Materials Studio: CASTEP Guide
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department
Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department Name of FOSS Applications Employability GChemPaint GChemPaint is an editor for 2Dchem- GChemPaint is currently being developed ical structures with a multiple docu- as part of The Chemistry Development ment interface. Kit, and a Standard Widget Tool kit- based GChemPaint application is being developed, as part of Bioclipse. Jmol Jmol applet is used to explore the Jmol is a free, open source molecule viewer structure of molecules. Jmol applet is for students, educators, and researchers used to depict X-ray structures in chemistry and biochemistry. It is cross- platform, running on Windows, Mac OS X, and Linux/Unix systems. For PG Students LaTeX Document markup language and Value addition to academic Skills set. preparation system for Tex typesetting Essential for International paper presentation and scientific journals. For PG student for their project work Scilab Scientific Computation package for Value addition in technical problem numerical computations solving via use of computational methods for engineering problems, Applicable in Chemical, ECE, Electrical, Electronics, Civil, Mechanical, Mathematics etc. For PG student who are taking Physical Chemistry Avogadro Avogadro is a free and open source, Research and Development in Chemistry, advanced molecule editor and Pharmacist and University lecturers. visualizer designed for cross-platform use in computational chemistry, molecular modeling, material science, bioinformatics, etc. Spoken Tutorial Project, IIT Bombay Brochure for Commerce and Commerce IT Name of FOSS Applications / Employability LibreOffice – Writer, Calc, Writing letters, documents, creating spreadsheets, tables, Impress making presentations, desktop publishing LibreOffice – Base, Draw, Managing databases, Drawing, doing simple Mathematical Math operations For Commerce IT Students Drupal Drupal is a free and open source content management system (CMS). -
Pymol Modelling Workshop
PyMOL Modelling Workshop My website: http://pldserver1.biochem.queensu.ca/~rlc/work/teaching/BCHM442/ There you will find links to download the latest educational version of PyMOL as well as a link to my “Introduction to PyMOL”, which in turn has links to other people's PyMOL tutorials. Note also the PyMOL Wiki: http://pymolwiki.org. Structure files can be found by searching the Protein Data Bank (PDB) for structure: easy to remember website http://www.pdb.org. There is also the PDBe (PDB Europe, http://www.pdbe.org) that contains the same databank of structures, but with a different web interface for searching for structures and a different set of tools for analyzing structures. What is in a PDB file? Lots of information in the “header” (the section of the file preceding the actual atomic coordinates) as well as the coordinates for the atoms. When assessing a structure, one needs to take account of the resolution and R-factor, error estimates and missing residues. There is information about the sequence that was used to determine the structure with a sequence database reference. There is also information about the biological unit. In the case of crystal structures the biological unit may need to be generated by applying crystallographic symmetry operators, although there are web sites that also try to provide that information. (e.g. http://www.ebi.ac.uk/msd-srv/pisa/). Warning: One cannot blindly trust a crystal structure to be providing you with a completely accurate picture of reality. Reading the paper that describes the structure is a good start! Outline of PyMOL usage PyMOL is a very powerful, scriptable (customizable) tool for making publication-quality figures and performing analyses on structures. -
Dmol Guide to Select a Dmol3 Task 1
DMOL3 GUIDE MATERIALS STUDIO 8.0 Copyright Notice ©2014 Dassault Systèmes. All rights reserved. 3DEXPERIENCE, the Compass icon and the 3DS logo, CATIA, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, EXALEAD, 3D VIA, BIOVIA and NETVIBES are commercial trademarks or registered trademarks of Dassault Systèmes or its subsidiaries in the U.S. and/or other countries. All other trademarks are owned by their respective owners. Use of any Dassault Systèmes or its subsidiaries trademarks is subject to their express written approval. Acknowledgments and References To print photographs or files of computational results (figures and/or data) obtained using BIOVIA software, acknowledge the source in an appropriate format. For example: "Computational results obtained using software programs from Dassault Systèmes Biovia Corp.. The ab initio calculations were performed with the DMol3 program, and graphical displays generated with Materials Studio." BIOVIA may grant permission to republish or reprint its copyrighted materials. Requests should be submitted to BIOVIA Support, either through electronic mail to [email protected], or in writing to: BIOVIA Support 5005 Wateridge Vista Drive, San Diego, CA 92121 USA Contents DMol3 1 Setting up a molecular dynamics calculation20 Introduction 1 Choosing an ensemble 21 Further Information 1 Defining the time step 21 Tasks in DMol3 2 Defining the thermostat control 21 Energy 3 Constraints during dynamics 21 Setting up the calculation 3 Setting up a transition state calculation 22 Dynamics 4 Which method to use? -
