LECTURE 12 : a Survey of Ab Initio Based MD - Finding Approximations - Density Functional Theory - Functionals - Car Parrinello MD
Total Page:16
File Type:pdf, Size:1020Kb

Load more
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. -
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface Chenghan Li and Gregory A. Voth* Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, 60637 Keywords: free energy sampling, proton transport, density functional theory, proton transfer ABSTRACT: The ab initio molecular dynamics (AIMD) and quantum mechanics/molecular mechanics (QM/MM) methods are powerful tools for studying proton solvation, transfer, and transport processes in various environments. However, due to the high computational cost of such methods, achieving sufficient sampling of rare events involving excess proton motion – especially when Grotthuss proton shuttling is involved – usually requires enhanced free energy sampling methods to obtain informative results. Moreover, an appropriate collective variable (CV) that describes the effective position of the net positive charge defect associated with an excess proton is essential for both tracking the trajectory of the defect and for the free energy sampling of the processes associated with the resulting proton transfer and transport. In this work, such a CV is derived from first principles using constrained density functional theory (CDFT). This CV is applicable to a broad array of proton transport and transfer processes as studied via AIMD and QM/MM simulation. 1 INTRODUCTION The accurate and efficient delineation of proton transport (PT) and -
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
processes Review Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development Outi M. H. Salo-Ahen 1,2,* , Ida Alanko 1,2, Rajendra Bhadane 1,2 , Alexandre M. J. J. Bonvin 3,* , Rodrigo Vargas Honorato 3, Shakhawath Hossain 4 , André H. Juffer 5 , Aleksei Kabedev 4, Maija Lahtela-Kakkonen 6, Anders Støttrup Larsen 7, Eveline Lescrinier 8 , Parthiban Marimuthu 1,2 , Muhammad Usman Mirza 8 , Ghulam Mustafa 9, Ariane Nunes-Alves 10,11,* , Tatu Pantsar 6,12, Atefeh Saadabadi 1,2 , Kalaimathy Singaravelu 13 and Michiel Vanmeert 8 1 Pharmaceutical Sciences Laboratory (Pharmacy), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland; ida.alanko@abo.fi (I.A.); rajendra.bhadane@abo.fi (R.B.); parthiban.marimuthu@abo.fi (P.M.); atefeh.saadabadi@abo.fi (A.S.) 2 Structural Bioinformatics Laboratory (Biochemistry), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland 3 Faculty of Science-Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands; [email protected] 4 Swedish Drug Delivery Forum (SDDF), Department of Pharmacy, Uppsala Biomedical Center, Uppsala University, 751 23 Uppsala, Sweden; [email protected] (S.H.); [email protected] (A.K.) 5 Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 7 A, FI-90014 Oulu, Finland; andre.juffer@oulu.fi 6 School of Pharmacy, University of Eastern Finland, FI-70210 Kuopio, Finland; maija.lahtela-kakkonen@uef.fi (M.L.-K.); tatu.pantsar@uef.fi -
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: -
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. -
Molecular Dynamics Study of the Stress–Strain Behavior of Carbon-Nanotube Reinforced Epon 862 Composites R
Materials Science and Engineering A 447 (2007) 51–57 Molecular dynamics study of the stress–strain behavior of carbon-nanotube reinforced Epon 862 composites R. Zhu a,E.Pana,∗, A.K. Roy b a Department of Civil Engineering, University of Akron, Akron, OH 44325, USA b Materials and Manufacturing Directorate, Air Force Research Laboratory, AFRL/MLBC, Wright-Patterson Air Force Base, OH 45433, USA Received 9 March 2006; received in revised form 2 August 2006; accepted 20 October 2006 Abstract Single-walled carbon nanotubes (CNTs) are used to reinforce epoxy Epon 862 matrix. Three periodic systems – a long CNT-reinforced Epon 862 composite, a short CNT-reinforced Epon 862 composite, and the Epon 862 matrix itself – are studied using the molecular dynamics. The stress–strain relations and the elastic Young’s moduli along the longitudinal direction (parallel to CNT) are simulated with the results being also compared to those from the rule-of-mixture. Our results show that, with increasing strain in the longitudinal direction, the Young’s modulus of CNT increases whilst that of the Epon 862 composite or matrix decreases. Furthermore, a long CNT can greatly improve the Young’s modulus of the Epon 862 composite (about 10 times stiffer), which is also consistent with the prediction based on the rule-of-mixture at low strain level. Even a short CNT can also enhance the Young’s modulus of the Epon 862 composite, with an increment of 20% being observed as compared to that of the Epon 862 matrix. © 2006 Elsevier B.V. All rights reserved. Keywords: Carbon nanotube; Epon 862; Nanocomposite; Molecular dynamics; Stress–strain curve 1. -
The Empirical Pseudopotential Method
The empirical pseudopotential method Richard Tran PID: A1070602 1 Background In 1934, Fermi [1] proposed a method to describe high lying atomic states by replacing the complex effects of the core electrons with an effective potential described by the pseudo-wavefunctions of the valence electrons. Shortly after, this pseudopotential (PSP), was realized by Hellmann [2] who implemented it to describe the energy levels of alkali atoms. Despite his initial success, further exploration of the PSP and its application in efficiently solving the Hamiltonian of periodic systems did not reach its zenith until the middle of the twenti- eth century when Herring [3] proposed the orthogonalized plane wave (OPW) method to calculate the wave functions of metals and semiconductors. Using the OPW, Phillips and Kleinman [4] showed that the orthogonality of the va- lence electrons’ wave functions relative to that of the core electrons leads to a cancellation of the attractive and repulsive potentials of the core electrons (with the repulsive potentials being approximated either analytically or empirically), allowing for a rapid convergence of the OPW calculations. Subsequently in the 60s, Marvin Cohen expanded and improved upon the pseudopotential method by using the crystalline energy levels of several semicon- ductor materials [6, 7, 8] to empirically obtain the potentials needed to compute the atomic wave functions, developing what is now known as the empirical pseu- dopotential method (EPM). Although more advanced PSPs such as the ab-initio pseudopotentials exist, the EPM still provides an incredibly accurate method for calculating optical properties and band structures, especially for metals and semiconductors, with a less computationally taxing method. -
