A Summary of ERCAP Survey of the Users of Top Chemistry Codes
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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. -
Supporting Information
Electronic Supplementary Material (ESI) for RSC Advances. This journal is © The Royal Society of Chemistry 2020 Supporting Information How to Select Ionic Liquids as Extracting Agent Systematically? Special Case Study for Extractive Denitrification Process Shurong Gaoa,b,c,*, Jiaxin Jina,b, Masroor Abroc, Ruozhen Songc, Miao Hed, Xiaochun Chenc,* a State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China b Research Center of Engineering Thermophysics, North China Electric Power University, Beijing, 102206, China c Beijing Key Laboratory of Membrane Science and Technology & College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, PR China d Office of Laboratory Safety Administration, Beijing University of Technology, Beijing 100124, China * Corresponding author, Tel./Fax: +86-10-6443-3570, E-mail: [email protected], [email protected] 1 COSMO-RS Computation COSMOtherm allows for simple and efficient processing of large numbers of compounds, i.e., a database of molecular COSMO files; e.g. the COSMObase database. COSMObase is a database of molecular COSMO files available from COSMOlogic GmbH & Co KG. Currently COSMObase consists of over 2000 compounds including a large number of industrial solvents plus a wide variety of common organic compounds. All compounds in COSMObase are indexed by their Chemical Abstracts / Registry Number (CAS/RN), by a trivial name and additionally by their sum formula and molecular weight, allowing a simple identification of the compounds. We obtained the anions and cations of different ILs and the molecular structure of typical N-compounds directly from the COSMObase database in this manuscript. -
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 -
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. -
Popular GPU-Accelerated Applications
LIFE & MATERIALS SCIENCES GPU-ACCELERATED APPLICATIONS | CATALOG | AUG 12 LIFE & MATERIALS SCIENCES APPLICATIONS CATALOG Application Description Supported Features Expected Multi-GPU Release Status Speed Up* Support Bioinformatics BarraCUDA Sequence mapping software Alignment of short sequencing 6-10x Yes Available now reads Version 0.6.2 CUDASW++ Open source software for Smith-Waterman Parallel search of Smith- 10-50x Yes Available now protein database searches on GPUs Waterman database Version 2.0.8 CUSHAW Parallelized short read aligner Parallel, accurate long read 10x Yes Available now aligner - gapped alignments to Version 1.0.40 large genomes CATALOG GPU-BLAST Local search with fast k-tuple heuristic Protein alignment according to 3-4x Single Only Available now blastp, multi cpu threads Version 2.2.26 GPU-HMMER Parallelized local and global search with Parallel local and global search 60-100x Yes Available now profile Hidden Markov models of Hidden Markov Models Version 2.3.2 mCUDA-MEME Ultrafast scalable motif discovery algorithm Scalable motif discovery 4-10x Yes Available now based on MEME algorithm based on MEME Version 3.0.12 MUMmerGPU A high-throughput DNA sequence alignment Aligns multiple query sequences 3-10x Yes Available now LIFE & MATERIALS& LIFE SCIENCES APPLICATIONS program against reference sequence in Version 2 parallel SeqNFind A GPU Accelerated Sequence Analysis Toolset HW & SW for reference 400x Yes Available now assembly, blast, SW, HMM, de novo assembly UGENE Opensource Smith-Waterman for SSE/CUDA, Fast short -
Electronic Supporting Information a New Class of Soluble and Stable Transition-Metal-Substituted Polyoxoniobates: [Cr2(OH)4Nb10o
Electronic Supplementary Material (ESI) for Dalton Transactions This journal is © The Royal Society of Chemistry 2012 Electronic Supporting Information A New Class of Soluble and Stable Transition-Metal-Substituted Polyoxoniobates: [Cr2(OH)4Nb10O30]8-. Jung-Ho Son, C. André Ohlin and William H. Casey* A) Computational Results The two most interesting aspects of this new polyoxoanion is the inclusion of a more redox active metal and the optical activity in the visible region. For these reasons we in particular wanted to explore whether it was possible to readily and routinely predict the location of the absorbance bands of this type of compound. Also, owing to the difficulty in isolating the oxidised and reduced forms of the central polyanion dichromate species, we employed computations at the density functional theory (DFT) level to get a better idea of the potential characteristics of these forms. Structures were optimised for anti-parallel and parallel electronic spin alignments, and for both high and low spin cases where relevant. Intermediate spin configurations were 8- II III also tested for [Cr2(OH)4Nb10O30] . As expected, the high spin form of the Cr Cr complex was favoured (Table S1, below). DFT overestimates the bond lengths in general, but gives a good picture of the general distortion of the octahedral chromium unit, and in agreement with the crystal structure predicts that the shortest bond is the Cr-µ2-O bond trans from the central µ6- oxygen in the Lindqvist unit, and that the (Nb-)µ2-O-Cr-µ4-O(-Nb,Nb,Cr) angle is smaller by ca 5˚ from the ideal 180˚. -