Atomistic Molecular Dynamics Simulation of Graphene- Isoprene Nanocomposites
Atomistic molecular dynamics simulation of graphene- isoprene nanocomposites P Chanlert1, J Wong-Ekkabut2, W Liewrian3,4,5 and T Sutthibutpong3,4,5* 1 Program of Physics, Faculty of Science and Technology, Songkhla Rajabhat University, Songkhla, 90000, Thailand 2 Department of Physics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand 3 Theoretical and Computational Physics Group, Department of Physics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand 4 Theoretical and Computational Science Center (TaCS), Science Laboratory Building, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand 5 Thailand Center of Excellence in Physics (ThEP Center), Commission on Higher Education, Bangkok 10400, Thailand *email: [email protected] Abstract. A series of atomistic molecular dynamics simulations were performed for 4-mer isoprene molecules confined between graphene sheets with varied graphene separations in order to observe the finite size effects arose from the van der Waals interactions between the isoprene oligomer, forming high density shells at the graphene interfaces. For the small confinement regions, 1.24 nm and 2.21 nm, local density of isoprene at the graphene interface becomes higher than 10 times the bulk density. These results provided the further insights towards the rational design of nanostructures based on graphene sheets and natural rubber polymer. 1. Introduction Composite material is the combination between two materials or more where chemical reaction between these materials is absent. In composites, one with higher quantity is called matrix while those with lesser quantity are called fillers. In general, adding fillers into matrix material enhances both physical and mechanical properties in some ways. -
EPSRC Service Level Agreement with STFC for Computational Science Support
CoSeC Computational Science Centre for Research Communities EPSRC Service Level Agreement with STFC for Computational Science Support FY 2016/17 Report and Update on FY 2017/18 Work Plans This document contains the 2016/17 plans, 2016/17 summary reports, and 2017/18 plans for the programme in support of CCP and HEC communities delivered by STFC and funded by EPSRC through a Service Level Agreement. Notes in blue are in-year updates on progress to the tasks included in the 2016/17 plans. Text highlighted in yellow shows changes to the draft 2017/18 plans that we submitted in January 2017. Contents CCP5 – Computer Simulation of Condensed Phases .......................................................................... 4 CCP5 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 4 CCP5 – Summary Report (1 April 2016 – 31 March 2017) .................................................... 7 CCP5 –2017 / 18 Plans (1 April 2017 – 31 March 2018) ....................................................... 8 CCP9 – Electronic Structure of Solids .................................................................................................. 9 CCP9 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 9 CCP9 – Summary Report (1 April 2016 – 31 March 2017) .................................................. 11 CCP9 – 2017 / 18 Plans (1 April 2017 – 31 March 2018) .................................................... 12 CCP-mag – Computational Multiscale -
Arxiv:1508.02735V1 [Cond-Mat.Mtrl-Sci] 11 Aug 2015
A critical look at methods for calculating charge transfer couplings fast and accurately Pablo Ramos, Marc Mankarious, and Michele Pavanello∗ Department of Chemistry, Rutgers University, Newark, NJ 07102, USA E-mail: [email protected] Abstract We present here a short and subjective review of methods for calculating charge trans- fer couplings. Although we mostly focus on Density Functional Theory, we discuss a small subset of semiempirical methods as well as the adiabatic-to-diabatic transformation methods typically coupled with wavefunction-based electronic structure calculations. In this work, we will present the reader with a critical assessment of the regimes that can be modelled by the various methods – their strengths and weaknesses. In order to give a feeling about the practical aspects of the calculations, we also provide the reader with a practical protocol for running coupling calculations with the recently developed FDE-ET method. arXiv:1508.02735v1 [cond-mat.mtrl-sci] 11 Aug 2015 ∗To whom correspondence should be addressed 1 Contents 1 Introduction 3 2 DFT Based Methods 5 2.1 The Frozen Density Embedding formalism . 5 2.1.1 FDE-ET method . 9 2.1.2 Distance dependence of the electronic coupling . 12 2.1.3 Hole transfer in DNA oligomers . 14 2.2 Constrained Density Functional Theory Applied to Electron Transfer Simulations . 16 2.3 Fragment Orbital DFT . 18 2.3.1 Hole transfer rates on DNA hairpins . 20 2.3.2 The Curious Case of Sulfite Oxidase . 20 2.4 Ultrafast computations of the electronic couplings: The AOM method . 22 2.5 Note on orthogonality . -
