GW Calculations for Molecules and Solids: Theory and Implementation in CP2K

Total Page:16

File Type:pdf, Size:1020Kb

GW Calculations for Molecules and Solids: Theory and Implementation in CP2K GW calculations for molecules and solids: Theory and implementation in CP2K Jan Wilhelm [email protected] 13 July 2017 Jan Wilhelm GW calculations for molecules and solids 13 July 2017 1 / 41 Overview 1 Theory and practical G0W0 scheme 2 Physics of the GW approximation 3 Benchmarks and applications of G0W0 4 Canonical G0W0: Implementation in CP2K and input 5 Periodic G0W0 calculations: Correction scheme and input 6 Cubic-scaling G0W0: Formalism, implementation and input 7 Summary Jan Wilhelm GW calculations for molecules and solids 13 July 2017 2 / 41 1 Theory and practical G0W0 scheme 2 Physics of the GW approximation 3 Benchmarks and applications of G0W0 4 Canonical G0W0: Implementation in CP2K and input 5 Periodic G0W0 calculations: Correction scheme and input 6 Cubic-scaling G0W0: Formalism, implementation and input 7 Summary Jan Wilhelm GW calculations for molecules and solids 13 July 2017 3 / 41 Quasiparticle energies in GW : Theory Definition: A quasiparticle energy "n is defined as energy which is needed to remove an electron from the system to the vacuum or Energy is gained if one places an electron from the vacuum to the system In DFT and Hartree-Fock, there is no theoretical foundation that "vac =0 the eigenvalues "n from an SCF, "LUMO+1 2 ! r + vel-core(r) + vH(r) + vxc(r) n(r) = "n n(r) − 2 "LUMO have anything to do with quasiparticle energies. Theorem: A self-energy Σ(r; r0;" ) (non-local, energy-dependent) containing "HOMO exchange and correlation effects exists, such that the solution of 2 ! Z "HOMO-1 −∇ 0 0 0 + vel-core(r) + vH(r) n(r) + dr Σ(r; r ;"n) n(r ) = "n n(r) 2 Single-electron (quasiparticle) levels of gives the correct quasiparticle energies "n of the interacting a closed shell molecule many-electron system. In the GW approximation, the self-energy reads 1 Z GW 0 i 0 0 0 0 0 Σ (r; r ;") = d" G(r; r ;" " ; "n ; n ) W (r; r ;" ; "n ; n ) 2π − f g f g f g f g −∞ Jan Wilhelm GW calculations for molecules and solids 13 July 2017 4 / 41 Quasiparticle energies in GW : G0W0 formalism in practice DFT G0W0: Start from DFT MOs n (r) and compute first-order correction to DFT eigenvalues: 1 Converge DFT SCF (e.g. PBE functional for solids "G0W0@PBE", PBE0 for molecules) 2 DFT DFT DFT r + vel-core(r) + vHartree(r) + vxc(r) (r) = " (r) : − 2 n n n 2 Compute density response (most expensive step): occ virt "DFT "DFT 0 X X DFT 0 DFT 0 DFT DFT i a 4 χ(r; r ; i!) = 2 a (r ) i (r ) i (r) a (r) − : ( (N )) !2 + "DFT "DFT2 O i a i − a 3 Compute dielectric function with v(r; r0)=1= r r0 jZ− j (r; r0; i!) = δ(r; r0) dr00v(r; r00)χ(r00; r0; i!) : ( (N3)) − O 4 Compute screened Coulomb interaction Z W (r; r0; i!) = dr00−1(r; r00; i!)v(r00; r0) : ( (N3)) 0 O 5 Compute the self-energy 1 all Z d!0 X DFT(r0) DFT(r) Σ( ; 0; !) = ( ; 0; ! !0) ( ; 0; !0) ; ( ; 0; !) = m m : ( 3) r r i G0 r r i i W0 r r i G0 r r i DFT ( N ) − 2π − i! + "F "m O −∞ m − 6 Compute G0W0 quasiparticle energies (replace wrong XC from DFT by better XC from GW ) "G0W0 = "DFT + DFT Re Σ("G0W0 ) v xc DFT ( (N3)) n n h n j