Parameterization of the Am1* Semiempirical Molecular Orbital Method for the First-Row Transition Metals and Other Elements
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Semiempirical Implementations of Molecular
Semiempirical Implementations of Molecular Orbital Theory How one can make Hartree-Fock theory less computationally intensive without much sacrificing its accuracy? The most demanding step – calculation of two-electron (four-index) integrals (J and K integrals) appearing in the Fock matrix elements (N4 where N is the number of basis functions). One way to save time – to estimate their value accurately in an a priori fashion and thus to avoid numerical integration. Coulomb integrals measure the repulsion between electrons in regions of space defined by the basis functions. When the basis functions in the integral for one electron are very far from the basis functions for the other, the value of that integral will approach zero. In a large molecule, one might be able to avoid the calculation of a very large number of integrals simply by assuming them to be zero. HF theory is intrinsically inaccurate as it does not include correlation energy. Therefore, modifications of the theory introduced in order to simplify its formalism may actually improve it, provided the new approximations somehow introduce an accounting for correlation energy. Most typically, such approximations involve the adoption of a parametric form for some aspect of the calculation where the parameters involved are chosen so as best reproduce experimental data – ‘semiempirical’. Another motivation for introducing semiempirical approximation into HF theory was to facilitate the computation of derivatives (gradients, Hessians) so that geometries could be more efficiently optimized. Extended Hückel Theory Before considering semiempirical methods we revisit Hückel theory: H11 − ES11 H12 − ES12 L H1N − ES1N H21 − ES21 H22 − ES22 L H2N − ES2N = 0 M M O M H − ES H − ES H − ES N1 N1 N2 N 2 L NN NN The dimension of the secular determinant depends on the choice of the basis set. -
Multiscale Simulation of Laser Ablation and Processing of Semiconductor Materials
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2012 Multiscale Simulation Of Laser Ablation And Processing Of Semiconductor Materials Lalit Shokeen University of Central Florida Part of the Materials Science and Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Shokeen, Lalit, "Multiscale Simulation Of Laser Ablation And Processing Of Semiconductor Materials" (2012). Electronic Theses and Dissertations, 2004-2019. 2420. https://stars.library.ucf.edu/etd/2420 MULTISCALE SIMULATION OF LASER PROCESSING AND ABLATION OF SEMICONDUCTOR MATERIALS by LALIT SHOKEEN Bachelor of Science (B.Sc. Physics), University of Delhi, India, 2006 Masters of Science (M.Sc. Physics), University of Delhi, India, 2008 Master of Science (M.S. Materials Engg.), University of Central Florida, USA, 2009 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mechanical, Materials and Aerospace Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida, USA Fall Term 2012 Major Professor: Patrick Schelling © 2012 Lalit Shokeen ii ABSTRACT We present a model of laser-solid interactions in silicon based on an empirical potential developed under conditions of strong electronic excitations. The parameters of the interatomic potential depends on the temperature of the electronic subsystem Te, which is directly related to the density of the electron-hole pairs and hence the number of broken bonds. -
Implementation of Methods to Accurately Predict Transition Pathways and the Underlying Potential Energy Surface of Biomolecular Systems
IMPLEMENTATION OF METHODS TO ACCURATELY PREDICT TRANSITION PATHWAYS AND THE UNDERLYING POTENTIAL ENERGY SURFACE OF BIOMOLECULAR SYSTEMS By DELARAM GHOREISHI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2019 c 2019 Delaram Ghoreishi I dedicate this dissertation to my mother, my brother, and my partner. For their endless love, support, and encouragement. ACKNOWLEDGMENTS I am thankful to my advisor, Adrian Roitberg, for his guidance during my graduate studies. I am grateful for the opportunities he provided me and for allowing me to work independently. I also thank my committee members, Rodney Bartlett, Xiaoguang Zhang, and Alberto Perez, for their valuable inputs. I am grateful to the University of Florida Informatics Institue for providing financial support in 2016, allowing me to take a break from teaching and focus more on research. I like to acknowledge my group members and friends for their moral