Free Energy Methods Involving Quantum Physics, Path Integrals, and Virtual Screenings
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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. -
JRC QSAR Model Database
JRC QSAR Model Database EURL ECVAM DataBase service on ALternative Methods to animal experimentation To promote the development and uptake of alternative and advanced methods in toxicology and biomedical sciences SDF - STRUCTURE DATA FORMAT: How to create from SMILES The European Commission’s science and knowledge service Joint Research Centre Directorate F Health, Consumers & Reference Materials Chemicals Safety & Alternative Methods Unit The European Commission’s science and knowledge service Joint Research Centre EUR 28708 EN This publication is a Tutorial by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide user support. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. Contact information Email: [email protected] JRC Science Hub https://ec.europa.eu/jrc JRC107492 EUR 28708 EN PDF ISBN 978-92-79-71294-4 ISSN 1831-9424 doi:10.2760/952280 Print ISBN 978-92-79-71295-1 ISSN 1018-5593 doi:10.2760/668595 Luxembourg: Publications Office of the European Union, 2017 Ispra: European Commission, 2017 © European Union, 2017 The reuse of the document is authorised, provided the source is acknowledged and the original meaning or message of the texts are not distorted. The European Commission shall not be held liable for any consequences stemming from the reuse. How to cite this document: Triebe -
MACSIMUS Manual
1 MACSIMUS manual benzocaine (ethylaminobenzoate) parameter_set = charmm21 HA | HA-CT-HA | HA-CT-HA | OSn.2 | Cp.7=On.5 | C6R--C6R-HA Most often used links: | | HA-C6R C6R-HA 2.2 Blend synopsis and options | | HA-C6R--C6R-NPn.5^-Hp.25 9.2 Cook synopsis and options | 9.2.5 Cook input data Hp.25 MACromolecule SIMUlation Software © Jiˇr´ıKolafa 1993{2020 MACSIMUS may be distributed under the terms of the GNU General Public Licence Credits: ray: Mark VandeWettering \reasonably intelligent raytracer" CHARMM force field (files: charmm*.par, charmm*/*.rsd) GROMOS force field (files: gromos*.par, gromos*/*.rsd) amoeba implementation by Z. Wagner moil support by J. Schofield several bug fixes by T. Trnka bug discovered by N. Parfenov Contents I Program `blend' version 2.4b 14 1 Introduction 16 1.1 Force fields...................................... 16 1.2 `blend' overview.................................... 16 1.3 Versions........................................ 17 2 Running blend 18 2.1 Environment...................................... 18 2.2 Synopsis........................................ 19 2.2.1 Global options................................. 19 2.2.2 par-options and parameter files....................... 20 2.2.3 mol-options and molecular files....................... 22 2.2.4 Extra-options ................................. 29 2.3 File extensions.................................... 34 2.4 Run-time control................................... 37 2.4.1 get data format for input........................... 37 2.4.2 Scrolling.................................... 38 2.4.3 Error handling................................ 39 2.4.4 Interrupts................................... 40 2.5 Showing molecules graphically............................ 40 2.5.1 X11 Graphics................................. 40 2.5.2 Playback output............................... 44 2.6 Energy minimization................................. 44 2.7 Missing coordinates.................................. 45 3 Force field and the parameter file 46 3.1 Structure of the parameter file........................... -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Ontology-Based Classification of Molecules
Ontology-Based Classification of Molecules: a Logic Programming Approach Despoina Magka Department of Computer Science, University of Oxford Wolfson Building, Parks Road, OX1 3QD, UK [email protected] Abstract. We describe a prototype that performs structure-based classification of molecular structures. The software we present implements a sound and com- plete reasoning procedure of a formalism that extends logic programming and builds upon the DLV deductive databases system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. In terms of performance, a no- ticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language which is suit- able for the Life Sciences domain and exhibits an encouraging balance between expressive power and practical feasibility. Keywords: Knowledge representation and reasoning, Logic programming and answer set programming, Cheminformatics. 1 Introduction The volume of bioinformatics data produced by research laboratories worldwide is in- creasing at an astonishing rate turning the need to adequately catalogue, represent and index the vast amounts of Life Sciences data sources into a pressing challenge. Semantic technologies have achieved significant progress towards the federation of biochemical information [33, 3, 4] via the definition and use of domain vocabularies with formal semantics, also known as ontologies. OWL [15], a family of logic-based knowledge representation (KR) formalisms standardised by W3C, has played a pivotal role in the advent of Semantic technologies due to its significant ability to reason over ontolo- gies by means of logical inference. -
