http://aip.scitation.org/toc/jcp/114/2 Published by the American Institute of Physics JOURNAL OF CHEMICAL PHYSICS VOLUME 114, NUMBER 2 8 JANUARY 2001 Thomas–Fermi–Dirac–von Weizsa¨cker models in finite systems Garnet Kin-Lic Chan,a) Aron J. Cohen, and Nicholas C. Handy Department of Chemistry, Lensfield Road, Cambridge CB2 1EW, United Kingdom ͑Received 7 June 2000; accepted 8 September 2000͒ To gain an understanding of the variational behavior of kinetic energy functionals, we perform a numerical study of the Thomas–Fermi–Dirac–von Weizsa¨cker theory in finite systems. A general purpose Gaussian-based code is constructed to perform energy and geometry optimizations on polyatomic systems to high accuracy. We carry out benchmark studies on atomic and diatomic systems. Our results indicate that the Thomas–Fermi–Dirac–von Weizsa¨cker theory can give an approximate description of matter, with atomic energies, binding energies, and bond lengths of the correct order of magnitude, though not to the accuracy required of a qualitative chemical theory. We discuss the implications for the development of new kinetic functionals. © 2001 American Institute of Physics. ͓DOI: 10.1063/1.1321308͔ I. INTRODUCTION
    [Show full text]
  • Quantum Chemical Calculations of NMR Parameters
    Quantum Chemical Calculations of NMR Parameters Tatyana Polenova University of Delaware Newark, DE Winter School on Biomolecular NMR January 20-25, 2008 Stowe, Vermont OUTLINE INTRODUCTION Relating NMR parameters to geometric and electronic structure Classical calculations of EFG tensors Molecular properties from quantum chemical calculations Quantum chemistry methods DENSITY FUNCTIONAL THEORY FOR CALCULATIONS OF NMR PARAMETERS Introduction to DFT Software Practical examples Tutorial RELATING NMR OBSERVABLES TO MOLECULAR STRUCTURE NMR Spectrum NMR Parameters Local geometry Chemical structure (reactivity) I. Calculation of experimental NMR parameters Find unique solution to CQ, Q, , , , , II. Theoretical prediction of fine structure constants from molecular geometry Classical electrostatic model (EFG)- only in simple ionic compounds Quantum mechanical calculations (Density Functional Theory) (EFG, CSA) ELECTRIC FIELD GRADIENT (EFG) TENSOR: POINT CHARGE MODEL EFG TENSOR IS DETERMINED BY THE COMBINED ELECTRONIC AND NUCLEAR WAVEFUNCTION, NO ANALYTICAL EXPRESSION IN THE GENERAL CASE THE SIMPLEST APPROXIMATION: CLASSICAL POINT CHARGE MODEL n Zie 4 V2,k = 3 Y2,k ()i,i i=1 di 5 ATOMS CONTRIBUTING TO THE EFG TENSOR ARE TREATED AS POINT CHARGES, THE RESULTING EFG TENSOR IS THE SUM WITH RESPECT TO ALL ATOMS VERY CRUDE MODEL, WORKS QUANTITATIVELY ONLY IN SIMPLEST IONIC SYSTEMS, BUT YIELDS QUALITATIVE TRENDS AND GENERAL UNDERSTANDING OF THE SYMMETRY AND MAGNITUDE OF THE EXPECTED TENSOR ELECTRIC FIELD GRADIENT (EFG) TENSOR: POINT CHARGE MODEL n Zie 4 V2,k = 3 Y2,k ()i,i i=1 di 5 Ze V = ; V = 0; V = 0 2,0 d 3 2,±1 2,±2 2Ze V = ; V = 0; V = 0 2,0 d 3 2,±1 2,±2 3 Ze V = ; V = 0; V = 0 2,0 2 d 3 2,±1 2,±2 V2,0 = 0; V2,±1 = 0; V2,±2 = 0 MOLECULAR PROPERTIES FROM QUANTUM CHEMICAL CALCULATIONS H = E See for example M.
