The Impact of Density Functional Theory on Materials Research

Total Page:16

File Type:pdf, Size:1020Kb

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. The interactions between atoms and electrons are governed by the series of simulations that meaningfully laws of quantum mechanics; hence, accurate and efficient techniques for solving the represent a physical or chemical property basic quantum-mechanical equations for complex many-atom, many-electron systems or model a process (e.g., a chemical reac- must be developed. Density functional theory (DFT) marks a decisive breakthrough in tion or phase transformation in a mate- these efforts, and in the past decade DFT has had a rapidly growing impact not only on rial), remains a challenging task, requiring fundamental but also industrial research. This article discusses the fundamental a judicious choice of approximations and principles of DFT and the highly efficient computational tools that have been developed an expert handling of these tools. for its application to complex problems in materials science. Also highlighted are state-of- the-art applications in many areas of materials research, such as structural materials, Designing DFT Calculations for catalysis and surface science, nanomaterials, and biomaterials and geophysics. Materials Science The successful application of DFT to a Keywords: computation, density functional theory, modeling, nanoscale, simulation. materials problem involves three distinct steps: (1) translation of the engineering problem to a computable atomistic model, (2) computation of the required physico- Introduction chemical properties, and (3) validation of During the past decade, computer sim- equation for the complex many-atom, the simulation results by confrontation ulations based on a quantum-mechanical many-electron system is not analytically with laboratory experiments. description of the interactions between elec- solvable, and numerical approaches have trons and atomic nuclei have had an in- become invaluable for physics, chemistry, Translation creasingly important impact on materials and materials science. A breakthrough in A theory that relates computable quan- science, not only in fundamental under- these computational efforts was realized tities to macroscopic engineering behavior standing but also with a strong emphasis in 1964 when Walter Kohn and co- is a prerequisite in order for DFT calcula- toward materials design for future tech- workers developed the density functional tions to be relevant. Before any computa- nologies. The simulations are performed theory (DFT), a theory based on electron tion can be started, significant effort often with atomistic detail by solving the density, which is a function of only three must be expended to translate an engi- Schrödinger equation to obtain energies spatial coordinates.1 The Kohn–Sham neering problem into questions and chal- and forces, require only the atomic num- equations of DFT cast the intractable com- lenges that can be addressed with DFT. bers of the constituents as input, and plexity of the electron–electron interactions This initial step requires considerable in- should describe the bonding between the into an effective single-particle potential sight into the materials science and engi- atoms with high accuracy. The Schrödinger determined by the exchange-correlation neering aspects of the problem. MRS BULLETIN • VOLUME 31 • SEPTEMBER 2006 659 The Impact of Density Functional Theory on Materials Research The translation step may be illustrated by differ from the computed result because of LDA and GGA are by far the most com- the application of DFT to the design of other factors that are not characterized monly used functionals today. All func- better materials for rechargeable lithium well in the problem translation. However, tionals exist in spin-degenerate and batteries. Some properties, such as the it should be emphasized that one of the spin-polarized versions. Quite generally, electrode potential for a new material, can most important roles of computational GGA calculations represent a systematic be directly related to total energy differ- modeling is to provide information that is improvement, compared with the LDA, ences2 and are therefore easy to compute. not accessible (or accessible only with great although for heavy elements, a certain But another critical property, such as cycle difficulty) by laboratory experiments. tendency of the GGA to overcorrect the life (the loss of capacity after repeated LDA error is observed. In certain cases charge and discharge), is more difficult to Hierarchy of Exchange-Correlation (prominent examples are the structural predict with DFT, because it can be caused Functionals for DFT and magnetic ground state of iron and the by a variety of poorly identified phenom- DFT reformulates the many-body potential-energy landscape for molecular ena, such as reaction of the material with Schrödinger equation in the form of a set dissociation on a metal surface) the differ- the