Computer-Assisted Catalyst Development Via Automated Modelling of Conformationally Complex Molecules
www.nature.com/scientificreports OPEN Computer‑assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Sibo Lin1*, Jenna C. Fromer2, Yagnaseni Ghosh1, Brian Hanna1, Mohamed Elanany3 & Wei Xu4 Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring signifcant researcher time to implement. We automate this workfow using all open‑source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious efects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save signifcant experimental efort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving signifcant time by ruling out candidates with non‑trivial synthetic routes and poor expected catalytic performance. Quantum mechanical methods with high energy accuracy, such as density functional theory (DFT), can opti- mize molecular input structures to a nearby local minimum, but calculating accurate reaction thermodynamics requires fnding global minimum energy structures1,2. For simple molecules, expert intuition can identify a few minima to focus study on, but an alternative approach must be considered for more complex molecules or to eventually fulfl the dream of autonomous catalyst design 3,4: the potential energy surface must be frst surveyed with a computationally efcient method; then minima from this survey must be refned using slower, more accurate methods; fnally, for molecules possessing low-frequency vibrational modes, those modes need to be treated appropriately to obtain accurate thermodynamic energies 5–7. -
Dalton2018.0 – Lsdalton Program Manual
Dalton2018.0 – lsDalton Program Manual V. Bakken, A. Barnes, R. Bast, P. Baudin, S. Coriani, R. Di Remigio, K. Dundas, P. Ettenhuber, J. J. Eriksen, L. Frediani, D. Friese, B. Gao, T. Helgaker, S. Høst, I.-M. Høyvik, R. Izsák, B. Jansík, F. Jensen, P. Jørgensen, J. Kauczor, T. Kjærgaard, A. Krapp, K. Kristensen, C. Kumar, P. Merlot, C. Nygaard, J. Olsen, E. Rebolini, S. Reine, M. Ringholm, K. Ruud, V. Rybkin, P. Sałek, A. Teale, E. Tellgren, A. Thorvaldsen, L. Thøgersen, M. Watson, L. Wirz, and M. Ziolkowski Contents Preface iv I lsDalton Installation Guide 1 1 Installation of lsDalton 2 1.1 Installation instructions ............................. 2 1.2 Hardware/software supported .......................... 2 1.3 Source files .................................... 2 1.4 Installing the program using the Makefile ................... 3 1.5 The Makefile.config ................................ 5 1.5.1 Intel compiler ............................... 5 1.5.2 Gfortran/gcc compiler .......................... 9 1.5.3 Using 64-bit integers ........................... 12 1.5.4 Using Compressed-Sparse Row matrices ................ 13 II lsDalton User’s Guide 14 2 Getting started with lsDalton 15 2.1 The Molecule input file ............................ 16 2.1.1 BASIS ................................... 17 2.1.2 ATOMBASIS ............................... 18 2.1.3 User defined basis sets .......................... 19 2.2 The LSDALTON.INP file ............................ 21 3 Interfacing to lsDalton 24 3.1 The Grand Canonical Basis ........................... 24 3.2 Basisset and Ordering .............................. 24 3.3 Normalization ................................... 25 i CONTENTS ii 3.4 Matrix File Format ................................ 26 3.5 The lsDalton library bundle .......................... 27 III lsDalton Reference Manual 29 4 List of lsDalton keywords 30 4.1 **GENERAL ................................... 31 4.1.1 *TENSOR ............................... -
Starting SCF Calculations by Superposition of Atomic Densities