n − j n i O Jan Wilhelm GW calculations for molecules and solids 13 July 2017 5 / 41 Historical sketch of GW 1965: Proposition of the GW method Lars Hedin: New Method for Calculating the One-Particle Green’s Function with Application to the Electron-Gas Problem, Phys. Rev. 139, A796 (1965), ∼ 3700 citations 1986: First G0W0@LDA calculation for diamond, Si, Ge, and LiCl M. S. Hybertsen and S. G. Louie: Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies, Phys. Rev. B 34, 5390 (1986), ∼ 2700 citations 2005 – now: GW for solids in publicly available plane-waves codes Abinit: X. Gonze et al., Z. Kristallogr. 220, 558 – 562 (2005) VASP: M. Shishkin and G. Kresse, Phys. Rev. B 74, 035101 (2006) Yambo: A. Marini, C. Hogan, M. Grüning, D. Varsano, Comput. Phys. Commun. 180, 1392 – 1403 (2009) BerkeleyGW: J. Deslippe et al., Comput. Phys. Commun. 183, 1269 – 1289 (2012) GPAW: F. Hüser, T. Olsen, and K. S. Thygesen, Phys. Rev. B 87, 235132 (2013) WEST: M. Govoni and G. Galli, J. Chem. Theory Comput. 11, 2680 –2696 (2015) 2011 – now: GW with localized basis in publicly available codes FHI-aims: X. Ren et al., New J. Phys. 14, 053020 (2012) Turbomole: M. van Setten, F. Weigend, and F. Evers, J. Chem. Theory Comput. 9, 232 – 246 (2012) molgw: F. Bruneval et al., Comput. Phys. Commun. 208, 149 – 161 (2016) CP2K: J. Wilhelm, M. Del Ben, and J. Hutter, J. Chem. Theory Comput. 12, 3623 – 3635 (2016) Recent trend: Numerically converged results and agreement between codes J. Klimeš, M. Kaltak, and G. Kresse: Predictive GW calculations using plane waves and pseudopotentials, Phys. Rev. B 90, 075125 (2014) M. van Setten et al.: GW 100: Benchmarking G0W0 for Molecular Systems, JCTC 11, 5665 – 5687 (2015) Jan Wilhelm GW calculations for molecules and solids 13 July 2017 6 / 41 1 Theory and practical G0W0 scheme 2 Physics of the GW approximation 3 Benchmarks and applications of G0W0 4 Canonical G0W0: Implementation in CP2K and input 5 Periodic G0W0 calculations: Correction scheme and input 6 Cubic-scaling G0W0: Formalism, implementation and input 7 Summary Jan Wilhelm GW calculations for molecules and solids 13 July 2017 7 / 41 Hedin’s equations Hedin’s equation: Complicated self-consistent equations which give the exact self-energy. Notation: (1) = (r1; t1), G0: non-interacting Green’s function, e.g. from DFT Z Self-energy: Σ(1; 2) = i d(34)G(1; 3)Γ(3; 2; 4)W (4; 1+) Z Green’s function: G(1; 2) = G0(1; 2) + d(34)G0(1; 3)Σ(3; 4)G(4; 2) Z Screened interaction: W (1; 2) = V (1; 2) + d(34)V (1; 3)P(3; 4)W (4; 2) Bare interaction: V (1; 2) = δ(t t )= r r 1 − 2 j 1 − 2j Z Polarization: P(1; 2) = i d(34)G(1; 3)G(4; 1+)Γ(3; 4; 2) − Z @Σ(1; 2) Vertex function: Γ(1; 2; 3) = δ(1; 2)δ(1; 3) + d(4567) G(4; 6)G(7; 5)Γ(6; 7; 3) @G(4; 5) It can be shown that Σ(r ; t ; r ; t )=Σ(r ; r ; t t ). After a Fourier transform of Σ from 1 1 2 2 1 2 2 − 1 time t t t to frequency (= energy), the self-energy Σ(r; r0;!) can be used to compute ≡ 2 − 1 the quasiparticle levels "n using 2 ! Z 0 0 0 −∇ + vel-core(r) + vH(r) n(r) + dr Σ(r; r ;"n) n(r ) = "n n(r) 2 Jan Wilhelm GW calculations for molecules and solids 13 July 2017 8 / 41 Hedin’s equations: Hartree-Fock Hartree-Fock is GV : Z Self-energy: Σ(1; 2) = i d(34)G(1; 3)Γ(3; 