support and technical assistance. Natali di Russo helped me become familiar with Amber. I thank Pilar Buteler, Sunidhi Lenka, and Vinicius Cruzeiro for daily conversations regarding science and life. Pancham Lal Gupta was my cpptraj encyclopedia. I thank my physicist colleagues, Ankita Sarkar and Dustin Tracy, who went through the intense physics coursework with me during the first year. I thank Farhad Ramezanghorbani, Justin Smith, Kavindri Ranasinghe, and Xiang Gao for helpful discussions regarding ANI and active learning. I also thank David Cerutti from Rutgers University for his help with NEB implementation. I thank Pilar Buteler and Alvaro Gonzalez for the good times we had camping and climbing. -
UOW High Performance Computing Cluster User's Guide
UOW High Performance Computing Cluster User’s Guide Information Management & Technology Services University of Wollongong ( Last updated on February 2, 2015) Contents 1. Overview6 1.1. Specification................................6 1.2. Access....................................7 1.3. File System.................................7 2. Quick Start9 2.1. Access the HPC Cluster...........................9 2.1.1. From within the UOW campus...................9 2.1.2. From the outside of UOW campus.................9 2.2. Work at the HPC Cluster.......................... 10 2.2.1. Being familiar with the environment................ 10 2.2.2. Setup the working space...................... 11 2.2.3. Initialize the computational task.................. 11 2.2.4. Submit your job and check the results............... 14 3. Software 17 3.1. Software Installation............................ 17 3.2. Software Environment........................... 17 3.3. Software List................................ 19 4. Queue System 22 4.1. Queue Structure............................... 22 4.1.1. Normal queue............................ 22 4.1.2. Special queues........................... 23 4.1.3. Schedule policy........................... 23 4.2. Job Management.............................. 23 4.2.1. PBS options............................. 23 4.2.2. Submit a batch job......................... 25 4.2.3. Check the job/queue status..................... 27 4.2.4. Submit an interactive job...................... 29 4.2.5. Submit workflow jobs....................... 30 4.2.6. Delete jobs............................. 31 5. Utilization Agreement 32 5.1. Policy.................................... 32 5.2. Acknowledgements............................. 32 5.3. Contact Information............................. 33 Appendices 35 A. Access the HPC cluster from Windows clients 35 A.1. Putty..................................... 35 A.2. Configure ‘Putty’ with UOW proxy.................... 36 A.3. SSH Secure Shell Client.......................... 38 B. Enable Linux GUI applications using Xming 41 B.1. -
Redox Active Iron Nitrosyl Units in Proton Reduction Electrocatalysis
ARTICLE Received 11 Nov 2013 | Accepted 18 Mar 2014 | Published 2 May 2014 DOI: 10.1038/ncomms4684 Redox active iron nitrosyl units in proton reduction electrocatalysis Chung-Hung Hsieh1, Shengda Ding1,O¨ zlen F. Erdem2, Danielle J. Crouthers1, Tianbiao Liu3, Charles C.L. McCrory4, Wolfgang Lubitz2, Codrina V. Popescu5, Joseph H. Reibenspies1, Michael B. Hall1 & Marcetta Y. Darensbourg1 Base metal, molecular catalysts for the fundamental process of conversion of protons and electrons to dihydrogen, remain a substantial synthetic goal related to a sustainable energy future. Here we report a diiron complex with bridging thiolates in the butterfly shape of the 2Fe2S core of the [FeFe]-hydrogenase active site but with nitrosyl rather than þ carbonyl or cyanide ligands. This binuclear [(NO)Fe(N2S2)Fe(NO)2] complex maintains structural integrity in two redox levels; it consists of a (N2S2)Fe(NO) complex (N2S2 ¼ N,N0-bis(2-mercaptoethyl)-1,4-diazacycloheptane) that serves as redox active metallo- dithiolato bidentate ligand to a redox active dinitrosyl iron unit, Fe(NO)2. Experimental and theoretical methods demonstrate the accommodation of redox levels in both components of the complex, each involving electronically versatile nitrosyl ligands. An interplay of orbital mixing between the Fe(NO) and Fe(NO)2 sites and within the iron nitrosyl bonds in each moiety is revealed, accounting for the interactions that facilitate electron uptake, storage and proton reduction. 1 Department of Chemistry, Texas A&M University, College Station, Texas 77843, USA. 2 Max Planck Institute for Chemical Energy Conversion, Stiftstrasse 34-36, 45470 Muelheim a.d. Ruhr, Germany. 3 Center for Molecular Electrocatalysis, Pacific Northwest National Laboratory, Richland, Washington 99354, USA. -