electrolyte to form an insulating layer, of coupled one-electron equations (the ence is not merely a quantitative one: only mechanical degradation with resulting Kohn–Sham equations), where all of the GGA calculations11 predict iron to be bcc loss of electrical contact, and phase trans- many-body interactions are contained in and magnetic, whereas the LDA predicts formations in the material. Each of these the exchange-correlation functional the ground state to be hexagonal and non- factors requires a different computation. magnetic. Only GGA calculations lead to ៬ Exc [n(r)], (1) the correct height and location of the dis- Computation sociation barrier for a diatomic molecule In many cases, solving the Schrödinger where n(r៬) is the electron density distribu- in contact with the surface of a metal.12 equation is not enough. The length and tion in the crystal. (For a modern intro- Climbing the density functional ladder time scales accessible by DFT calculations duction into DFT, see, for example, further to the meta-GGA or hyper-GGA are defined by the maximal system size Dreizler and Gross3 or the recent book by leads to only moderate progress, com- (typically a few hundred atoms; up to a Martin,4 which also provides a thorough pared with the GGA.13 Hybrid functionals few thousand in special cases) and by the introduction to the theory and practice of enjoy enormous popularity in molecular maximum time span that can be covered electronic structure calculations.) However, chemistry, but in materials science they by ab initio molecular dynamics (MD) sim- the exact form of this functional for which are merely at the test stage. Preliminary re- ulations (several tens of picoseconds). A the Kohn–Sham equations would give ex- sults show great promise for insulating strategy of multiscale simulations must be actly the same ground-state answer as the and semiconducting systems, but also used to translate the results of atomistic many-body Schrödinger equation is not show a need to develop new hybrid func- DFT simulations to real-world scales. As known, and approximations must be used. tionals better suited to describing metals.14 an example, the elementary steps in the Perdew5 has referred to the hierarchy of catalytic oxidation of CO on a metallic sur- approximations developed during the last Basis Sets face (adsorption/desorption/diffusion of two decades as the “Jacob’s ladder of DFT.” Practically, the effective one-electron ᭿ CO and O2, surface oxidation, dissociation In the local density approximation Kohn–Sham equations are nonlinear of oxygen, reaction CO + O → CO2, des- (LDA), the local exchange-correlation en- partial differential equations that are itera- orption of CO2, etc.) at fixed chemical ergy density is taken to be the same as in a tively solved by representing the electronic potentials of the reactants may be treated uniform electron gas of the same density. wave functions by a
Recommended publications
  • 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.
    [Show full text]
  • 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:
    [Show full text]
  • The CECAM Electronic Structure Library and the Modular Software Development Paradigm
    The CECAM electronic structure library and the modular software development paradigm Cite as: J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 Submitted: 06 May 2020 . Accepted: 08 June 2020 . Published Online: 13 July 2020 Micael J. T. Oliveira , Nick Papior , Yann Pouillon , Volker Blum , Emilio Artacho , Damien Caliste , Fabiano Corsetti , Stefano de Gironcoli , Alin M. Elena , Alberto García , Víctor M. García-Suárez , Luigi Genovese , William P. Huhn , Georg Huhs , Sebastian Kokott , Emine Küçükbenli , Ask H. Larsen , Alfio Lazzaro , Irina V. Lebedeva , Yingzhou Li , David López- Durán , Pablo López-Tarifa , Martin Lüders , Miguel A. L. Marques , Jan Minar , Stephan Mohr , Arash A. Mostofi , Alan O’Cais , Mike C. Payne, Thomas Ruh, Daniel G. A. Smith , José M. Soler , David A. Strubbe , Nicolas Tancogne-Dejean , Dominic Tildesley, Marc Torrent , and Victor Wen-zhe Yu COLLECTIONS Paper published as part of the special topic on Electronic Structure Software Note: This article is part of the JCP Special Topic on Electronic Structure Software. This paper was selected as Featured ARTICLES YOU MAY BE INTERESTED IN Recent developments in the PySCF program package The Journal of Chemical Physics 153, 024109 (2020); https://doi.org/10.1063/5.0006074 An open-source coding paradigm for electronic structure calculations Scilight 2020, 291101 (2020); https://doi.org/10.1063/10.0001593 Siesta: Recent developments and applications The Journal of Chemical Physics 152, 204108 (2020); https://doi.org/10.1063/5.0005077 J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 153, 024117 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp The CECAM electronic structure library and the modular software development paradigm Cite as: J.
    [Show full text]
  • 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.