    [Show full text]
  • Use of Mo”Ller-Plesset Perturbation Theory in Molecular Calculations: Spectroscopic Constants of First Row Diatomic Molecules
    JOURNAL OF CHEMICAL PHYSICS VOLUME 108, NUMBER 12 22 MARCH 1998 Use of Mo”ller-Plesset perturbation theory in molecular calculations: Spectroscopic constants of first row diatomic molecules Thom H. Dunning, Jr. Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352 Kirk A. Peterson Department of Chemistry, Washington State University and the Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352 ~Received 9 December 1997; accepted 17 December 1997! The convergence of Mo”ller–Plesset perturbation expansions ~MP2–MP4/MP5! for the spectroscopic constants of a selected set of diatomic molecules ~BH, CH, HF, N2, CO, and F2! has been investigated. It was found that the second-order perturbation contributions to the spectroscopic constants are strongly dependent on basis set, more so for HF and CO than for BH. The MP5 contributions for HF were essentially zero for the cc-pVDZ basis set, but increased significantly with basis set illustrating the difficulty of using small basis sets as benchmarks for correlated calculations. The convergence behavior of the exact Mo”ller–Plesset perturbation expansions were investigated using estimates of the complete basis set limits obtained using large correlation consistent basis sets. For BH and CH, the perturbation expansions of the spectroscopic constants converge monotonically toward the experimental values, while for HF, N2, CO, and F2, the expansions oscillate about the experimental values. The perturbation expansions are, in general, only slowly converging and, for HF, N2, CO, and F2, appear to be far from convergence at MP4. In fact, for HF, N2, and CO, the errors in the calculated spectroscopic constants for the MP4 method are larger than those for the MP2 method ~the only exception is De!.
    [Show full text]
  • Virt&L-Comm.3.2012.1
    Virt&l-Comm.3.2012.1 A MODERN APPROACH TO AB INITIO COMPUTING IN CHEMISTRY, MOLECULAR AND MATERIALS SCIENCE AND TECHNOLOGIES ANTONIO LAGANA’, DEPARTMENT OF CHEMISTRY, UNIVERSITY OF PERUGIA, PERUGIA (IT)* ABSTRACT In this document we examine the present situation of Ab initio computing in Chemistry and Molecular and Materials Science and Technologies applications. To this end we give a short survey of the most popular quantum chemistry and quantum (as well as classical and semiclassical) molecular dynamics programs and packages. We then examine the need to move to higher complexity multiscale computational applications and the related need to adopt for them on the platform side cloud and grid computing. On this ground we examine also the need for reorganizing. The design of a possible roadmap to establishing a Chemistry Virtual Research Community is then sketched and some examples of Chemistry and Molecular and Materials Science and Technologies prototype applications exploiting the synergy between competences and distributed platforms are illustrated for these applications the middleware and work habits into cooperative schemes and virtual research communities (part of the first draft of this paper has been incorporated in the white paper issued by the Computational Chemistry Division of EUCHEMS in August 2012) INTRODUCTION The computational chemistry (CC) community is made of individuals (academics, affiliated to research institutions and operators of chemistry related companies) carrying out computational research in Chemistry, Molecular and Materials Science and Technology (CMMST). It is to a large extent registered into the CC division (DCC) of the European Chemistry and Molecular Science (EUCHEMS) Society and is connected to other chemistry related organizations operating in Chemical Engineering, Biochemistry, Chemometrics, Omics-sciences, Medicinal chemistry, Forensic chemistry, Food chemistry, etc.
