A Tutorial for Theodore 2.0.2
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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. -
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. -
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. -
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Modern Quantum Chemistry with [Open]Molcas
Modern quantum chemistry with [Open]Molcas Cite as: J. Chem. Phys. 152, 214117 (2020); https://doi.org/10.1063/5.0004835 Submitted: 17 February 2020 . Accepted: 11 May 2020 . Published Online: 05 June 2020 Francesco Aquilante , Jochen Autschbach , Alberto Baiardi , Stefano Battaglia , Veniamin A. Borin , Liviu F. Chibotaru , Irene Conti , Luca De Vico , Mickaël Delcey , Ignacio Fdez. Galván , Nicolas Ferré , Leon Freitag , Marco Garavelli , Xuejun Gong , Stefan Knecht , Ernst D. Larsson , Roland Lindh , Marcus Lundberg , Per Åke Malmqvist , Artur Nenov , Jesper Norell , Michael Odelius , Massimo Olivucci , Thomas B. Pedersen , Laura Pedraza-González , Quan M. Phung , Kristine Pierloot , Markus Reiher , Igor Schapiro , Javier Segarra-Martí , Francesco Segatta , Luis Seijo , Saumik Sen , Dumitru-Claudiu Sergentu , Christopher J. Stein , Liviu Ungur , Morgane Vacher , Alessio Valentini , and Valera Veryazov J. Chem. Phys. 152, 214117 (2020); https://doi.org/10.1063/5.0004835 152, 214117 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp Modern quantum chemistry with [Open]Molcas Cite as: J. Chem. Phys. 152, 214117 (2020); doi: 10.1063/5.0004835 Submitted: 17 February 2020 • Accepted: 11 May 2020 • Published Online: 5 June 2020 Francesco Aquilante,1,a) Jochen Autschbach,2,b) Alberto Baiardi,3,c) Stefano Battaglia,4,d) Veniamin A. Borin,5,e) Liviu F. Chibotaru,6,f) Irene Conti,7,g) Luca De Vico,8,h) Mickaël Delcey,9,i) Ignacio Fdez. Galván,4,j) Nicolas Ferré,10,k) Leon Freitag,3,l) Marco Garavelli,7,m) Xuejun Gong,11,n) Stefan Knecht,3,o) Ernst D. Larsson,12,p) Roland Lindh,4,q) Marcus Lundberg,9,r) Per Åke Malmqvist,12,s) Artur Nenov,7,t) Jesper Norell,13,u) Michael Odelius,13,v) Massimo Olivucci,8,14,w) Thomas B. -
![Arxiv:1911.06836V1 [Physics.Chem-Ph] 8 Aug 2019 A](https://docslib.b-cdn.net/cover/0105/arxiv-1911-06836v1-physics-chem-ph-8-aug-2019-a-950105.webp)
Arxiv:1911.06836V1 [Physics.Chem-Ph] 8 Aug 2019 A
Multireference electron correlation methods: Journeys along potential energy surfaces Jae Woo Park,1, ∗ Rachael Al-Saadon,2 Matthew K. MacLeod,3 Toru Shiozaki,2, 4 and Bess Vlaisavljevich5, y 1Department of Chemistry, Chungbuk National University, Chungdae-ro 1, Cheongju 28644, Korea. 2Department of Chemistry, Northwestern University, 2145 Sheridan Rd., Evanston, IL 60208, USA. 3Workday, 4900 Pearl Circle East, Suite 100, Boulder, CO 80301, USA. 4Quantum Simulation Technologies, Inc., 625 Massachusetts Ave., Cambridge, MA 02139, USA. 5Department of Chemistry, University of South Dakota, 414 E. Clark Street, Vermillion, SD 57069, USA. (Dated: November 19, 2019) Multireference electron correlation methods describe static and dynamical electron correlation in a balanced way, and therefore, can yield accurate and predictive results even when single-reference methods or multicon- figurational self-consistent field (MCSCF) theory fails. One of their most prominent applications in quantum chemistry is the exploration of potential energy surfaces (PES). This includes the optimization of molecular ge- ometries, such as equilibrium geometries and conical intersections, and on-the-fly photodynamics simulations; both depend heavily on the ability of the method to properly explore the PES. Since such applications require the nuclear gradients and derivative couplings, the availability of analytical nuclear gradients greatly improves the utility of quantum chemical methods. This review focuses on the developments and advances made in the past two decades. To motivate the readers, we first summarize the notable applications of multireference elec- tron correlation methods to mainstream chemistry, including geometry optimizations and on-the-fly dynamics. Subsequently, we review the analytical nuclear gradient and derivative coupling theories for these methods, and the software infrastructure that allows one to make use of these quantities in applications. -
