Nanoinformatics: Emerging Databases and Available Tools
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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. -
Open Babel Documentation Release 2.3.1
Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options ................................................. -
An Ab Initio Materials Simulation Code
CP2K: An ab initio materials simulation code Lianheng Tong Physics on Boat Tutorial , Helsinki, Finland 2015-06-08 Faculty of Natural & Mathematical Sciences Department of Physics Brief Overview • Overview of CP2K - General information about the package - QUICKSTEP: DFT engine • Practical example of using CP2K to generate simulated STM images - Terminal states in AGNR segments with 1H and 2H termination groups What is CP2K? Swiss army knife of molecular simulation www.cp2k.org • Geometry and cell optimisation Energy and • Molecular dynamics (NVE, Force Engine NVT, NPT, Langevin) • STM Images • Sampling energy surfaces (metadynamics) • DFT (LDA, GGA, vdW, • Finding transition states Hybrid) (Nudged Elastic Band) • Quantum Chemistry (MP2, • Path integral molecular RPA) dynamics • Semi-Empirical (DFTB) • Monte Carlo • Classical Force Fields (FIST) • And many more… • Combinations (QM/MM) Development • Freely available, open source, GNU Public License • www.cp2k.org • FORTRAN 95, > 1,000,000 lines of code, very active development (daily commits) • Currently being developed and maintained by community of developers: • Switzerland: Paul Scherrer Institute Switzerland (PSI), Swiss Federal Institute of Technology in Zurich (ETHZ), Universität Zürich (UZH) • USA: IBM Research, Lawrence Livermore National Laboratory (LLNL), Pacific Northwest National Laboratory (PNL) • UK: Edinburgh Parallel Computing Centre (EPCC), King’s College London (KCL), University College London (UCL) • Germany: Ruhr-University Bochum • Others: We welcome contributions from -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
Automated Construction of Quantum–Classical Hybrid Models Arxiv:2102.09355V1 [Physics.Chem-Ph] 18 Feb 2021
Automated construction of quantum{classical hybrid models Christoph Brunken and Markus Reiher∗ ETH Z¨urich, Laboratorium f¨urPhysikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Z¨urich, Switzerland February 18, 2021 Abstract We present a protocol for the fully automated construction of quantum mechanical-(QM){ classical hybrid models by extending our previously reported approach on self-parametri- zing system-focused atomistic models (SFAM) [J. Chem. Theory Comput. 2020, 16 (3), 1646{1665]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the au- tomated construction and evaluation of differently sized QM regions with a bearable com- putational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing pa- rameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a con- venient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional clas- sical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region. arXiv:2102.09355v1 [physics.chem-ph] 18 Feb 2021 ∗Corresponding author; e-mail: [email protected] 1 1 Introduction In contrast to most protocols of computational quantum chemistry that consider isolated molecules, chemical processes can take place in a vast variety of complex environments. -
Metalloboranes from First-Principles Calculations: a Candidate for High-Density Hydrogen Storage
Metalloboranes from first-principles calculations: A candidate for high-density hydrogen storage A. R. Akbarzadeh, D. Vrinceanu, and C.J. Tymczak Department of Physics, Texas Southern University, Houston, Texas 77004, USA Abstract Using first principles calculations, we show the high hydrogen storage capacity of a new class of compounds, metalloboranes. Metalloboranes are transition metal (TM) and borane compounds that obey a novel-bonding scheme. We have found that the transition metal atoms can bind up to 10 H2-molecules with an average binding energy of 30 kJ/mole of H2, which lies favorably within the reversible adsorption range. Among the first row TM atoms, Sc and Ti are found to be the optimum in maximizing the H2 storage on the metalloborane cluster. Additionally, being ionically bonded to the borane molecule, the TMs do not suffer from the aggregation problem, which has been the biggest hurdle for the success of TM- decorated graphitic materials for hydrogen storage. Furthermore, since the borane 6-atom ring has identical bonding properties as carbon rings it is possible to link the metalloboranes into metal organic frameworks (MOF’s), which are thus able to adsorb hydrogen via Kubas interaction as well as the well-known van der Waals interaction. Finally, we construct a simple Monte-Catlo algorithm for Hydrogen uptake and show that Titanium metalloboranes in a MOF5 structure can absorb up to 11.5% hydrogen per weight at 100 bar of pressure. I. Introduction Due to the geographical distribution and supply limitation of fossil fuel resources and the increasing perceived negative impact of carbon dioxide emission in the environment, it is advantageous for the societies to take action in the search and implementation of alternative energy systems. -
User Manual for the Uppsala Quantum Chemistry Package UQUANTCHEM V.35