Interoperability Between Electronic Structure Codes with the Quippy Toolkit
Interoperability between electronic structure codes with the quippy toolkit James Kermode Department of Physics King’s College London 6 September 2012 Outline Overview of libAtoms and QUIP Scripting interfaces Overview of quippy capabilities Live demo! Summary and Conclusions Outline Overview of libAtoms and QUIP Scripting interfaces Overview of quippy capabilities Live demo! Summary and Conclusions I The QUIP (QUantum mechanics and Interatomic Potentials) package, built on top of libAtoms, implements a wide variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages. I Various hybrid combinations are also supported in the style of QM/MM, including ‘Learn on the Fly’ scheme (LOTF).2 3 I quippy is a Python interface to libAtoms and QUIP. The libAtoms, QUIP and quippy packages 1 I The libAtoms package is a software library written in Fortran 95 for the purposes of carrying out molecular dynamics simulations. 1http://www.libatoms.org 2Csányi et al., PRL (2004) 3http://www.jrkermode.co.uk/quippy I Various hybrid combinations are also supported in the style of QM/MM, including ‘Learn on the Fly’ scheme (LOTF).2 3 I quippy is a Python interface to libAtoms and QUIP. The libAtoms, QUIP and quippy packages 1 I The libAtoms package is a software library written in Fortran 95 for the purposes of carrying out molecular dynamics simulations. I The QUIP (QUantum mechanics and Interatomic Potentials) package, built on top of libAtoms, implements a wide variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages. 1http://www.libatoms.org 2Csányi et al., PRL (2004) 3http://www.jrkermode.co.uk/quippy 3 I quippy is a Python interface to libAtoms and QUIP. -
Surface Functionalization of H-Terminated Diamond with NH and NH2 on the (1, 0, 0), (1, 1, 0) and (1, 1, 1) Surface Planes Using Density Functional Theory
Uppsala University Bachelor project, 15hp Surface functionalization of H-terminated diamond with NH and NH2 on the (1; 0; 0), (1; 1; 0) and (1; 1; 1) surface planes using Density Functional Theory Sara Kjellberg Supervised by Prof. Karin Larsson October 5, 2017 Abstract The adsorption energy of aminated diamond surfaces on the (1; 1; 1), (1; 1; 0) and (1; 0; 0) diamond surfaces have been calculated with density functional theory (DFT). The resulting values show that bridging is favoured before on-top nitrogen at low coverage for all the low index planes. The trend is even more pronounced as the coverage increases, most likely due to sterical hindrance. As expected, the imidogen (NH) and amidogen (NH2) groups are bonded to the surface planes in the order (1; 0; 0) > (1; 1; 1) > (1; 1; 0) as decrease in energy is the highest on the (1; 0; 0) plane and lowest on the (1 010) plane. The effect of sterical hindrance is largest at the (1; 1; 0) surface while the (1; 0; 0) surface is almost unaffected by neighbouring functional groups. Also, double bonded on-top imidogen (NH) is the least stable group at the (1; 0; 0) surface at low coverage while it is almost as stable as bridging NH when having a full mono-layer. Moreover, on-top imidogen (NH) was highly distorted and forced a change in surface geometry 1 Contents 1 Goal 3 2 Introduction 3 2.1 Background . .3 3 Theory 4 3.1 Surfaces of diamond . .4 3.1.1 General surface theory . -
Trends in Atomistic Simulation Software Usage [1.3]
A LiveCoMS Perpetual Review Trends in atomistic simulation software usage [1.3] Leopold Talirz1,2,3*, Luca M. Ghiringhelli4, Berend Smit1,3 1Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland; 2Theory and Simulation of Materials (THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 3National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 4The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany This LiveCoMS document is Abstract Driven by the unprecedented computational power available to scientific research, the maintained online on GitHub at https: use of computers in solid-state physics, chemistry and materials science has been on a continuous //github.com/ltalirz/ rise. This review focuses on the software used for the simulation of matter at the atomic scale. We livecoms-atomistic-software; provide a comprehensive overview of major codes in the field, and analyze how citations to these to provide feedback, suggestions, or help codes in the academic literature have evolved since 2010. An interactive version of the underlying improve it, please visit the data set is available at https://atomistic.software. GitHub repository and participate via the issue tracker. This version dated August *For correspondence: 30, 2021 [email protected] (LT) 1 Introduction Gaussian [2], were already released in the 1970s, followed Scientists today have unprecedented access to computa- by force-field codes, such as GROMOS [3], and periodic tional power. -
Porting the DFT Code CASTEP to Gpgpus