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. -
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. -
$ GW $100: a Plane Wave Perspective for Small Molecules
GW 100: a plane wave perspective for small molecules Emanuele Maggio,1 Peitao Liu,1, 2 Michiel J. van Setten,3 and Georg Kresse1, ∗ 1University of Vienna, Faculty of Physics and Center for Computational Materials Science, Sensengasse 8/12, A-1090 Vienna, Austria 2Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China 3Nanoscopic Physics, Institute of Condensed Matter and Nanosciences, Universit´eCatholique de Louvain, 1348 Louvain-la-Neuve, Belgium (Dated: November 28, 2016) In a recent work, van Setten and coworkers have presented a carefully converged G0W0 study of 100 closed shell molecules [J. Chem. Theory Comput. 11, 5665 (2015)]. For two different codes they found excellent agreement to within few 10 meV if identical Gaussian basis sets were used. We inspect the same set of molecules using the projector augmented wave method and the Vienna ab initio simulation package (VASP). For the ionization potential, the basis set extrapolated plane wave results agree very well with the Gaussian basis sets, often reaching better than 50 meV agreement. In order to achieve this agreement, we correct for finite basis set errors as well as errors introduced by periodically repeated images. For electron affinities below the vacuum level differences between Gaussian basis sets and VASP are slightly larger. We attribute this to larger basis set extrapolation errors for the Gaussian basis sets. For quasi particle (QP) resonances above the vacuum level, differences between VASP and Gaussian basis sets are, however, found to be substantial. This is tentatively explained by insufficient basis set convergence of the Gaussian type orbital calculations as exemplified for selected test cases. -
Natural Transition Orbital Calculations in the RASSI Module of Openmolcas Introduction
1 Natural Transition Orbital Calculations in the RASSI module of OpenMolcas Introduction This document is a manual for the natural transition orbital (NTO) calculation that is done in the RASSI module of OpenMolcas. It includes discussion of the theory of NTOs, an explanation of the code, and several input and output examples of the NTO calculations with the RASSI module. Prior to adding the NTO calculation to the RASSI module, OpenMolcas already contained code for NTO calculations (and other analysis tools) in the WFA module https://molcas.gitlab.io/OpenMolcas/sphinx/users.guide/programs/wfa.html but the new implementation extends the capability in two key ways: 1. An external package, in particular TheoDORE,1 is required for visualizing the NTOs calculated in the WHA module; however, NTOs calculated in the RASSI module can be visualized with luscus, https://sourceforge.net/projects/luscus/ which is a more commonly used external package for OpenMolcas users than TheoDORE. The new implementation prints out the NTO files in the same format as how other orbital files, such as ScfOrb file and RasOrb file, are printed. So the output file can also be used for three-dimensional graphics with the GRID_IT module. 2. WHA cannot calculate NTOs for states with different symmetries, but the new implementation in RASSI can do this, as shown in Section 4 for this document. To distinguish the NTO calculations in two modules, those performed in the RASSI module will be called RASSI-NTO, or simply NTO, and those performed in the WFA module will be called WFA-NTO. The comparison between RASSI-NTO and WFA-NTO is only made in Section 4, so all discussions in other sections of this module refer to the implementation in the RASSI module. -
1 Advances in Electronic Structure Theory: Gamess a Decade Later Mark S. Gordon and Michael W. Schmidt Department of Chemistry A