2; 4)W (4; 1+)= G(1; 2)V (2; 1) Z Green’s function: G(1; 2) = G0(1; 2) + d(34)G0(1; 3)Σ(3; 4)G(4; 2) Z Screened interaction: W (1; 2) = V (1; 2) + d(34)V (1; 3)P(X3;X4)X!0W (4; 2)= V (1; 2) Bare interaction: V (1; 2) = δ(t t )= r r 1 − 2 j 1 − 2j ((h hZ hh (( hh (((+ Polarization: P(1; 2) = i d(34)(G((1; (h3)Gh(4h; 1h)Γ(3; 4; 2)= 0 (− ((( hhhh h ((h Z hhh ((( @Σ(h1h; 2)(h((( Vertex function: Γ(1; 2; 3) = δ(1; 2)δ(1; 3) + d(4567)((( G(h4;h6)Gh(7h; 5)Γ(6; 7; 3) (((( @G(4; 5) hhhh Jan Wilhelm GW calculations for molecules and solids 13 July 2017 9 / 41 Hedin’s equations: GW Z Self-energy: Σ(1; 2) = i d(34)G(1; 3)Γ(3; 2; 4)W (4; 1+)= iG(1; 2)W (2; 1+) Z Green’s function: G(1; 2) = G0(1; 2) + d(34)G0(1; 3)Σ(3; 4)G(4; 2) Z Screened interaction: W (1; 2) = V (1; 2) + d(34)V (1; 3)P(3; 4)W (4; 2) Bare interaction: V (1; 2) = δ(t t )= r r 1 − 2 j 1 − 2j Z X Polarization: P(1; 2) = i d(34)G(1; 3)G(4; 1+)Γ(3X; 4X; 2)= G(1; 2)G(2; 1+) − X hh (((h Z hh@Σ( ; ) ((( h1h2 (hh(( Vertex function: Γ(1; 2; 3) = δ(1; 2)δ(1; 3) + d(4567)((( G(4;h6)Gh(7h; 5)Γ(6; 7; 3) (((( @G(4; 5) hhhh Jan Wilhelm GW calculations for molecules and solids 13 July 2017 10 / 41 Screening In GW , the screened Coulomb interaction is appearing: Z W (r; r0;!) = dr00−1(r; r00;!)v(r00; r0) where v(r; r0) = 1= r r0 . j − j Compare to screened Coulomb potential with local and static [(r; r0;!) = δ(r r0)] r − dielectric function using the dielectric constant r (in SI units): 1 1 W (r; r0) = 4π r r0 0 r j − j screening charge which has been added to the system internal mobile charge carriers (e.g.
Recommended publications
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • Computer-Assisted Catalyst Development Via Automated Modelling of Conformationally Complex Molecules
    www.nature.com/scientificreports OPEN Computer‑assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Sibo Lin1*, Jenna C. Fromer2, Yagnaseni Ghosh1, Brian Hanna1, Mohamed Elanany3 & Wei Xu4 Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring signifcant researcher time to implement. We automate this workfow using all open‑source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious efects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save signifcant experimental efort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving signifcant time by ruling out candidates with non‑trivial synthetic routes and poor expected catalytic performance. Quantum mechanical methods with high energy accuracy, such as density functional theory (DFT), can opti- mize molecular input structures to a nearby local minimum, but calculating accurate reaction thermodynamics requires fnding global minimum energy structures1,2. For simple molecules, expert intuition can identify a few minima to focus study on, but an alternative approach must be considered for more complex molecules or to eventually fulfl the dream of autonomous catalyst design 3,4: the potential energy surface must be frst surveyed with a computationally efcient method; then minima from this survey must be refned using slower, more accurate methods; fnally, for molecules possessing low-frequency vibrational modes, those modes need to be treated appropriately to obtain accurate thermodynamic energies 5–7.
