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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. -
Density Functional Theory
Density Functional Approach Francesco Sottile Ecole Polytechnique, Palaiseau - France European Theoretical Spectroscopy Facility (ETSF) 22 October 2010 Density Functional Theory 1. Any observable of a quantum system can be obtained from the density of the system alone. < O >= O[n] Hohenberg, P. and W. Kohn, 1964, Phys. Rev. 136, B864 Density Functional Theory 1. Any observable of a quantum system can be obtained from the density of the system alone. < O >= O[n] 2. The density of an interacting-particles system can be calculated as the density of an auxiliary system of non-interacting particles. Hohenberg, P. and W. Kohn, 1964, Phys. Rev. 136, B864 Kohn, W. and L. Sham, 1965, Phys. Rev. 140, A1133 Density Functional ... Why ? Basic ideas of DFT Importance of the density Example: atom of Nitrogen (7 electron) 1. Any observable of a quantum Ψ(r1; ::; r7) 21 coordinates system can be obtained from 10 entries/coordinate ) 1021 entries the density of the system alone. 8 bytes/entry ) 8 · 1021 bytes 4:7 × 109 bytes/DVD ) 2 × 1012 DVDs 2. The density of an interacting-particles system can be calculated as the density of an auxiliary system of non-interacting particles. Density Functional ... Why ? Density Functional ... Why ? Density Functional ... Why ? Basic ideas of DFT Importance of the density Example: atom of Oxygen (8 electron) 1. Any (ground-state) observable Ψ(r1; ::; r8) 24 coordinates of a quantum system can be 24 obtained from the density of the 10 entries/coordinate ) 10 entries 8 bytes/entry ) 8 · 1024 bytes system alone. 5 · 109 bytes/DVD ) 1015 DVDs 2. -
Robust Determination of the Chemical Potential in the Pole
Robust determination of the chemical potential in the pole expansion and selected inversion method for solving Kohn-Sham density functional theory Weile Jia, and Lin Lin Citation: The Journal of Chemical Physics 147, 144107 (2017); doi: 10.1063/1.5000255 View online: http://dx.doi.org/10.1063/1.5000255 View Table of Contents: http://aip.scitation.org/toc/jcp/147/14 Published by the American Institute of Physics THE JOURNAL OF CHEMICAL PHYSICS 147, 144107 (2017) Robust determination of the chemical potential in the pole expansion and selected inversion method for solving Kohn-Sham density functional theory Weile Jia1,a) and Lin Lin1,2,b) 1Department of Mathematics, University of California, Berkeley, California 94720, USA 2Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA (Received 14 August 2017; accepted 24 September 2017; published online 11 October 2017) Fermi operator expansion (FOE) methods are powerful alternatives to diagonalization type methods for solving Kohn-Sham density functional theory (KSDFT). One example is the pole expansion and selected inversion (PEXSI) method, which approximates the Fermi operator by rational matrix functions and reduces the computational complexity to at most quadratic scaling for solving KSDFT. Unlike diagonalization type methods, the chemical potential often cannot be directly read off from the result of a single step of evaluation of the Fermi operator. Hence multiple evaluations are needed to be sequentially performed to compute the chemical potential to ensure the correct number of electrons within a given tolerance. This hinders the performance of FOE methods in practice. In this paper, we develop an efficient and robust strategy to determine the chemical potential in the context of the PEXSI method. -
Introducing ONETEP: Linear-Scaling Density Functional Simulations on Parallel Computers Chris-Kriton Skylaris,A) Peter D
THE JOURNAL OF CHEMICAL PHYSICS 122, 084119 ͑2005͒ Introducing ONETEP: Linear-scaling density functional simulations on parallel computers Chris-Kriton Skylaris,a) Peter D. Haynes, Arash A. Mostofi, and Mike C. Payne Theory of Condensed Matter, Cavendish Laboratory, Madingley Road, Cambridge CB3 0HE, United Kingdom ͑Received 29 September 2004; accepted 4 November 2004; published online 23 February 2005͒ We present ONETEP ͑order-N electronic total energy package͒, a density functional program for parallel computers whose computational cost scales linearly with the number of atoms and the number of processors. ONETEP is based on our reformulation of the plane wave pseudopotential method which exploits the electronic localization that is inherent in systems with a nonvanishing band gap. We summarize the theoretical developments that enable the direct optimization of strictly localized