User manual for the Uppsala Quantum Chemistry package UQUANTCHEM V.35 by Petros Souvatzis Uppsala 2016 Contents 1 Introduction 6 2 Compiling the code 7 3 What Can be done with UQUANTCHEM 9 3.1 Hartree Fock Calculations . 9 3.2 Configurational Interaction Calculations . 9 3.3 M¨ollerPlesset Calculations (MP2) . 9 3.4 Density Functional Theory Calculations (DFT)) . 9 3.5 Time Dependent Density Functional Theory Calculations (TDDFT)) . 10 3.6 Quantum Montecarlo Calculations . 10 3.7 Born Oppenheimer Molecular Dynamics . 10 4 Setting up a UQANTCHEM calculation 12 4.1 The input files . 12 4.1.1 The INPUTFILE-file . 12 4.1.2 The BASISFILE-file . 13 4.1.3 The BASISFILEAUX-file . 14 4.1.4 The DENSMATSTARTGUESS.dat-file . 14 4.1.5 The MOLDYNRESTART.dat-file . 14 4.1.6 The INITVELO.dat-file . 15 4.1.7 Running Uquantchem . 15 4.2 Input parameters . 15 4.2.1 CORRLEVEL ................................. 15 4.2.2 ADEF ..................................... 15 4.2.3 DOTDFT ................................... 16 4.2.4 NSCCORR ................................... 16 4.2.5 SCERR .................................... 16 4.2.6 MIXTDDFT .................................. 16 4.2.7 EPROFILE .................................. 16 4.2.8 DOABSSPECTRUM ............................... 17 4.2.9 NEPERIOD .................................. 17 4.2.10 EFIELDMAX ................................. 17 4.2.11 EDIR ..................................... 17 4.2.12 FIELDDIR .................................. 18 4.2.13 OMEGA .................................... 18 2 CONTENTS 3 4.2.14 NCHEBGAUSS ................................. 18 4.2.15 RIAPPROX .................................. 18 4.2.16 LIMPRECALC (Only openmp-version) . 19 4.2.17 DIAGDG ................................... 19 4.2.18 NLEBEDEV .................................. 19 4.2.19 MOLDYN ................................... 19 4.2.20 DAMPING ................................... 19 4.2.21 XLBOMD .................................. -
Living at the Top of the Top500: Myopia from Being at the Bleeding Edge
Living at the Top of the Top500: Myopia from Being at the Bleeding Edge Bronson Messer Oak Ridge Leadership Computing Facility & Theoretical Astrophysics Group Oak Ridge National Laboratory Department of Physics & Astronomy University of Tennessee Friday, July 1, 2011 Outline • Statements made without proof • OLCF’s Center for Accelerated Application Readiness • Speculations on task-based approaches for multiphysics applications in astrophysics (e.g. blowing up stars) 2 Friday, July 1, 2011 Riffing on Hank’s fable... 3 Friday, July 1, 2011 The Effects of Moore’s Law and Slacking 1 on Large Computations Chris Gottbrath, Jeremy Bailin, Casey Meakin, Todd Thompson, J.J. Charfman Steward Observatory, University of Arizona Abstract We show that, in the context of Moore’s Law, overall productivity can be increased for large enough computations by ‘slacking’orwaiting for some period of time before purchasing a computer and beginning the calculation. According to Moore’s Law, the computational power availableataparticular price doubles every 18 months. Therefore it is conceivable that for sufficiently large numerical calculations and fixed budgets, computing power will improve quickly enough that the calculation will finish faster if we wait until the available computing power is sufficiently better and start the calculation then. The Effects of Moore’s Law and Slacking 1Figureon Large 1: Computations Chris Gottbrath, Jeremy Bailin, Casey Meakin, Todd Thompson, J.J. Charfman 1 The Effects of Moore’sSteward Observatory, Law and University Slacking of Arizona on Large astro-ph/9912202 Computations Abstract Chris Gottbrath, Jeremy Bailin, Casey Meakin, Todd Thompson, We show that, in the context of Moore’s Law, overall productivity can be increased forJ.J. -
The Norbornene Mystery Revealed
Supplementary Material (ESI) for Chemical Communications This journal is (c) The Royal Society of Chemistry 2010 Electronic Supporting Information The Norbornene Mystery Revealed Stephan N. Steinmann, Pierre Vogel, Yirong Mo, and Clémence Corminboeuf* To be published in Chemical Communication. The supplementary Data were prepared on June 22, 2010 and contains 14 pages. Contents: Method Section Figure S1: Molecules used for the comparisons between known experimental 1 Jcc-coupling constants (in Hertz) and the computed values. 1 Table S1 JCC-coupling comparison between experiment and computation. 1 Table S2: JCC-coupling constants for all compounds considered. Cartesian coordinates: B3LYP/6-311+G(d,p) (BLW and canonical) optimized structures for all compounds considered in the article. Contents: Computational Details 1 Table S1: JCC-coupling constants for all compounds considered. 1 Figure S1: Molecules for JCC-coupling comparison between experiment and computation. 1 Table S2: JCC-coupling comparison between experiment and computation. B3LYP/6-311+G(d,p) (BLW and canonical) optimized structures for all compounds considered in the article. Computational Details Standard geometries were optimized at the B3LYP1, 2/6-311+G** level using Gaussian 09.3 The BLW- constrained B3LYP/6-311+G** geometries (‘loc’) were optimized using a modified version of GAMESS-US (release 2008)4interfaced with the BLW-module.5, 6 Both the density and the J-coupling constants have been computed at the PBE7/IGLO-III level in Dalton 2.0.8 The BLW-eigenvalues and eigenvectors were optimized at the given DFT level with the SCF module of Dalton, SIRIUS, that has been interfaced with the BLW- module. -
Computational Chemistry for Chemistry Educators Shawn C