    [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]
  • TURBOMOLE: Modular Program Suite for Ab Initio Quantum-Chemical and Condensed- Matter Simulations
    TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed- matter simulations Cite as: J. Chem. Phys. 152, 184107 (2020); https://doi.org/10.1063/5.0004635 Submitted: 12 February 2020 . Accepted: 07 April 2020 . Published Online: 13 May 2020 Sree Ganesh Balasubramani, Guo P. Chen, Sonia Coriani, Michael Diedenhofen, Marius S. Frank, Yannick J. Franzke, Filipp Furche, Robin Grotjahn, Michael E. Harding, Christof Hättig, Arnim Hellweg, Benjamin Helmich-Paris, Christof Holzer, Uwe Huniar, Martin Kaupp, Alireza Marefat Khah, Sarah Karbalaei Khani, Thomas Müller, Fabian Mack, Brian D. Nguyen, Shane M. Parker, Eva Perlt, Dmitrij Rappoport, Kevin Reiter, Saswata Roy, Matthias Rückert, Gunnar Schmitz, Marek Sierka, Enrico Tapavicza, David P. Tew, Christoph van Wüllen, Vamsee K. Voora, Florian Weigend, Artur Wodyński, and Jason M. Yu COLLECTIONS Paper published as part of the special topic on Electronic Structure SoftwareESS2020 ARTICLES YOU MAY BE INTERESTED IN PSI4 1.4: Open-source software for high-throughput quantum chemistry The Journal of Chemical Physics 152, 184108 (2020); https://doi.org/10.1063/5.0006002 NWChem: Past, present, and future The Journal of Chemical Physics 152, 184102 (2020); https://doi.org/10.1063/5.0004997 CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations The Journal of Chemical Physics 152, 194103 (2020); https://doi.org/10.1063/5.0007045 J. Chem. Phys. 152, 184107 (2020); https://doi.org/10.1063/5.0004635 152, 184107 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations Cite as: J.
    [Show full text]
  • A Summary of ERCAP Survey of the Users of Top Chemistry Codes
    A survey of codes and algorithms used in NERSC chemical science allocations Lin-Wang Wang NERSC System Architecture Team Lawrence Berkeley National Laboratory We have analyzed the codes and their usages in the NERSC allocations in chemical science category. This is done mainly based on the ERCAP NERSC allocation data. While the MPP hours are based on actually ERCAP award for each account, the time spent on each code within an account is estimated based on the user’s estimated partition (if the user provided such estimation), or based on an equal partition among the codes within an account (if the user did not provide the partition estimation). Everything is based on 2007 allocation, before the computer time of Franklin machine is allocated. Besides the ERCAP data analysis, we have also conducted a direct user survey via email for a few most heavily used codes. We have received responses from 10 users. The user survey not only provide us with the code usage for MPP hours, more importantly, it provides us with information on how the users use their codes, e.g., on which machine, on how many processors, and how long are their simulations? We have the following observations based on our analysis. (1) There are 48 accounts under chemistry category. This is only second to the material science category. The total MPP allocation for these 48 accounts is 7.7 million hours. This is about 12% of the 66.7 MPP hours annually available for the whole NERSC facility (not accounting Franklin). The allocation is very tight. The majority of the accounts are only awarded less than half of what they requested for.
    [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]
  • Porting the DFT Code CASTEP to Gpgpus
    Porting the DFT code CASTEP to GPGPUs Toni Collis [email protected] EPCC, University of Edinburgh CASTEP and GPGPUs Outline • Why are we interested in CASTEP and Density Functional Theory codes. • Brief introduction to CASTEP underlying computational problems. • The OpenACC implementation http://www.nu-fuse.com CASTEP: a DFT code • CASTEP is a commercial and academic software package • Capable of Density Functional Theory (DFT) and plane wave basis set calculations. • Calculates the structure and motions of materials by the use of electronic structure (atom positions are dictated by their electrons). • Modern CASTEP is a re-write of the original serial code, developed by Universities of York, Durham, St. Andrews, Cambridge and Rutherford Labs http://www.nu-fuse.com CASTEP: a DFT code • DFT/ab initio software packages are one of the largest users of HECToR (UK national supercomputing service, based at University of Edinburgh). • Codes such as CASTEP, VASP and CP2K. All involve solving a Hamiltonian to explain the electronic structure. • DFT codes are becoming more complex and with more functionality. http://www.nu-fuse.com HECToR • UK National HPC Service • Currently 30- cabinet Cray XE6 system – 90,112 cores • Each node has – 2×16-core AMD Opterons (2.3GHz Interlagos) – 32 GB memory • Peak of over 800 TF and 90 TB of memory http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) Ab initio codes (VASP, CP2K, CASTEP, ONETEP, NWChem, Quantum Espresso, GAMESS-US, SIESTA, GAMESS-UK, MOLPRO) GS2NEMO ChemShell 2%2% SENGA2% 3% UM Others 4% 34% MITgcm 4% CASTEP 4% GROMACS 6% DL_POLY CP2K VASP 5% 8% 19% http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) 35% of the Chemistry software on HECToR is using DFT methods.