FORCE FIELDS for PROTEIN SIMULATIONS by JAY W. PONDER
FORCE FIELDS FOR PROTEIN SIMULATIONS By JAY W. PONDER* AND DAVIDA. CASEt *Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 51. Louis, Missouri 63110, and tDepartment of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037 I. Introduction. ...... .... ... .. ... .... .. .. ........ .. .... .... ........ ........ ..... .... 27 II. Protein Force Fields, 1980 to the Present.............................................. 30 A. The Am.ber Force Fields.............................................................. 30 B. The CHARMM Force Fields ..., ......... 35 C. The OPLS Force Fields............................................................... 38 D. Other Protein Force Fields ....... 39 E. Comparisons Am.ong Protein Force Fields ,... 41 III. Beyond Fixed Atomic Point-Charge Electrostatics.................................... 45 A. Limitations of Fixed Atomic Point-Charges ........ 46 B. Flexible Models for Static Charge Distributions.................................. 48 C. Including Environmental Effects via Polarization................................ 50 D. Consistent Treatment of Electrostatics............................................. 52 E. Current Status of Polarizable Force Fields........................................ 57 IV. Modeling the Solvent Environment .... 62 A. Explicit Water Models ....... 62 B. Continuum Solvent Models.......................................................... 64 C. Molecular Dynamics Simulations with the Generalized Born Model........ -
Force Fields for MD Simulations
Force Fields for MD simulations • Topology/parameter files • Where do the numbers an MD code uses come from? • How to make topology files for ligands, cofactors, special amino acids, … • How to obtain/develop missing parameters. • QM and QM/MM force fields/potential energy descriptions used for molecular simulations. The Potential Energy Function Ubond = oscillations about the equilibrium bond length Uangle = oscillations of 3 atoms about an equilibrium bond angle Udihedral = torsional rotation of 4 atoms about a central bond Unonbond = non-bonded energy terms (electrostatics and Lenard-Jones) Energy Terms Described in the CHARMm Force Field Bond Angle Dihedral Improper Classical Molecular Dynamics r(t +!t) = r(t) + v(t)!t v(t +!t) = v(t) + a(t)!t a(t) = F(t)/ m d F = ! U (r) dr Classical Molecular Dynamics 12 6 &, R ) , R ) # U (r) = . $* min,ij ' - 2* min,ij ' ! 1 qiq j ij * ' * ' U (r) = $ rij rij ! %+ ( + ( " 4!"0 rij Coulomb interaction van der Waals interaction Classical Molecular Dynamics Classical Molecular Dynamics Bond definitions, atom types, atom names, parameters, …. What is a Force Field? In molecular dynamics a molecule is described as a series of charged points (atoms) linked by springs (bonds). To describe the time evolution of bond lengths, bond angles and torsions, also the non-bonding van der Waals and elecrostatic interactions between atoms, one uses a force field. The force field is a collection of equations and associated constants designed to reproduce molecular geometry and selected properties of tested structures. Energy Functions Ubond = oscillations about the equilibrium bond length Uangle = oscillations of 3 atoms about an equilibrium bond angle Udihedral = torsional rotation of 4 atoms about a central bond Unonbond = non-bonded energy terms (electrostatics and Lenard-Jones) Parameter optimization of the CHARMM Force Field Based on the protocol established by Alexander D. -
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. -
The Fip35 WW Domain Folds with Structural and Mechanistic Heterogeneity in Molecular Dynamics Simulations
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Biophysical Journal Volume 96 April 2009 L53–L55 L53 The Fip35 WW Domain Folds with Structural and Mechanistic Heterogeneity in Molecular Dynamics Simulations Daniel L. Ensign and Vijay S. Pande* Department of Chemistry, Stanford University, Stanford, California ABSTRACT We describe molecular dynamics simulations resulting in the folding the Fip35 Hpin1 WW domain. The simulations were run on a distributed set of graphics processors, which are capable of providing up to two orders of magnitude faster compu- tation than conventional processors. Using the Folding@home distributed computing system, we generated thousands of inde- pendent trajectories in an implicit solvent model, totaling over 2.73 ms of simulations. A small number of these trajectories folded; the folding proceeded along several distinct routes and the system folded into two distinct three-stranded b-sheet conformations, showing that the folding mechanism of this system is distinctly heterogeneous. Received for publication 8 September 2008 and in final form 22 January 2009. *Correspondence: [email protected] Because b-sheets are a ubiquitous protein structural motif, tories on the distributed computing environment, Folding@ understanding how they fold is imperative in solving the home (5). For these calculations, we utilized graphics pro- protein folding problem. Liu et al. (1) recently made a heroic cessing units (ATI Technologies; Sunnyvale, CA), deployed set of measurements of the folding kinetics of 35 three- by the Folding@home contributors. Using optimized code, stranded b-sheet sequences derived from the Hpin1 WW individual graphics processing units (GPUs) of the type domain.