Quantum Chemical Calculations of NMR Parameters
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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. -
Accessing the Accuracy of Density Functional Theory Through Structure
This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. Letter Cite This: J. Phys. Chem. Lett. 2019, 10, 4914−4919 pubs.acs.org/JPCL Accessing the Accuracy of Density Functional Theory through Structure and Dynamics of the Water−Air Interface † # ‡ # § ‡ § ∥ Tatsuhiko Ohto, , Mayank Dodia, , Jianhang Xu, Sho Imoto, Fujie Tang, Frederik Zysk, ∥ ⊥ ∇ ‡ § ‡ Thomas D. Kühne, Yasuteru Shigeta, , Mischa Bonn, Xifan Wu, and Yuki Nagata*, † Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan ‡ Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany § Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, United States ∥ Dynamics of Condensed Matter and Center for Sustainable Systems Design, Chair of Theoretical Chemistry, University of Paderborn, Warburger Strasse 100, 33098 Paderborn, Germany ⊥ Graduate School of Pure and Applied Sciences, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8571, Japan ∇ Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan *S Supporting Information ABSTRACT: Density functional theory-based molecular dynamics simulations are increasingly being used for simulating aqueous interfaces. Nonetheless, the choice of the appropriate density functional, critically affecting the outcome of the simulation, has remained arbitrary. Here, we assess the performance of various exchange−correlation (XC) functionals, based on the metrics relevant to sum-frequency generation spectroscopy. The structure and dynamics of water at the water−air interface are governed by heterogeneous intermolecular interactions, thereby providing a critical benchmark for XC functionals. -
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. -
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 ................................................. -
The Alexandria Library, a Quantum-Chemical Database of Molecular Properties for Force field Development 9 2017 Received: October 1 1 1 Mohammad M
www.nature.com/scientificdata OPEN Data Descriptor: The Alexandria library, a quantum-chemical database of molecular properties for force field development 9 2017 Received: October 1 1 1 Mohammad M. Ghahremanpour , Paul J. van Maaren & David van der Spoel Accepted: 19 February 2018 Published: 10 April 2018 Data quality as well as library size are crucial issues for force field development. In order to predict molecular properties in a large chemical space, the foundation to build force fields on needs to encompass a large variety of chemical compounds. The tabulated molecular physicochemical properties also need to be accurate. Due to the limited transparency in data used for development of existing force fields it is hard to establish data quality and reusability is low. This paper presents the Alexandria library as an open and freely accessible database of optimized molecular geometries, frequencies, electrostatic moments up to the hexadecupole, electrostatic potential, polarizabilities, and thermochemistry, obtained from quantum chemistry calculations for 2704 compounds. Values are tabulated and where available compared to experimental data. This library can assist systematic development and training of empirical force fields for a broad range of molecules. Design Type(s) data integration objective • molecular physical property analysis objective Measurement Type(s) physicochemical characterization Technology Type(s) Computational Chemistry Factor Type(s) Sample Characteristic(s) 1 Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden. Correspondence and requests for materials should be addressed to D.v.d.S. (email: [email protected]). -
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. -
Solid-State NMR Techniques for the Structural Characterization of Cyclic Aggregates Based on Borane–Phosphane Frustrated Lewis Pairs