Starting SCF Calculations by Superposition of Atomic Densities J. H. VAN LENTHE,1 R. ZWAANS,1 H. J. J. VAN DAM,2 M. F. GUEST2 1Theoretical Chemistry Group (Associated with the Department of Organic Chemistry and Catalysis), Debye Institute, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands 2CCLRC Daresbury Laboratory, Daresbury WA4 4AD, United Kingdom Received 5 July 2005; Accepted 20 December 2005 DOI 10.1002/jcc.20393 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: We describe the procedure to start an SCF calculation of the general type from a sum of atomic electron densities, as implemented in GAMESS-UK. Although the procedure is well known for closed-shell calculations and was already suggested when the Direct SCF procedure was proposed, the general procedure is less obvious. For instance, there is no need to converge the corresponding closed-shell Hartree–Fock calculation when dealing with an open-shell species. We describe the various choices and illustrate them with test calculations, showing that the procedure is easier, and on average better, than starting from a converged minimal basis calculation and much better than using a bare nucleus Hamiltonian. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 926–932, 2006 Key words: SCF calculations; atomic densities Introduction hrstuhl fur Theoretische Chemie, University of Kahrlsruhe, Tur- bomole; http://www.chem-bio.uni-karlsruhe.de/TheoChem/turbo- Any quantum chemical calculation requires properly defined one- mole/),12 GAMESS(US) (Gordon Research Group, GAMESS, electron orbitals. These orbitals are in general determined through http://www.msg.ameslab.gov/GAMESS/GAMESS.html, 2005),13 an iterative Hartree–Fock (HF) or Density Functional (DFT) pro- Spartan (Wavefunction Inc., SPARTAN: http://www.wavefun. -
In Quantum Chemistry
http://www.cca-forum.org Computational Quality of Service (CQoS) in Quantum Chemistry Joseph Kenny1, Kevin Huck2, Li Li3, Lois Curfman McInnes3, Heather Netzloff4, Boyana Norris3, Meng-Shiou Wu4, Alexander Gaenko4 , and Hirotoshi Mori5 1Sandia National Laboratories, 2University of Oregon, 3Argonne National Laboratory, 4Ames Laboratory, 5Ochanomizu University, Japan This work is a collaboration among participants in the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS), Performance Engineering Research Institute (PERI), Quantum Chemistry Science Application Partnership (QCSAP), and the Tuning and Analysis Utilities (TAU) group at the University of Oregon. Quantum Chemistry and the CQoS in Quantum Chemistry: Motivation and Approach Common Component Architecture (CCA) Motivation: CQoS Approach: CCA Overview: • QCSAP Challenges: How, during runtime, can we make the best choices • Overall: Develop infrastructure for dynamic component adaptivity, i.e., • The CCA Forum provides a specification and software tools for the for reliability, accuracy, and performance of interoperable quantum composing, substituting, and reconfiguring running CCA component development of high-performance components. chemistry components based on NWChem, MPQC, and GAMESS? applications in response to changing conditions – Performance, accuracy, mathematical consistency, reliability, etc. • Components = Composition – When several QC components provide the same functionality, what • Approach: Develop CQoS tools for – A component is a unit -
Quantum Chemistry on a Grid
Quantum Chemistry on a Grid Lucas Visscher Vrije Universiteit Amsterdam 1 Outline Why Quantum Chemistry needs to be gridified Computational Demands Other requirements What may be expected ? Program descriptions Dirac • Organization • Distributed development and testing Dalton Amsterdam Density Functional (ADF) An ADF-Dalton-Dirac grid driver Design criteria Current status Quantum Chemistry on the Grid, 08-11-2005 L. Visscher, Vrije Universiteit Amsterdam 2 Computational (Quantum) Chemistry QC is traditionally run on supercomputers Large fraction of the total supercomputer time annually allocated Characteristics Compute Time scales cubically or higher with number of atoms treated. Typical : a few hours to several days on a supercomputer Memory Usage scales quadratically or higher with number of atoms. Typical : A few hundred MB till tens of GB Data Storage variable. Typical: tens of Mb till hundreds of GB. Communication • Often serial (one-processor) runs in production work • Parallelization needs sufficient band width. Typical lower limit 100 Mb/s. Quantum Chemistry on the Grid, 08-11-2005 L. Visscher, Vrije Universiteit Amsterdam 3 Changing paradigms in quantum chemistry Computable molecules are often already “large” enough Many degrees of freedom Search for global minimum energy is less important Statistical sampling of different conformations is desired Let the nuclei move, follow molecules that evolve in time 2 steps : (Quantum) dynamics on PES 1 step : Classical dynamics with forces computed via QM Changing characteristics General: more similar calculations Compute time : Increases : more calculations Memory usage : Stays constant : typical size remains the same Data storage : Decreases : intermediate results are less important Communication: Decreases : only start-up data and final result Quantum Chemistry on the Grid, 08-11-2005 L.