    [Show full text]
  • Ab Initio Investigation of the Ground State Properties of PO, PO + , and PO − Aristophanes Metropoulos, Aristotle Papakondylis, and Aristides Mavridis
    Ab initio investigation of the ground state properties of PO, PO + , and PO − Aristophanes Metropoulos, Aristotle Papakondylis, and Aristides Mavridis Citation: The Journal of Chemical Physics 119, 5981 (2003); doi: 10.1063/1.1599341 View online: http://dx.doi.org/10.1063/1.1599341 View Table of Contents: http://scitation.aip.org/content/aip/journal/jcp/119/12?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Correlated ab initio investigations on the intermolecular and intramolecular potential energy surfaces in the ground electronic state of the O 2 − ( X Π g 2 ) − HF ( X Σ + 1 ) complex J. Chem. Phys. 138, 014304 (2013); 10.1063/1.4772653 Molecular geometry of OCAgI determined by broadband rotational spectroscopy and ab initio calculations J. Chem. Phys. 136, 064306 (2012); 10.1063/1.3683221 Ab initio properties of Li-group-II molecules for ultracold matter studies J. Chem. Phys. 135, 164108 (2011); 10.1063/1.3653974 Calculation of the structure, potential energy surface, vibrational dynamics, and electric dipole properties for the Xe:HI van der Waals complex J. Chem. Phys. 134, 174302 (2011); 10.1063/1.3583817 Potential curves and spectroscopic properties for the ground state of ClO and for the ground and various excited states of ClO − J. Chem. Phys. 117, 9703 (2002); 10.1063/1.1516803 This article is copyrighted as indicated in the article. Reuse of AIP content is subject to the terms at: http://scitation.aip.org/termsconditions. Downloaded to IP: 128.240.225.44 On: Sat, 20 Dec 2014 03:10:02 JOURNAL OF CHEMICAL PHYSICS VOLUME 119, NUMBER 12 22 SEPTEMBER 2003 Ab initio investigation of the ground state properties of PO, PO¿, and POÀ Aristophanes Metropoulosa) Theoretical and Physical Chemistry Institute, National Hellenic Research Foundation, 48 Vass.
    [Show full text]
  • Computational Chemistry: a Practical Guide for Applying Techniques to Real-World Problems
    Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems. David C. Young Copyright ( 2001 John Wiley & Sons, Inc. ISBNs: 0-471-33368-9 (Hardback); 0-471-22065-5 (Electronic) COMPUTATIONAL CHEMISTRY COMPUTATIONAL CHEMISTRY A Practical Guide for Applying Techniques to Real-World Problems David C. Young Cytoclonal Pharmaceutics Inc. A JOHN WILEY & SONS, INC., PUBLICATION New York . Chichester . Weinheim . Brisbane . Singapore . Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ( 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008, E-Mail: PERMREQ @ WILEY.COM. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional person should be sought.
    [Show full text]
  • DALTON2011 Program Manual
    DALTON2011 Program Manual C. Angeli, K. L. Bak, V. Bakken, O. Christiansen, R. Cimiraglia, S. Coriani, P. Dahle, E. K. Dalskov, T. Enevoldsen, B. Fernandez, L. Ferrighi, L. Frediani, C. H¨attig, K. Hald, A. Halkier, H. Heiberg, T. Helgaker, H. Hettema, B. Jansik, H. J. Aa. Jensen, D. Jonsson, P. Jørgensen, S. Kirpekar, W. Klopper, S. Knecht, R. Kobayashi, J. Kongsted, H. Koch, A. Ligabue, O. B. Lutnæs, K. V. Mikkelsen, C. B. Nielsen, P. Norman, J. Olsen, A. Osted, M. J. Packer, T. B. Pedersen, Z. Rinkevicius, E. Rudberg, T. A. Ruden, K. Ruud, P. Sa lek,C. C. M. Samson, A. Sanchez de Meras, T. Saue, S. P. A. Sauer, B. Schimmelpfennig, A. H. Steindal, K. O. Sylvester-Hvid, P. R. Taylor, O. Vahtras, D. J. Wilson, and H. Agren,˚ Contents Preface ix 1 Introduction 1 1.1 General description of the manual . 2 1.2 Acknowledgments . 3 2 New features in the Dalton releases 4 2.1 New features in Dalton2011 . 4 2.2 New features in Dalton 2.0 (2005) . 7 2.3 New features in Dalton 1.2 . 10 I DALTON Installation Guide 13 3 Installation 14 3.1 Hardware/software supported . 14 3.2 Source files . 14 3.3 Installing the program using the Makefile . 15 3.4 Running the dalton2011 test suite . 18 4 Maintenance 20 4.1 Memory requirements . 20 4.1.1 Redimensioning dalton2011 ...................... 20 4.2 New versions, patches . 21 4.3 Reporting bugs and user support . 22 II DALTON User’s Guide 23 5 Getting started with dalton2011 24 5.1 The DALTON.INP file .