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. -
Semiemprical Methods Chapter 5 Semiemprical Methods
Semiemprical Methods Chapter 5 Semiemprical Methods Because of the difficulties in applying ab initio methods to medium and large molecules, many semiempirical methods have been developed to treat such molecules. The earliest semiempirical methods treated only the π electrons of conjugated molecules. In the π-electron approximation, the nπ π electrons are treated separately by incorporating the effects of the σ electrons and the nuclei into some sort of effective π- electron Hamiltonian Ĥπ (recall the similar valence-electron approximation where Vi is the potential energy of the ith π electron in the field produced by the nuclei and the σ electrons. The core is everything except the π electrons. The most celebrated semiempirical π-electron theory is the Hückel molecular-orbital (HMO) method, developed in the 1930s to treat planar conjugated hydrocarbons. Here the π-electron Hamiltonian is approximated by the simpler form where Ĥeff(i) incorporates the effects of the π-electron repulsions in an average way. In fact the Hückel method does not specify any explicit form for Ĥeff(i). Since the Hückel π-electron Hamiltonian is the sum of one-electron Hamiltonians, a separation of variables is possible. We have The wave function takes no account of spin or the antisymmetry requirement. To do so, we must put each electron in a spin-orbital ui=ii. The wave function π is then written as a Slater determinant of spin-orbitals. Since Ĥeff(i) is not specified, there is no point in trying to solve above eq. directly. Instead, the variational method is used. The next assumption in the HMO method is to approximate the π MOs as LCAOs. -
Seeding the Multi-Dimensional Nonequilibrium Pulling for Hamiltonian Variation
Seeding the Multi-dimensional Nonequilibrium Pulling for Hamiltonian Variation: Indirect QM/MM Free Energy Simulations Zhaoxi Sun1* and Qiaole He2 1Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Institute of Theoretical and Computational Chemistry, Peking University, Beijing 100871, China 2State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 200237 Shanghai, China *To whom correspondence should be addressed: [email protected] Abstract Combination of free energy simulations in the alchemical and configurational spaces provides a feasible route to access the thermodynamic profiles under a computationally demanding target Hamiltonian. Normally, due to the significant differences between the computational cost of ab initio quantum mechanics (QM) calculations and those of semi-empirical quantum mechanics (SQM) and molecular mechanics (MM), this indirect method could be used to obtain the QM thermodynamics by combining the SQM or MM results and the SQM-to-QM or MM-to-QM corrections. In our previous works, a multi-dimensional nonequilibrium pulling framework for Hamiltonian variations has been introduced based on bidirectional pulling and bidirectional reweighting. The method performs nonequilibrium free energy simulations in the configurational space to obtain the thermodynamic profile along the conformational change pathway under a selected computationally efficient Hamiltonian, and uses the nonequilibrium alchemical method to correct or perturb the thermodynamic profile to that under the target Hamiltonian. The BAR-based method is designed to achieve the best generality and transferability and thus leads to modest (~20 folds) speedup. In this work, we explore the possibility of further accelerating the nonequilibrium free energy simulation by employing unidirectional pulling and using the selection criterion to obtain the initial configurations used to initiate nonequilibrium trajectories following the idea of adaptive steered molecular dynamics (ASMD). -
Gaussian 16 Features at a Glance Features Introduced Since Gaussian 09 Rev a Are in Blue