ADVANCES IN ELECTRONIC STRUCTURE THEORY: GAMESS A DECADE LATER MARK S. GORDON AND MICHAEL W. SCHMIDT DEPARTMENT OF CHEMISTRY AND AMES LABORATORY, IOWA STATE UNIVERSITY, AMES, IA 50011 Abstract. Recent developments in advanced quantum chemistry and quantum chemistry interfaced with model potentials are discussed, with the primary focus on new implementations in the GAMESS electronic structure suite of programs. Applications to solvent effects and surface science are discussed. 1. Introduction The past decade has seen an extraordinary growth in novel new electronic structure methods and creative implementations of these methods. Concurrently, there have been important advances in “middleware”, software that enables the implementation of efficient electronic structure algorithms. Combined with continuing improvements in computer and interconnect hardware, these advances have extended both the accuracy of computations and the sizes of molecular systems to which such methods may be applied. The great majority of the new computational chemistry algorithms have found their way into one or more broadly distributed electronic structure packages. Since this work focuses on new features of the GAMESS (General Atomic and Molecular Electronic Structure System1) suite of codes, it is important at the outset to recognize the many other packages that offer both similar and complementary features. These include ACES2 CADPAC3, DALTON4, GAMESS-UK5, HYPERCHEM6, JAGUAR7, MOLCAS8 MOLPRO9, NWChem10, PQS11, PSI312, Q-Chem13, SPARTAN14, TURBOMOLE15, and UT-Chem16. Some1,3,4,10 of these packages are distributed at no cost to all, or to academic users, while others are commercial packages, but all are generally available to users without restriction or constraint. Indeed, the developers of these codes frequently collaborate to share features, a practice that clearly benefits all of their users. -
Computational Chemistry: a Practical Guide for Applying Techniques to Real-World Problems
Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems. David C. Young Copyright ( 2001 John Wiley & Sons, Inc. ISBNs: 0-471-33368-9 (Hardback); 0-471-22065-5 (Electronic) COMPUTATIONAL CHEMISTRY COMPUTATIONAL CHEMISTRY A Practical Guide for Applying Techniques to Real-World Problems David C. Young Cytoclonal Pharmaceutics Inc. A JOHN WILEY & SONS, INC., PUBLICATION New York . Chichester . Weinheim . Brisbane . Singapore . Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ( 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008, E-Mail: PERMREQ @ WILEY.COM. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional person should be sought. -
1 Synthetic and Analytical Details
Electronic Supplementary Material (ESI) for Chemical Science. This journal is © The Royal Society of Chemistry 2018 1 Synthetic and analytical details 1.1 Materials and synthetic methods All experiments were carried out using Schlenk and glove-box (argon atmosphere) techniques. All solvents were dried by passing through columns packed with activated alumina unless otherwise mentioned. Deuterated solvents (Euriso-Top GmbH) and tBuNH2 (Sigma Aldrich), were dried over Na/K (d6-benzene and d8-THF) or CaH2 (1,8- Diazabicyclo[5.4.0]undec-7-ene (DBU)), respectively, distilled by trap-to-trap transfer in vacuo and degassed by three freeze-pump-thaw cycles. DMF (Sigma Aldrich) was dried by storage over molecular sieves (4 Å). Aluminum oxide (Brockmann I, basic) was heated in vacuo for 3 d to 200 °C prior to use. AgCF3CO2 (ABCR) and bis(cyclopentadienyl)cobalt(II) (ABCR) were used as purchased. 6 and thianthrenium [1,2] 15 tetrafluoroborate were prepared according to published procedure. tBu NH2 was prepared using a modified method of Bergman and coworkers (see below).[3] 1.2 Analytical methods Elemental analyses were obtained from the Analytical laboratories at the Georg- August University on a Elementar Vario EL 3. NMR spectra were recorded on Bruker Avance III 300 or 400 MHz spectrometers and were calibrated to the residual solvent proton resonance (d6-benzene: H = 7.16 ppm, C = 128.06 ppm; d8-THF: H = 3.58 31 ppm, C = 67.2 ppm). P chemical shifts are reported relative to external phosphoric acid. Signal multiplicities are abbreviated as: s (singlet), d (dublet), t (triplet), q (quartet), m (multiplet) and br (broad).