Core Software Blocks in Quantum Chemistry: Tensors and Integrals Workshop Program
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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 ................................................. -
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 -
Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published
Best Practices: Application Profiling at the HPCAC High Performance Center Pak Lui 157 Applications Best Practices Published • Abaqus • COSMO • HPCC • Nekbone • RFD tNavigator • ABySS • CP2K • HPCG • NEMO • SNAP • AcuSolve • CPMD • HYCOM • NWChem • SPECFEM3D • Amber • Dacapo • ICON • Octopus • STAR-CCM+ • AMG • Desmond • Lattice QCD • OpenAtom • STAR-CD • AMR • DL-POLY • LAMMPS • OpenFOAM • VASP • ANSYS CFX • Eclipse • LS-DYNA • OpenMX • WRF • ANSYS Fluent • FLOW-3D • miniFE • OptiStruct • ANSYS Mechanical• GADGET-2 • MILC • PAM-CRASH / VPS • BQCD • Graph500 • MSC Nastran • PARATEC • BSMBench • GROMACS • MR Bayes • Pretty Fast Analysis • CAM-SE • Himeno • MM5 • PFLOTRAN • CCSM 4.0 • HIT3D • MPQC • Quantum ESPRESSO • CESM • HOOMD-blue • NAMD • RADIOSS For more information, visit: http://www.hpcadvisorycouncil.com/best_practices.php 2 35 Applications Installation Best Practices Published • Adaptive Mesh Refinement (AMR) • ESI PAM-CRASH / VPS 2013.1 • NEMO • Amber (for GPU/CUDA) • GADGET-2 • NWChem • Amber (for CPU) • GROMACS 5.1.2 • Octopus • ANSYS Fluent 15.0.7 • GROMACS 4.5.4 • OpenFOAM • ANSYS Fluent 17.1 • GROMACS 5.0.4 (GPU/CUDA) • OpenMX • BQCD • Himeno • PyFR • CASTEP 16.1 • HOOMD Blue • Quantum ESPRESSO 4.1.2 • CESM • LAMMPS • Quantum ESPRESSO 5.1.1 • CP2K • LAMMPS-KOKKOS • Quantum ESPRESSO 5.3.0 • CPMD • LS-DYNA • WRF 3.2.1 • DL-POLY 4 • MrBayes • WRF 3.8 • ESI PAM-CRASH 2015.1 • NAMD For more information, visit: http://www.hpcadvisorycouncil.com/subgroups_hpc_works.php 3 HPC Advisory Council HPC Center HPE Apollo 6000 HPE ProLiant -
High-Performance Algorithms and Software for Large-Scale Molecular Simulation
HIGH-PERFORMANCE ALGORITHMS AND SOFTWARE FOR LARGE-SCALE MOLECULAR SIMULATION A Thesis Presented to The Academic Faculty by Xing Liu In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computational Science and Engineering Georgia Institute of Technology May 2015 Copyright ⃝c 2015 by Xing Liu HIGH-PERFORMANCE ALGORITHMS AND SOFTWARE FOR LARGE-SCALE MOLECULAR SIMULATION Approved by: Professor Edmond Chow, Professor Richard Vuduc Committee Chair School of Computational Science and School of Computational Science and Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Edmond Chow, Advisor Professor C. David Sherrill School of Computational Science and School of Chemistry and Biochemistry Engineering Georgia Institute of Technology Georgia Institute of Technology Professor David A. Bader Professor Jeffrey Skolnick School of Computational Science and Center for the Study of Systems Biology Engineering Georgia Institute of Technology Georgia Institute of Technology Date Approved: 10 December 2014 To my wife, Ying Huang the woman of my life. iii ACKNOWLEDGEMENTS I would like to first extend my deepest gratitude to my advisor, Dr. Edmond Chow, for his expertise, valuable time and unwavering support throughout my PhD study. I would also like to sincerely thank Dr. David A. Bader for recruiting me into Georgia Tech and inviting me to join in this interesting research area. My appreciation is extended to my committee members, Dr. Richard Vuduc, Dr. C. David Sherrill and Dr. Jeffrey Skolnick, for their advice and helpful discussions during my research. Similarly, I want to thank all of the faculty and staff in the School of Compu- tational Science and Engineering at Georgia Tech. -
Pyscf: the Python-Based Simulations of Chemistry Framework
PySCF: The Python-based Simulations of Chemistry Framework Qiming Sun∗1, Timothy C. Berkelbach2, Nick S. Blunt3,4, George H. Booth5, Sheng Guo1,6, Zhendong Li1, Junzi Liu7, James D. McClain1,6, Elvira R. Sayfutyarova1,6, Sandeep Sharma8, Sebastian Wouters9, and Garnet Kin-Lic Chany1 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena CA 91125, USA 2Department of Chemistry and James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA 3Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA 4Department of Chemistry, University of California, Berkeley, California 94720, USA 5Department of Physics, King's College London, Strand, London WC2R 2LS, United Kingdom 6Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA 7Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, P. R. China 8Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO 80302, USA 9Brantsandpatents, Pauline van Pottelsberghelaan 24, 9051 Sint-Denijs-Westrem, Belgium arXiv:1701.08223v2 [physics.chem-ph] 2 Mar 2017 Abstract PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible ∗[email protected] [email protected] 1 computational workflows. The package provides a wide range of tools to support simulations of finite-size systems, extended systems with periodic boundary conditions, low-dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines. -
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. -
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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. -
Massive-Parallel Implementation of the Resolution-Of-Identity Coupled
Article Cite This: J. Chem. Theory Comput. 2019, 15, 4721−4734 pubs.acs.org/JCTC Massive-Parallel Implementation of the Resolution-of-Identity Coupled-Cluster Approaches in the Numeric Atom-Centered Orbital Framework for Molecular Systems † § † † ‡ § § Tonghao Shen, , Zhenyu Zhu, Igor Ying Zhang,*, , , and Matthias Scheffler † Department of Chemistry, Fudan University, Shanghai 200433, China ‡ Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Science, Fudan University, Shanghai 200433, China § Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany *S Supporting Information ABSTRACT: We present a massive-parallel implementation of the resolution of identity (RI) coupled-cluster approach that includes single, double, and perturbatively triple excitations, namely, RI-CCSD(T), in the FHI-aims package for molecular systems. A domain-based distributed-memory algorithm in the MPI/OpenMP hybrid framework has been designed to effectively utilize the memory bandwidth and significantly minimize the interconnect communication, particularly for the tensor contraction in the evaluation of the particle−particle ladder term. Our implementation features a rigorous avoidance of the on- the-fly disk storage and excellent strong scaling of up to 10 000 and more cores. Taking a set of molecules with different sizes, we demonstrate that the parallel performance of our CCSD(T) code is competitive with the CC implementations in state-of- the-art high-performance-computing computational chemistry packages. We also demonstrate that the numerical error due to the use of RI approximation in our RI-CCSD(T) method is negligibly small. Together with the correlation-consistent numeric atom-centered orbital (NAO) basis sets, NAO-VCC-nZ, the method is applied to produce accurate theoretical reference data for 22 bio-oriented weak interactions (S22), 11 conformational energies of gaseous cysteine conformers (CYCONF), and 32 Downloaded via FRITZ HABER INST DER MPI on January 8, 2021 at 22:13:06 (UTC). -
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. -
Optimizing the Use of Open-Source Software Applications in Drug
DDT • Volume 11, Number 3/4 • February 2006 REVIEWS TICS INFORMA Optimizing the use of open-source • software applications in drug discovery Reviews Werner J. Geldenhuys1, Kevin E. Gaasch2, Mark Watson2, David D. Allen1 and Cornelis J.Van der Schyf1,3 1Department of Pharmaceutical Sciences, School of Pharmacy,Texas Tech University Health Sciences Center, Amarillo,TX, USA 2West Texas A&M University, Canyon,TX, USA 3Pharmaceutical Chemistry, School of Pharmacy, North-West University, Potchefstroom, South Africa Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs. To bring a new drug to the market is very costly, with the current of combinatorial approaches and HTS. The addition of computer- price tag approximating US$800 million, according to data reported aided drug design technologies to the R&D approaches of a com- in a recent study [1]. Therefore, it is not surprising that pharma- pany, could lead to a reduction in the cost of drug design and ceutical companies are seeking ways to optimize costs associated development by up to 50% [6,7]. -
Openmolcas: from Source Code to Insight Ignacio Fdez
Article Cite This: J. Chem. Theory Comput. XXXX, XXX, XXX−XXX pubs.acs.org/JCTC OpenMolcas: From Source Code to Insight Ignacio Fdez. Galvan,́ 1,2 Morgane Vacher,1 Ali Alavi,3 Celestino Angeli,4 Francesco Aquilante,5 Jochen Autschbach,6 Jie J. Bao,7 Sergey I. Bokarev,8 Nikolay A. Bogdanov,3 Rebecca K. Carlson,7,9 Liviu F. Chibotaru,10 Joel Creutzberg,11,12 Nike Dattani,13 Mickael̈ G. Delcey,1 Sijia S. Dong,7,14 Andreas Dreuw,15 Leon Freitag,16 Luis Manuel Frutos,17 Laura Gagliardi,7 Fredé rić Gendron,6 Angelo Giussani,18,19 Leticia Gonzalez,́ 20 Gilbert Grell,8 Meiyuan Guo,1,21 Chad E. Hoyer,7,22 Marcus Johansson,12 Sebastian Keller,16 Stefan Knecht,16 Goran Kovacevič ,́ 23 Erik Kallman,̈ 1 Giovanni Li Manni,3 Marcus Lundberg,1 Yingjin Ma,16 Sebastian Mai,20 Joaõ Pedro Malhado,24 Per Åke Malmqvist,12 Philipp Marquetand,20 Stefanie A. Mewes,15,25 Jesper Norell,11 Massimo Olivucci,26,27,28 Markus Oppel,20 Quan Manh Phung,29 Kristine Pierloot,29 Felix Plasser,30 Markus Reiher,16 Andrew M. Sand,7,31 Igor Schapiro,32 Prachi Sharma,7 Christopher J. Stein,16,33 Lasse Kragh Sørensen,1,34 Donald G. Truhlar,7 Mihkel Ugandi,1,35 Liviu Ungur,36 Alessio Valentini,37 Steven Vancoillie,12 Valera Veryazov,12 Oskar Weser,3 Tomasz A. Wesołowski,5 Per-Olof Widmark,12 Sebastian Wouters,38 Alexander Zech,5 J. Patrick Zobel,12 and Roland Lindh*,2,39 1Department of Chemistry − Ångström Laboratory, Uppsala University, P.O.