molecules Review Solid-State NMR Techniques for the Structural Characterization of Cyclic Aggregates Based on Borane–Phosphane Frustrated Lewis Pairs Robert Knitsch 1, Melanie Brinkkötter 1, Thomas Wiegand 2, Gerald Kehr 3 , Gerhard Erker 3, Michael Ryan Hansen 1 and Hellmut Eckert 1,4,* 1 Institut für Physikalische Chemie, WWU Münster, 48149 Münster, Germany; [email protected] (R.K.); [email protected] (M.B.); [email protected] (M.R.H.) 2 Laboratorium für Physikalische Chemie, ETH Zürich, 8093 Zürich, Switzerland; [email protected] 3 Organisch-Chemisches Institut, WWU Münster, 48149 Münster, Germany; [email protected] (G.K.); [email protected] (G.E.) 4 Instituto de Física de Sao Carlos, Universidad de Sao Paulo, Sao Carlos SP 13566-590, Brazil * Correspondence: [email protected] Academic Editor: Mattias Edén Received: 20 February 2020; Accepted: 17 March 2020; Published: 19 March 2020 Abstract: Modern solid-state NMR techniques offer a wide range of opportunities for the structural characterization of frustrated Lewis pairs (FLPs), their aggregates, and the products of cooperative addition reactions at their two Lewis centers. This information is extremely valuable for materials that elude structural characterization by X-ray diffraction because of their nanocrystalline or amorphous character, (pseudo-)polymorphism, or other types of disordering phenomena inherent in the solid state. Aside from simple chemical shift measurements using single-pulse or cross-polarization/magic-angle spinning NMR detection techniques, the availability of advanced multidimensional and double-resonance NMR methods greatly deepened the informational content of these experiments. In particular, methods quantifying the magnetic dipole–dipole interaction strengths and indirect spin–spin interactions prove useful for the measurement of intermolecular association, connectivity, assessment of FLP–ligand distributions, and the stereochemistry of adducts. -
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 . -
The Impact of Density Functional Theory on Materials Research
www.mrs.org/bulletin functional. This functional (i.e., a function whose argument is another function) de- scribes the complex kinetic and energetic interactions of an electron with other elec- Toward Computational trons. Although the form of this functional that would make the reformulation of the many-body Schrödinger equation exact is Materials Design: unknown, approximate functionals have proven highly successful in describing many material properties. Efficient algorithms devised for solving The Impact of the Kohn–Sham equations have been imple- mented in increasingly sophisticated codes, tremendously boosting the application of DFT methods. New doors are opening to in- Density Functional novative research on materials across phys- ics, chemistry, materials science, surface science, and nanotechnology, and extend- ing even to earth sciences and molecular Theory on Materials biology. The impact of this fascinating de- velopment has not been restricted to aca- demia, as DFT techniques also find application in many different areas of in- Research dustrial research. The development is so fast that many current applications could Jürgen Hafner, Christopher Wolverton, and not have been realized three years ago and were hardly dreamed of five years ago. Gerbrand Ceder, Guest Editors The articles collected in this issue of MRS Bulletin present a few of these suc- cess stories. However, even if the compu- Abstract tational tools necessary for performing The development of modern materials science has led to a growing need to complex quantum-mechanical calcula- tions relevant to real materials problems understand the phenomena determining the properties of materials and processes on are now readily available, designing a an atomistic level. -
Practice: Quantum ESPRESSO I
MODULE 2: QUANTUM MECHANICS Practice: Quantum ESPRESSO I. What is Quantum ESPRESSO? 2 DFT software PW-DFT, PP, US-PP, PAW FREE http://www.quantum-espresso.org PW-DFT, PP, PAW FREE http://www.abinit.org DFT PW, PP, Car-Parrinello FREE http://www.cpmd.org DFT PP, US-PP, PAW $3000 [moderate accuracy, fast] http://www.vasp.at DFT full-potential linearized augmented $500 plane-wave (FLAPW) [accurate, slow] http://www.wien2k.at Hartree-Fock, higher order correlated $3000 electron approaches http://www.gaussian.com 3 Quantum ESPRESSO 4 Quantum ESPRESSO Quantum ESPRESSO is an integrated suite of Open- Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Core set of codes, plugins for more advanced tasks and third party packages Open initiative coordinated by the Quantum ESPRESSO Foundation, across Italy. Contributed to by developers across the world Regular hands-on workshops in Trieste, Italy Open-source code: FREE (unlike VASP...) 5 Performance Small jobs (a few atoms) can be run on single node Includes determining convergence parameters, lattice constants Can use OpenMP parallelization on multicore machines Large jobs (~10’s to ~100’s atoms) can run in parallel using MPI to 1000’s of cores Includes molecular dynamics, large geometry relaxation, phonons Parallel performance tied to BLAS/LAPACK (linear algebra routines) and 3D FFT (fast Fourier transform) New GPU-enabled version available 6 Usability Documented online: -
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.