Porting the DFT code CASTEP to GPGPUs Toni Collis [email protected] EPCC, University of Edinburgh CASTEP and GPGPUs Outline • Why are we interested in CASTEP and Density Functional Theory codes. • Brief introduction to CASTEP underlying computational problems. • The OpenACC implementation http://www.nu-fuse.com CASTEP: a DFT code • CASTEP is a commercial and academic software package • Capable of Density Functional Theory (DFT) and plane wave basis set calculations. • Calculates the structure and motions of materials by the use of electronic structure (atom positions are dictated by their electrons). • Modern CASTEP is a re-write of the original serial code, developed by Universities of York, Durham, St. Andrews, Cambridge and Rutherford Labs http://www.nu-fuse.com CASTEP: a DFT code • DFT/ab initio software packages are one of the largest users of HECToR (UK national supercomputing service, based at University of Edinburgh). • Codes such as CASTEP, VASP and CP2K. All involve solving a Hamiltonian to explain the electronic structure. • DFT codes are becoming more complex and with more functionality. http://www.nu-fuse.com HECToR • UK National HPC Service • Currently 30- cabinet Cray XE6 system – 90,112 cores • Each node has – 2×16-core AMD Opterons (2.3GHz Interlagos) – 32 GB memory • Peak of over 800 TF and 90 TB of memory http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) Ab initio codes (VASP, CP2K, CASTEP, ONETEP, NWChem, Quantum Espresso, GAMESS-US, SIESTA, GAMESS-UK, MOLPRO) GS2NEMO ChemShell 2%2% SENGA2% 3% UM Others 4% 34% MITgcm 4% CASTEP 4% GROMACS 6% DL_POLY CP2K VASP 5% 8% 19% http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) 35% of the Chemistry software on HECToR is using DFT methods. -
HPC Issues for DFT Calculations
HPC Issues for DFT Calculations Adrian Jackson EPCC Scientific Simulation • Simulation fast becoming 4 th pillar of science – Observation, Theory, Experimentation, Simulation • Explore universe through simulation rather than experimentation – Test theories – Predict or validate experiments – Simulate “untestable” science • Reproduce “real world” in computers – Generally simplified – Dimensions and timescales restricted – Simulation of scientific problem or environment – Input of real data – Output of simulated data – Parameter space studies – Wide range of approaches http://www.nu-fuse.com Reduce runtime • Serial code optimisations – Reduce runtime through efficiencies – Unlikely to produce required savings • Upgrade hardware – 1965: Moore’s law predicts growth in complexity of processors – Doubling of CPU performance – Performance often improved through on chip parallelism http://www.nu-fuse.com Parallel Background • Why not just make a faster chip? – Theoretical • Physical limitations to size and speed of a single chip • Capacitance increases with complexity • Speed of light, size of atoms, dissipation of heat • The power used by a CPU core is proportional to Clock Frequency x Voltage 2 • Voltage reduction vs Clock speed for power requirements – Voltages become too small for “digital” differences – Practical • Developing new chips is incredibly expensive • Must make maximum use of existing technology http://www.nu-fuse.com Parallel Systems • Different types of parallel systems P M – Shared memory P M P M – Distributed memory P M Interconnect -
Lawrence Berkeley National Laboratory Recent Work
Lawrence Berkeley National Laboratory Recent Work Title From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape. Permalink https://escholarship.org/uc/item/4sm897jh Journal Chemical reviews, 121(8) ISSN 0009-2665 Authors Kowalski, Karol Bair, Raymond Bauman, Nicholas P et al. Publication Date 2021-04-01 DOI 10.1021/acs.chemrev.0c00998 Peer reviewed eScholarship.org Powered by the California Digital Library University of California From NWChem to NWChemEx: Evolving with the computational chemistry landscape Karol Kowalski,y Raymond Bair,z Nicholas P. Bauman,y Jeffery S. Boschen,{ Eric J. Bylaska,y Jeff Daily,y Wibe A. de Jong,x Thom Dunning, Jr,y Niranjan Govind,y Robert J. Harrison,k Murat Keçeli,z Kristopher Keipert,? Sriram Krishnamoorthy,y Suraj Kumar,y Erdal Mutlu,y Bruce Palmer,y Ajay Panyala,y Bo Peng,y Ryan M. Richard,{ T. P. Straatsma,# Peter Sushko,y Edward F. Valeev,@ Marat Valiev,y Hubertus J. J. van Dam,4 Jonathan M. Waldrop,{ David B. Williams-Young,x Chao Yang,x Marcin Zalewski,y and Theresa L. Windus*,r yPacific Northwest National Laboratory, Richland, WA 99352 zArgonne National Laboratory, Lemont, IL 60439 {Ames Laboratory, Ames, IA 50011 xLawrence Berkeley National Laboratory, Berkeley, 94720 kInstitute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794 ?NVIDIA Inc, previously Argonne National Laboratory, Lemont, IL 60439 #National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6373 @Department of Chemistry, Virginia Tech, Blacksburg, VA 24061 4Brookhaven National Laboratory, Upton, NY 11973 rDepartment of Chemistry, Iowa State University and Ames Laboratory, Ames, IA 50011 E-mail: [email protected] 1 Abstract Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems.