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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. -
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 .................................. -
Python Tools in Computational Chemistry (And Biology)
Python Tools in Computational Chemistry (and Biology) Andrew Dalke Dalke Scientific, AB Göteborg, Sweden EuroSciPy, 26-27 July, 2008 “Why does ‘import numpy’ take 0.4 seconds? Does it need to import 228 libraries?” - My first Numpy-discussion post (paraphrased) Your use case isn't so typical and so suffers on the import time end of the balance. - Response from Robert Kern (Others did complain. Import time down to 0.28s.) 52,000 structures PDB doubles every 2½ years HEADER PHOTORECEPTOR 23-MAY-90 1BRD 1BRD 2 COMPND BACTERIORHODOPSIN 1BRD 3 SOURCE (HALOBACTERIUM $HALOBIUM) 1BRD 4 EXPDTA ELECTRON DIFFRACTION 1BRD 5 AUTHOR R.HENDERSON,J.M.BALDWIN,T.A.CESKA,F.ZEMLIN,E.BECKMANN, 1BRD 6 AUTHOR 2 K.H.DOWNING 1BRD 7 REVDAT 3 15-JAN-93 1BRDB 1 SEQRES 1BRDB 1 REVDAT 2 15-JUL-91 1BRDA 1 REMARK 1BRDA 1 .. ATOM 54 N PRO 8 20.397 -15.569 -13.739 1.00 20.00 1BRD 136 ATOM 55 CA PRO 8 21.592 -15.444 -12.900 1.00 20.00 1BRD 137 ATOM 56 C PRO 8 21.359 -15.206 -11.424 1.00 20.00 1BRD 138 ATOM 57 O PRO 8 21.904 -15.930 -10.563 1.00 20.00 1BRD 139 ATOM 58 CB PRO 8 22.367 -14.319 -13.591 1.00 20.00 1BRD 140 ATOM 59 CG PRO 8 22.089 -14.564 -15.053 1.00 20.00 1BRD 141 ATOM 60 CD PRO 8 20.647 -15.054 -15.103 1.00 20.00 1BRD 142 ATOM 61 N GLU 9 20.562 -14.211 -11.095 1.00 20.00 1BRD 143 ATOM 62 CA GLU 9 20.192 -13.808 -9.737 1.00 20.00 1BRD 144 ATOM 63 C GLU 9 19.567 -14.935 -8.932 1.00 20.00 1BRD 145 ATOM 64 O GLU 9 19.815 -15.104 -7.724 1.00 20.00 1BRD 146 ATOM 65 CB GLU 9 19.248 -12.591 -9.820 1.00 99.00 1 1BRD 147 ATOM 66 CG GLU 9 19.902 -11.351 -10.387 1.00 99.00 1 1BRD 148 ATOM 67 CD GLU 9 19.243 -10.169 -10.980 1.00 99.00 1 1BRD 149 ATOM 68 OE1 GLU 9 18.323 -10.191 -11.782 1.00 99.00 1 1BRD 150 ATOM 69 OE2 GLU 9 19.760 -9.089 -10.597 1.00 99.00 1 1BRD 151 ATOM 70 N TRP 10 18.764 -15.737 -9.597 1.00 20.00 1BRD 152 ATOM 71 CA TRP 10 18.034 -16.884 -9.090 1.00 20.00 1BRD 153 ATOM 72 C TRP 10 18.843 -17.908 -8.318 1.00 20.00 1BRD 154 ATOM 73 O TRP 10 18.376 -18.310 -7.230 1.00 20.00 1BRD 155 . -
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. -
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. -
Virt&L-Comm.3.2012.1
Virt&l-Comm.3.2012.1 A MODERN APPROACH TO AB INITIO COMPUTING IN CHEMISTRY, MOLECULAR AND MATERIALS SCIENCE AND TECHNOLOGIES ANTONIO LAGANA’, DEPARTMENT OF CHEMISTRY, UNIVERSITY OF PERUGIA, PERUGIA (IT)* ABSTRACT In this document we examine the present situation of Ab initio computing in Chemistry and Molecular and Materials Science and Technologies applications. To this end we give a short survey of the most popular quantum chemistry and quantum (as well as classical and semiclassical) molecular dynamics programs and packages. We then examine the need to move to higher complexity multiscale computational applications and the related need to adopt for them on the platform side cloud and grid computing. On this ground we examine also the need for reorganizing. The design of a possible roadmap to establishing a Chemistry Virtual Research Community is then sketched and some examples of Chemistry and Molecular and Materials Science and Technologies prototype applications exploiting the synergy between competences and distributed platforms are illustrated for these applications the middleware and work habits into cooperative schemes and virtual research communities (part of the first draft of this paper has been incorporated in the white paper issued by the Computational Chemistry Division of EUCHEMS in August 2012) INTRODUCTION The computational chemistry (CC) community is made of individuals (academics, affiliated to research institutions and operators of chemistry related companies) carrying out computational research in Chemistry, Molecular and Materials Science and Technology (CMMST). It is to a large extent registered into the CC division (DCC) of the European Chemistry and Molecular Science (EUCHEMS) Society and is connected to other chemistry related organizations operating in Chemical Engineering, Biochemistry, Chemometrics, Omics-sciences, Medicinal chemistry, Forensic chemistry, Food chemistry, etc. -
Open Source Molecular Modeling
Accepted Manuscript Title: Open Source Molecular Modeling Author: Somayeh Pirhadi Jocelyn Sunseri David Ryan Koes PII: S1093-3263(16)30118-8 DOI: http://dx.doi.org/doi:10.1016/j.jmgm.2016.07.008 Reference: JMG 6730 To appear in: Journal of Molecular Graphics and Modelling Received date: 4-5-2016 Accepted date: 25-7-2016 Please cite this article as: Somayeh Pirhadi, Jocelyn Sunseri, David Ryan Koes, Open Source Molecular Modeling, <![CDATA[Journal of Molecular Graphics and Modelling]]> (2016), http://dx.doi.org/10.1016/j.jmgm.2016.07.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Open Source Molecular Modeling Somayeh Pirhadia, Jocelyn Sunseria, David Ryan Koesa,∗ aDepartment of Computational and Systems Biology, University of Pittsburgh Abstract The success of molecular modeling and computational chemistry efforts are, by definition, de- pendent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. 1. Introduction What is Open Source? Free and open source software (FOSS) is software that is both considered \free software," as defined by the Free Software Foundation (http://fsf.org) and \open source," as defined by the Open Source Initiative (http://opensource.org). -
A General Quantum Mechanical Method to Predict Positron Spectroscopy
Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 9-13-2007 A General Quantum Mechanical Method to Predict Positron Spectroscopy Paul E. Adamson Follow this and additional works at: https://scholar.afit.edu/etd Part of the Quantum Physics Commons Recommended Citation Adamson, Paul E., "A General Quantum Mechanical Method to Predict Positron Spectroscopy" (2007). Theses and Dissertations. 2898. https://scholar.afit.edu/etd/2898 This Dissertation is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. A GENERAL QUANTUM MECHANICAL METHOD TO PREDICT POSITRON SPECTROSCOPY DISSERTATION Paul E. Adamson, Captain, USAF AFIT/DS/ENP/07-04 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this dissertation are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. AFIT/DS/ENP/07-04 A GENERAL QUANTUM MECHANICAL METHOD TO PREDICT POSITRON SPECTROSCOPY DISSERTATION Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Paul E. Adamson, B.S.Chem.E., M.S.Env.E. Captain, USAF June 2007 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. -
Nanoinformatics: Emerging Databases and Available Tools
Int. J. Mol. Sci. 2014, 15, 7158-7182; doi:10.3390/ijms15057158 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Review Nanoinformatics: Emerging Databases and Available Tools Suresh Panneerselvam and Sangdun Choi * Department of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +82-31-219-2600; Fax: +82-31-219-1615. Received: 17 February 2014; in revised form: 28 March 2014 / Accepted: 9 April 2014 / Published: 25 April 2014 Abstract: Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research. Keywords: biomedical informatics; databases; molecular simulations; nanoinformatics; nanomedicine; nanotechnology; QSAR; text mining 1. Introduction Nanotechnology enables the design and assembly of components with dimensions ranging from 1 to 100 nanometers (nm) [1]. The word nano, which is derived from the Greek word for dwarf, has been part of the scientific nomenclature since 1960 [2]. -
Toward Quantum-Chemical Method Development for Arbitrary Basis Functions Michael F
THE JOURNAL OF CHEMICAL PHYSICS 149, 084106 (2018) Toward quantum-chemical method development for arbitrary basis functions Michael F. Herbst,1,a) Andreas Dreuw,1,b) and James Emil Avery2,c) 1Interdisciplinary Center for Scientific Computing, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany 2Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 København, Denmark (Received 15 June 2018; accepted 2 August 2018; published online 27 August 2018) Wepresent the design of a flexible quantum-chemical method development framework, which supports employing any type of basis function. This design has been implemented in the light-weight program package molsturm, yielding a basis-function-independent self-consistent field scheme. Versatile interfaces, making use of open standards like python, mediate the integration of molsturm with existing third-party packages. In this way, both rapid extension of the present set of meth- ods for electronic structure calculations as well as adding new basis function types can be readily achieved. This makes molsturm well-suitable for testing novel approaches for discretising the electronic wave function and allows comparing them to existing methods using the same software stack. This is illustrated by two examples, an implementation of coupled-cluster doubles as well as a gradient-free geometry optimisation, where in both cases, arbitrary basis functions could be used. molsturm is open-sourced and can be obtained from http://molsturm.org. Published by AIP Publishing. https://doi.org/10.1063/1.5044765 I. INTRODUCTION computation per integral for having fewer more accurate basis functions. The central goal of electronic-structure theory is to find In many practical applications, the shortcomings of GTOs approximate solutions to the electronic wave equation numer- are not important, or one is able to compensate by employing ically. -
WHAT INFLUENCE WOULD a CLOUD BASED SEMANTIC LABORATORY NOTEBOOK HAVE on the DIGITISATION and MANAGEMENT of SCIENTIFIC RESEARCH? by Samantha Kanza
UNIVERSITY OF SOUTHAMPTON Faculty of Physical Sciences and Engineering School of Electronics and Computer Science What Influence would a Cloud Based Semantic Laboratory Notebook have on the Digitisation and Management of Scientific Research? by Samantha Kanza Thesis for the degree of Doctor of Philosophy 25th April 2018 UNIVERSITY OF SOUTHAMPTON ABSTRACT FACULTY OF PHYSICAL SCIENCES AND ENGINEERING SCHOOL OF ELECTRONICS AND COMPUTER SCIENCE Doctor of Philosophy WHAT INFLUENCE WOULD A CLOUD BASED SEMANTIC LABORATORY NOTEBOOK HAVE ON THE DIGITISATION AND MANAGEMENT OF SCIENTIFIC RESEARCH? by Samantha Kanza Electronic laboratory notebooks (ELNs) have been studied by the chemistry research community over the last two decades as a step towards a paper-free laboratory; sim- ilar work has also taken place in other laboratory science domains. However, despite the many available ELN platforms, their uptake in both the academic and commercial worlds remains limited. This thesis describes an investigation into the current ELN landscape, and its relationship with the requirements of laboratory scientists. Market and literature research was conducted around available ELN offerings to characterise their commonly incorporated features. Previous studies of laboratory scientists examined note-taking and record-keeping behaviours in laboratory environments; to complement and extend this, a series of user studies were conducted as part of this thesis, drawing upon the techniques of user-centred design, ethnography, and collaboration with domain experts. These user studies, combined with the characterisation of existing ELN features, in- formed the requirements and design of a proposed ELN environment which aims to bridge the gap between scientists' current practice using paper lab notebooks, and the necessity of publishing their results electronically, at any stage of the experiment life cycle. -
Simulation of Electronic Structure Hamiltonians Using Quantum Computers
Simulation of Electronic Structure Hamiltonians Using Quantum Computers The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Whitfield, James D., Jacob Biamonte, and Alán Aspuru-Guzik. 2011. Simulation of electronic structure Hamiltonians using quantum computers. Molecular Physics 109(5): 735-750. Published Version doi:10.1080/00268976.2011.552441 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:8403540 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP December 21, 2010 1:24 arXiv:1001.3855 h2.arxiv2 arXiv:1001.3855 1 Simulation of Electronic Structure Hamiltonians Using Quantum Computers James D. Whitfielda, Jacob Biamontea;b, and Al´anAspuru-Guzika aHarvard University, Department of Chemistry and Chemical Biology, 12 Oxford St., Cambridge, MA, 02138, USA; bOxford University Computing Laboratory, Oxford OX1 3QD, UK () Over the last century, a large number of physical and mathematical developments paired with rapidly advancing technology have allowed the field of quantum chemistry to ad- vance dramatically. However, the lack of computationally efficient methods for the exact simulation of quantum systems on classical computers presents a limitation of current computational approaches. We report, in detail, how a set of pre-computed molecular integrals can be used to explicitly create a quantum circuit, i.e. a sequence of elementary quantum operations, that, when run on a quantum computer, to obtain the energy of a molecular system with fixed nuclear geometry using the quantum phase estimation algorithm.