(CDK): an Open-Source Java Library for Chemo- and Bioinformatics
J. Chem. Inf. Comput. Sci. 2003, 43, 493-500 493 The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics Christoph Steinbeck,*,† Yongquan Han,† Stefan Kuhn,† Oliver Horlacher,‡ Edgar Luttmann,§ and Egon Willighagen# Max-Planck-Institute of Chemical Ecology, Jena, Germany, TheraSTrat AG, Allschwil, Switzerland, Institute of Organic Chemistry, University of Paderborn, Germany, and Nijmegen, The Netherlands Received August 17, 2002 The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo- and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given. 1. INTRODUCTION of software development, most widely recognized through the great success of the free Unix-like operating system Whoever pursues the endeavor of creating a larger GNU/Linux, a collaborative work of many individuals and software package in chemoinformatics or computational organizations, including the Free Software Foundation lead chemistry from scratch will soon be confronted with the by Richard Stallman and the Finish computer science student Syssiphus task of implementing the standard repertoire of Linus Torvalds who started the project. According to several chemoinformatical algorithms and components invented essays on this subject, open-source software, for which, by during the last 20 or 30 years. -
Chemical File Format Conversion Tools : a N Overview
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 2, February - 2014 Chemical File Format Conversion Tools : A n Overview Kavitha C. R Dr. T Mahalekshmi Research Scholar, Bharathiyar University Principal Dept of Computer Applications Sree Narayana Institute of Technology SNGIST Kollam, India Cochin, India Abstract— There are a lot of chemical data stored in large different chemical file formats. Three types of file format databases, repositories and other resources. These data are used conversion tools are discussed in section III. And the by different researchers in different applications in various areas conclusion is given in section IV followed by the references. of chemistry. Since these data are stored in several standard chemical file formats, there is a need for the inter-conversion of II. CHEMICAL FILE FORMATS chemical structures between different formats because all the formats are not supported by various software and tools used by A chemical is a collection of atoms bonded together the researchers. Therefore it becomes essential to convert one file in space. The structure of a chemical makes it unique and format to another. This paper reviews some of the chemical file gives it its physical and biological characteristics. This formats and also presents a few inter-conversion tools such as structure is represented in a variety of chemical file formats. Open Babel [1], Mol converter [2] and CncTranslate [3]. These formats are used to represent chemical structure records and its associated data fields. Some of the file Keywords— File format Conversion, Open Babel, mol formats are CML (Chemical Markup Language), SDF converter, CncTranslate, inter- conversion tools. -
A Summary of ERCAP Survey of the Users of Top Chemistry Codes
A survey of codes and algorithms used in NERSC chemical science allocations Lin-Wang Wang NERSC System Architecture Team Lawrence Berkeley National Laboratory We have analyzed the codes and their usages in the NERSC allocations in chemical science category. This is done mainly based on the ERCAP NERSC allocation data. While the MPP hours are based on actually ERCAP award for each account, the time spent on each code within an account is estimated based on the user’s estimated partition (if the user provided such estimation), or based on an equal partition among the codes within an account (if the user did not provide the partition estimation). Everything is based on 2007 allocation, before the computer time of Franklin machine is allocated. Besides the ERCAP data analysis, we have also conducted a direct user survey via email for a few most heavily used codes. We have received responses from 10 users. The user survey not only provide us with the code usage for MPP hours, more importantly, it provides us with information on how the users use their codes, e.g., on which machine, on how many processors, and how long are their simulations? We have the following observations based on our analysis. (1) There are 48 accounts under chemistry category. This is only second to the material science category. The total MPP allocation for these 48 accounts is 7.7 million hours. This is about 12% of the 66.7 MPP hours annually available for the whole NERSC facility (not accounting Franklin). The allocation is very tight. The majority of the accounts are only awarded less than half of what they requested for. -
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.