    [Show full text]
  • Quantum Chemistry (QC) on Gpus Feb
    Quantum Chemistry (QC) on GPUs Feb. 2, 2017 Overview of Life & Material Accelerated Apps MD: All key codes are GPU-accelerated QC: All key codes are ported or optimizing Great multi-GPU performance Focus on using GPU-accelerated math libraries, OpenACC directives Focus on dense (up to 16) GPU nodes &/or large # of GPU nodes GPU-accelerated and available today: ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS- Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012, LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, OpenMM, PolyFTS, SOP-GPU* & more Quantum Espresso/PWscf, QUICK, TeraChem* Active GPU acceleration projects: CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* = application where >90% of the workload is on GPU 2 MD vs. QC on GPUs “Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp) Simulates positions of atoms over time; Calculates electronic properties; chemical-biological or ground state, excited states, spectral properties, chemical-material behaviors making/breaking bonds, physical properties Forces calculated from simple empirical formulas Forces derived from electron wave function (bond rearrangement generally forbidden) (bond rearrangement OK, e.g., bond energies) Up to millions of atoms Up to a few thousand atoms Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically
    [Show full text]
  • CPMD Tutorial Car–Parrinello Molecular Dynamics
    CPMD Tutorial Car–Parrinello Molecular Dynamics Ari P Seitsonen CNRS & Universit´ePierre et Marie Curie, Paris CSC, October 2006 Introduction CPMD — concept 1 • Car-Parrinello Molecular Dynamics – Roberto Car & Michele Parrinello, Physical Review Letters 55, 2471 (1985) — 20+1 years of Car-Parrinello method! Roberto Car Michele Parrinello CPMD — concept 2 • CPMD code — http://www.cpmd.org/; based on the original code of Roberto Car and Michele Parrinello CPMD What is it, what is it not? • Computer code for performing static, dynamic simulations and analysis of electronic structure • About 200’000 lines of code — FORTRAN77 with a flavour towards F90 • (Freely) available via http://www.cpmd.org/ with source code • Most suitable for dynamical simulations of condensed systems or large molecules — less for small molecules or bulk properties of small crystals • Computationally highly optimised for vector and scalar supercomputers, shared and distributed memory machines and combinations thereof (SMP) Capabilities • Solution of electronic and ionic structure • XC-functional: LDA, GGA, meta-GGA, hybrid • Molecular dynamics in NVE, NVT, NPT ensembles • General constraints (MD and geometry optimisation) • Metadynamics ‡ • Free energy functional ‡ • Path integrals ‡ • QM/MM ‡ • Wannier functions • Response properties: TDDFT, NMR, Raman, IR, . • Norm conserving and ultra-soft pseudo potentials ‡ : Will not be considered during this course CPMD: Examples Amorphous Si CPMD: Examples Amorphous Si CPMD: Examples Liquid water: Solvation and transport of
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • A ABINIT Software, 171 Ab Initio Molecular Dynamics (AIMD
    Index A high-throughput screening, 162–163 ABINIT software, 171 thiophosphates, 158–159 Ab initio molecular dynamics (AIMD) Allen-Cahn equation, 69, 72, 74, 77, 78, 80 techniques Application programming interface (API), 177, advantages, 147–148 194, 198, 201, 203–206 alkali superionic conductors Argon oxygen degasser (AOD), 52 garnet superionic conductors, 160–162 high-throughput screening, 162–163 thiophosphates, 158–159 B BO variants, 148, 149 Band gap, 173, 178, 184, 185, 214–216, 218, CP variants, 148, 149 221 diffusivity, 149, 150 Bardeen-Cooper-Schrieffer (BCS) mechanism, disadvantages, 149 36 dopability, 152–153 Basic oxygen furnace (BOF) process, 51–53, iodide superionic conductors, 154–155 59–61 ionic conductivity, 150 Battery electrodes, 78–79 NVT ensemble, 152 Bi-Fe-O system, 40 probability density function, 151 Birch-Murnaghan (BM4) equation, 31 simulation temperature, 152 Boltzmann transport calculations, 184, 186 SOFCs, 156–158 BoltzTrap code, 184 spin, 152 Born-Oppenheimer (BO) variants, 148, 149, supercell sizes, 151–152 152 time step, 152 Butler-Volmer reaction rate, 79–81 total simulation time, 152 van Hove correlation function, 151 Additive manufacturing C gradient alloys, 33–35 Cahn-Hilliard equation, 69, 72, 74, 77–81, 112, process, 32 113 Ti-6Al-4V, 32–35 CALculation of PHAse Diagram (CALPHAD) Aflowlib, 171, 195 approach ˛AgI, 154, 155 description, 27–28 AIMD techniques. See Ab initio molecular first-principles calculations, 31–32 dynamics (AIMD) techniques Gibbs energy, 28–31, 42 Alkali superionic conductors gradient