    [Show full text]
  • Acronyms Used in Theoretical Chemistry
    Pure & Appl. Chem., Vol. 68, No. 2, pp. 387-456, 1996. Printed in Great Britain. INTERNATIONAL UNION OF PURE AND APPLIED CHEMISTRY PHYSICAL CHEMISTRY DIVISION WORKING PARTY ON THEORETICAL AND COMPUTATIONAL CHEMISTRY ACRONYMS USED IN THEORETICAL CHEMISTRY Prepared for publication by the Working Party consisting of R. D. BROWN* (Australia, Chiman);J. E. BOWS (USA); R. HILDERBRANDT (USA); K. LIM (Australia); I. M. MILLS (UK); E. NIKITIN (Russia); M. H. PALMER (UK). The focal point to which to send comments and suggestions is the coordinator of the project: RONALD D. BROWN Chemistry Department, Monash University, Clayton, Victoria 3 168, Australia. Responses by e-mail would be particularly appreciated, the number being: rdbrown @ vaxc.cc .monash.edu.au another alternative is fax at: +61 3 9905 4597 Acronyms used in theoretical chemistry synogsis An alphabetic list of acronyms used in theoretical chemistry is presented. Some explanatory references have been added to make acronyms better understandable but still more are needed. Critical comments, additional references, etc. are requested. INTRODUC'IION The IUPAC Working Party on Theoretical Chemistry was persuaded, by discussion with colleagues, that the compilation of a list of acronyms used in theoretical chemistry would be a useful contribution. Initial lists of acronyms drawn up by several members of the working party have been augmented by the provision of a substantial list by Chemical Abstract Service (see footnote below). The working party is particularly grateful to CAS for this generous help. It soon became apparent that many of the acron ms needed more than mere spelling out to make them understandable and so we have added expr anatory references to many of them.
    [Show full text]
  • Enzyme Engineering
    Enzyme Engineering 10. Softwares for protein engineering Central dogma ¦ Central dogma of molecular biology Computation Biology Environment Protein Modeling Cell Central dogma ¦ Central dogma of molecular biology Biological Phenomena Function Sequence Protein Protein structure ¦Protein structure validation: Ramachandran plot Protein structure ¦ How to predict protein structure? ü Experimental • X-ray crystallography • NMR ü Simulational • Comparative method • Threading method • Ab initio method Protein structure ¦ Experimental ü X-ray crystallography • No size limitation • Single structure ü NMR • Structure determination from chemical shift • Multiple ensemble structures • less than 300 a.a Protein structure ¦ Simulational ü Ab initio method • Ab initio = “from the beginning”; in strictest sense uses first principle, not information about other protein structure • Size limitation : less than 50~60 a.a • expensive computing time • Good for finding new fold structure • Not exact method for already existed fold structure AKWDSTF KKGGGLLL Protein structure ¦ Simulational ü Homology modeling • Comparative protein modeling • Knowledge – based modeling • Extrapolation of the structure for a new sequence (target) form the known 3D structure of related family members (template) AKWDSTF KKGGGLLL Protein structure ¦ Simulational ü Threading method (Folding recognition) • Find a compatible fold for a given sequence • Take all possible segments from all PDB structures, “Thread” a target sequence through all conformations and compute some energy value
    [Show full text]