quantities expressed in terms of a delocalized plane wave basis. These same localized quantities lead us to a physical way of dividing the computational effort among many processors to allow calculations to be performed efficiently on parallel supercomputers. We show with examples that ONETEP achieves excellent speedups with increasing numbers of processors and confirm that the time taken by ONETEP as a function of increasing number of atoms for a given number of processors is indeed linear. What distinguishes our approach is that the localization is achieved in a controlled and mathematically consistent manner so that ONETEP obtains the same accuracy as conventional cubic-scaling plane wave approaches and offers fast and stable convergence. We expect that calculations with ONETEP have the potential to provide quantitative theoretical predictions for problems involving thousands of atoms such as those often encountered in nanoscience and biophysics. -
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. -
Natural Bond Orbital Analysis in the ONETEP Code: Applications to Large Protein Systems Louis P
WWW.C-CHEM.ORG FULL PAPER Natural Bond Orbital Analysis in the ONETEP Code: Applications to Large Protein Systems Louis P. Lee,*[a] Daniel J. Cole,[a] Mike C. Payne,[a] and Chris-Kriton Skylaris[b] First principles electronic structure calculations are typically Generalized Wannier Functions of ONETEP to natural atomic performed in terms of molecular orbitals (or bands), providing a orbitals, NBO analysis can be performed within a localized straightforward theoretical avenue for approximations of region in such a way that ensures the results are identical to an increasing sophistication, but do not usually provide any analysis on the full system. We demonstrate the capabilities of qualitative chemical information about the system. We can this approach by performing illustrative studies of large derive such information via post-processing using natural bond proteins—namely, investigating changes in charge transfer orbital (NBO) analysis, which produces a chemical picture of between the heme group of myoglobin and its ligands with bonding in terms of localized Lewis-type bond and lone pair increasing system size and between a protein and its explicit orbitals that we can use to understand molecular structure and solvent, estimating the contribution of electronic delocalization interactions. We present NBO analysis of large-scale calculations to the stabilization of hydrogen bonds in the binding pocket of with the ONETEP linear-scaling density functional theory package, a drug-receptor complex, and observing, in situ, the n ! p* which we have interfaced with the NBO 5 analysis program. In hyperconjugative interactions between carbonyl groups that ONETEP calculations involving thousands of atoms, one is typically stabilize protein backbones. -
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 .................................. -
Camcasp 5.9 Alston J. Misquitta† and Anthony J
CamCASP 5.9 Alston J. Misquittay and Anthony J. Stoneyy yDepartment of Physics and Astronomy, Queen Mary, University of London, 327 Mile End Road, London E1 4NS yy University Chemical Laboratory, Lensfield Road, Cambridge CB2 1EW September 1, 2015 Abstract CamCASP is a suite of programs designed to calculate molecular properties (multipoles and frequency- dependent polarizabilities) in single-site and distributed form, and interaction energies between pairs of molecules, and thence to construct atom–atom potentials. The CamCASP distribution also includes the programs Pfit, Casimir,Gdma 2.2, Cluster, and Process. Copyright c 2007–2014 Alston J. Misquitta and Anthony J. Stone Contents 1 Introduction 1 1.1 Authors . .1 1.2 Citations . .1 2 What’s new? 2 3 Outline of the capabilities of CamCASP and other programs 5 3.1 CamCASP limits . .7 4 Installation 7 4.1 Building CamCASP from source . .9 5 Using CamCASP 10 5.1 Workflows . 10 5.2 High-level scripts . 10 5.3 The runcamcasp.py script . 11 5.4 Low-level scripts . 13 6 Data conventions 13 7 CLUSTER: Detailed specification 14 7.1 Prologue . 15 7.2 Molecule definitions . 15 7.3 Geometry manipulations and other transformations . 16 7.4 Job specification . 18 7.5 Energy . 23 7.6 Crystal . 25 7.7 ORIENT ............................................... 26 7.8 Finally, . 29 8 Examples 29 8.1 A SAPT(DFT) calculation . 29 8.2 An example properties calculation . 30 8.3 Dispersion coefficients . 34 8.4 Using CLUSTER to obtain the dimer geometry . 36 9 CamCASP program specification 39 9.1 Global data .