    [Show full text]
  • A Tutorial for Theodore 2.0.2
    A Tutorial for TheoDORE 2.0.2 Felix Plasser, Patrick Kimber Loughborough, 2019 Department of Chemistry – Loughborough University Contents 1 Before Starting3 1.1 Introduction . .3 1.2 Notation . .3 1.3 Installation . .3 2 Natural transition orbitals (Turbomole)4 2.1 Input generation . .4 2.2 Transition density matrix (1TDM) analysis . .5 2.3 Plotting of the orbitals . .6 3 Charge transfer number and exciton analysis (Turbomole)9 3.1 Input generation . .9 3.2 Transition density matrix (1TDM) analysis . 10 3.3 Electron-hole correlation plots . 11 4 Interface to the external cclib library (Gaussian 09) 12 4.1 Check the log file . 12 4.2 Input generation . 13 4.3 Transition density matrix (1TDM) analysis . 15 5 Advanced fragment input and double excitations (Columbus) 16 5.1 Fragment preparation using Avogadro . 17 5.2 Input generation . 18 5.3 Transition density matrix (1TDM) analysis . 19 6 Fragment decomposition for a transition metal complex 21 6.1 Input generation . 21 6.2 Transition density matrix analysis and decomposition . 23 7 Domain NTO and conditional density analysis 26 7.1 Input generation . 26 1 7.2 Transition density matrix analysis . 28 7.3 Plotting of the orbitals . 29 8 Attachment/detachment analysis (Molcas - natural orbitals) 31 8.1 Input generation . 31 8.2 State density matrix analysis . 32 8.3 Plotting of the orbitals . 33 9 Contact 34 TheoDORE tutorial 2 1 Before Starting 1.1 Introduction This tutorial is intended to provide an overview over the functionalities of the TheoDORE program package. Various tasks of different complexity are discussed using interfaces to dif- ferent quantum chemistry packages.
    [Show full text]
  • Density Functional Theory for Transition Metals and Transition Metal Chemistry
    REVIEW ARTICLE www.rsc.org/pccp | Physical Chemistry Chemical Physics Density functional theory for transition metals and transition metal chemistry Christopher J. Cramer* and Donald G. Truhlar* Received 8th April 2009, Accepted 20th August 2009 First published as an Advance Article on the web 21st October 2009 DOI: 10.1039/b907148b We introduce density functional theory and review recent progress in its application to transition metal chemistry. Topics covered include local, meta, hybrid, hybrid meta, and range-separated functionals, band theory, software, validation tests, and applications to spin states, magnetic exchange coupling, spectra, structure, reactivity, and catalysis, including molecules, clusters, nanoparticles, surfaces, and solids. 1. Introduction chemical systems, in part because its cost scales more favorably with system size than does the cost of correlated WFT, and yet Density functional theory (DFT) describes the electronic states it competes well in accuracy except for very small systems. This of atoms, molecules, and materials in terms of the three- is true even in organic chemistry, but the advantages of DFT dimensional electronic density of the system, which is a great are still greater for metals, especially transition metals. The simplification over wave function theory (WFT), which involves reason for this added advantage is static electron correlation. a3N-dimensional antisymmetric wave function for a system It is now well appreciated that quantitatively accurate 1 with N electrons. Although DFT is sometimes considered the electronic structure calculations must include electron correlation. B ‘‘new kid on the block’’, it is now 45 years old in its modern It is convenient to recognize two types of electron correlation, 2 formulation (more than half as old as quantum mechanics the first called dynamical electron correlation and the second 3,4 itself), and it has roots that are almost as ancient as the called static correlation, near-degeneracy correlation, or non- Schro¨ dinger equation.
    [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]