Gaussian 16 Features at a Glance Features introduced since Gaussian 09 Rev A are in blue. Existing features enhanced in Gaussian 16 are in green. Fundamental Algorithms ◆ empirical dispersion: PFD, GD2, GD3, GD3BJ ◆ Calculation of one- & two-electron integrals over any contracted ◆ functionals including dispersion: APFD, B97D3, B2PLYPD3 gaussian functions ◆ long range-corrected: LC-ωPBE, CAM-B3LYP, ωB97XD and ◆ Conventional, direct, semi-direct and in-core algorithms variations, Hirao’s general LC correction ◆ Linearized computational cost via automated fast multipole ◆ Larger numerical integrations grids methods (FMM) and sparse matrix techniques ◆ Harris initial guess Electron Correlation: ◆ Initial guess generated from fragment guesses or fragment SCF All methods/job types are available for both closed and open shell solutions systems and may use frozen core orbitals; restricted open shell ◆ Density fitting and Coulomb engine for pure DFT calculations, calculations are available for MP2, MP3, MP4 and CCSD/CCSD(T) including automated generation of fitting basis sets energies. ◆ O(N) exact exchange for HF and hybrid DFT ◆ MP2 energies, gradients, and frequencies ◆ 1D, 2D, 3D periodic boundary conditions (PBC) energies & ◆ Double hybrid DFT energies, gradients and frequencies, with gradients (HF & DFT) optional empirical dispersion (see list in “Density Functional ◆ Shared-memory (SMP), cluster/network and GPU-based parallel Theory” above) execution ◆ CASSCF calculations with MP2 correlation for any specified set of states Model Chemistries -
Acronyms Used in Theoretical Chemistry
Pure & Appl. Chem., Vol. 68, No. 2, pp. 387-456, 1996. Printed in Great Britain. INTERNATIONAL UNION OF PURE AND APPLIED CHEMISTRY PHYSICAL CHEMISTRY DIVISION WORKING PARTY ON THEORETICAL AND COMPUTATIONAL CHEMISTRY ACRONYMS USED IN THEORETICAL CHEMISTRY Prepared for publication by the Working Party consisting of R. D. BROWN* (Australia, Chiman);J. E. BOWS (USA); R. HILDERBRANDT (USA); K. LIM (Australia); I. M. MILLS (UK); E. NIKITIN (Russia); M. H. PALMER (UK). The focal point to which to send comments and suggestions is the coordinator of the project: RONALD D. BROWN Chemistry Department, Monash University, Clayton, Victoria 3 168, Australia. Responses by e-mail would be particularly appreciated, the number being: rdbrown @ vaxc.cc .monash.edu.au another alternative is fax at: +61 3 9905 4597 Acronyms used in theoretical chemistry synogsis An alphabetic list of acronyms used in theoretical chemistry is presented. Some explanatory references have been added to make acronyms better understandable but still more are needed. Critical comments, additional references, etc. are requested. INTRODUC'IION The IUPAC Working Party on Theoretical Chemistry was persuaded, by discussion with colleagues, that the compilation of a list of acronyms used in theoretical chemistry would be a useful contribution. Initial lists of acronyms drawn up by several members of the working party have been augmented by the provision of a substantial list by Chemical Abstract Service (see footnote below). The working party is particularly grateful to CAS for this generous help. It soon became apparent that many of the acron ms needed more than mere spelling out to make them understandable and so we have added expr anatory references to many of them. -
Semiempirical Methods
Chapter 7 Semiempirical Methods 7.1 Introduction Semiempirical methods modify Hartree-Fock (HF) calculations by introducing functions with empirical parameters. These parameters are adjusted with experimen- tal conclusions to improve the quality of computation. The real cost of computation is due to the two-electron integrals in the Hamiltonian that has been simplified in this method. Semiempirical methods are based on three approximation schemes. 1. The elimination of the core electrons from the calculation. Inner electrons do not contribute towards chemical activity, which makes it pos- sible to remove the core electron functions from the Hamiltonian calculation. Normally, the entire core (the nucleus and core electrons) of atoms is replaced by a parameterized function. This has the effect of drastically reducing the com- plexity of the calculation without a major impact on the accuracy. 2. The use of the minimum number of basis sets. In this approximation, while introducing the functions of valence electrons, only the minimum required number of basis sets will be used. This technique also reduces the complexity of computation to a large extent. 3. The reduction of the number of two-electron integrals. This approximation is introduced on the basis of experimentation rather than chemical grounds. The majority of the work in ab initio calculations is in the evaluation of the two electron integrals (Coulomb and exchange). All modern semiempirical methods are based on the modified neglect of differential over- lap (MNDO) approach. In this method, parameters are assigned for different atomic types and are fitted to reproduce properties such as heats of formation, geometrical variables, dipole moments, and first ionization energies. -
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