    [Show full text]
  • Quantum Chemical Calculations of NMR Parameters
    Quantum Chemical Calculations of NMR Parameters Tatyana Polenova University of Delaware Newark, DE Winter School on Biomolecular NMR January 20-25, 2008 Stowe, Vermont OUTLINE INTRODUCTION Relating NMR parameters to geometric and electronic structure Classical calculations of EFG tensors Molecular properties from quantum chemical calculations Quantum chemistry methods DENSITY FUNCTIONAL THEORY FOR CALCULATIONS OF NMR PARAMETERS Introduction to DFT Software Practical examples Tutorial RELATING NMR OBSERVABLES TO MOLECULAR STRUCTURE NMR Spectrum NMR Parameters Local geometry Chemical structure (reactivity) I. Calculation of experimental NMR parameters Find unique solution to CQ, Q, , , , , II. Theoretical prediction of fine structure constants from molecular geometry Classical electrostatic model (EFG)- only in simple ionic compounds Quantum mechanical calculations (Density Functional Theory) (EFG, CSA) ELECTRIC FIELD GRADIENT (EFG) TENSOR: POINT CHARGE MODEL EFG TENSOR IS DETERMINED BY THE COMBINED ELECTRONIC AND NUCLEAR WAVEFUNCTION, NO ANALYTICAL EXPRESSION IN THE GENERAL CASE THE SIMPLEST APPROXIMATION: CLASSICAL POINT CHARGE MODEL n Zie 4 V2,k = 3 Y2,k ()i,i i=1 di 5 ATOMS CONTRIBUTING TO THE EFG TENSOR ARE TREATED AS POINT CHARGES, THE RESULTING EFG TENSOR IS THE SUM WITH RESPECT TO ALL ATOMS VERY CRUDE MODEL, WORKS QUANTITATIVELY ONLY IN SIMPLEST IONIC SYSTEMS, BUT YIELDS QUALITATIVE TRENDS AND GENERAL UNDERSTANDING OF THE SYMMETRY AND MAGNITUDE OF THE EXPECTED TENSOR ELECTRIC FIELD GRADIENT (EFG) TENSOR: POINT CHARGE MODEL n Zie 4 V2,k = 3 Y2,k ()i,i i=1 di 5 Ze V = ; V = 0; V = 0 2,0 d 3 2,±1 2,±2 2Ze V = ; V = 0; V = 0 2,0 d 3 2,±1 2,±2 3 Ze V = ; V = 0; V = 0 2,0 2 d 3 2,±1 2,±2 V2,0 = 0; V2,±1 = 0; V2,±2 = 0 MOLECULAR PROPERTIES FROM QUANTUM CHEMICAL CALCULATIONS H = E See for example M.
    [Show full text]
  • Siesta Caminando Andrei Postnikov Universite´ Paul Verlaine – Metz June 2009
    Siesta caminando Andrei Postnikov Universite´ Paul Verlaine – Metz June 2009 For whom this reading Vuestra merced came to an idea to try the SIESTA code – but is this a really good idea, and what to begin with? The present document intends to save you some pains of decision making, of trial, and of error. It does not replace basic reading on condensed matter physics, density functional theory, or SIESTA documentation. What SIESTA is and isn’t SIESTA is a calculation method and computer code which solves problems at the level of density functional theory (DFT). These problems are, generally, related to ground-state properties. En- ergy/volume curves, phase diagrams (e.g., magnetic ones), phonons, molecular dynamics are all related to ground-state properties. Electron excitations are not. So if you need to calculate a good excitation spectrum you should better go for a good TDDFT code, or a method which implements the GW approximation. As other DFT codes, SIESTA provides no access to many-electron wavefunction and deals with electron density which is in many aspects good and reasonable, but is a single-determinant density. Hence the effects of Configuration Interaction, accounted for in quantum chemistry meth- ods, are not included, even insofar as ground-state properties are concerned! Extensions beyond “standard” DFT like hybride functionals, LDA+U, self-interaction correction are not included either. In the description of correct equilibrium geometry, even as the latter is a ground-state property, SIESTA has problems (albeit of fundamental character and shared by many other DFT methods) in correctly describing van-der-Waals interactions and hydrogen bonds.
    [Show full text]