Volume 1, Issue 1 Journal Of Computational Science Education Computational Chemistry for Chemistry Educators Shawn C. Sendlinger Clyde R. Metz North Carolina Central University College of Charleston Department of Chemistry Department of Chemistry and Biochemistry 1801 Fayetteville Street, 66 George Street, Durham, NC 27707 Charleston, SC 29424 919-530-6297 843-953-8097 [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we describe an ongoing project where the goal is to The majority of today’s students are technologically savvy and are develop competence and confidence among chemistry faculty so often more comfortable using computers than the faculty who they are able to utilize computational chemistry as an effective teach them. In order to harness the student’s interest in teaching tool. Advances in hardware and software have made technology and begin to use it as an educational tool, most faculty research-grade tools readily available to the academic community. members require some level of instruction and training. Because Training is required so that faculty can take full advantage of this chemistry research increasingly utilizes computation as an technology, begin to transform the educational landscape, and important tool, our approach to chemistry education should reflect attract more students to the study of science. this. The ability of computer technology to visualize and manipulate objects on an atomic scale can be a powerful tool to increase both student interest in chemistry as well as their level of Categories and Subject Descriptors understanding. Computational Chemistry for Chemistry Educators (CCCE) is a project that seeks to provide faculty the J.2 [Physical Sciences and Engineering]: Chemistry necessary knowledge, experience, and technology access so that they can begin to design and incorporate computational approaches in the courses they teach. -
Calculation of Effective Interaction Potentials From
Methodologies of systematic structure-based coarse-graining Alexander Lyubartsev Division of Physical Chemistry Department of Material and Environmental Chemistry Stockholm University E-CAM workshop: State of the art in mesoscale and multiscale modeling Dublin 29-31 May 2017 Outline 1. Multiscale and coarse-grained simulations 2. Systematic structure-based coarse-graining by Inverse Monte Carlo 3. Examples - water and ions - lipid bilayers and lipid assemblies 4. Software - MagiC Example of systematic coarse-graining: DNA in Chromatin: presentation 30 May Computer modeling: from 1st principles to mesoscale Levels of molecular modeling: First-principles Atomistic Meso-scale (Quantum Classical Langevine/ BD, Mechanics) Molecular Dynamics DPD... nuclei electrons atoms coarse-grained biomolecules atoms molecules molecules soft matter 0.1 nm 1.0 nm 10 nm 100 nm 1 000 nm Larger scale more approximations Mesoscale Simulations Length scale: > 10 nm ( nanoscale: 10 - 1000 nm) Atomistic modeling is generally not possible box 10 nm - more than 105 atoms even if doable for 105 - 106 particles - do not forget about time scale! larger size : longer time for equilibration and reliable sampling 105 atoms - time scale should be above 1 ms = 1000 ns Need approximations - coarse-graining Coarse-graining – an example Original size – 2.4Mb Compressed to 24 Kb Levels of coarse- graining Level of coarse-graining can be different. For example, for a DMPC lipid: All-atom model United-atom Coarse-grained Coarse-grained 118 atoms model: 10 sites: 3 sites: 46 united atoms Martini model Cooke model more details - chemical specificity faster computations; larger systems Coarse-graining of solvent: 1) Explicit solvent: 2) Implicit solvent: One or several solvent molecules are No solvent particles but their effect is united in a single site. -
64-194 Projekt Parallelrechnerevaluation Abschlussbericht
64-194 Projekt Parallelrechnerevaluation Abschlussbericht Automatisierte Build-Tests für Spack Sarah Lubitz Malte Fock [email protected] [email protected] Studiengang: LA Berufliche Schulen – Informatik Studiengang LaGym - Informatik Matr.-Nr. 6570465 Matr.-Nr. 6311966 Inhaltsverzeichnis i Inhaltsverzeichnis 1 Einleitung1 1.1 Motivation.....................................1 1.2 Vorbereitungen..................................2 1.2.1 Spack....................................2 1.2.2 Python...................................2 1.2.3 Unix-Shell.................................3 1.2.4 Slurm....................................3 1.2.5 GitHub...................................4 2 Code 7 2.1 Automatisiertes Abrufen und Installieren von Spack-Paketen.......7 2.1.1 Python Skript: install_all_packets.py..................7 2.2 Batch-Jobskripte..................................9 2.2.1 Test mit drei Paketen........................... 10 2.3 Analyseskript................................... 11 2.3.1 Test mit 500 Paketen........................... 12 2.3.2 Code zur Analyse der Error-Logs, forts................. 13 2.4 Hintereinanderausführung der Installationen................. 17 2.4.1 Wrapper Skript.............................. 19 2.5 E-Mail Benachrichtigungen........................... 22 3 Testlauf 23 3.1 Durchführung und Ergebnisse......................... 23 3.2 Auswertung und Schlussfolgerungen..................... 23 3.3 Fazit und Ausblick................................ 24 4 Schulische Relevanz 27 Literaturverzeichnis