Quantum Chemistry for Spectroscopy
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Immanants of Totally Positive Matrices Are Nonnegative
IMMANANTS OF TOTALLY POSITIVE MATRICES ARE NONNEGATIVE JOHN R. STEMBRIDGE Introduction Let Mn{k) denote the algebra of n x n matrices over some field k of characteristic zero. For each A>valued function / on the symmetric group Sn, we may define a corresponding matrix function on Mn(k) in which (fljji—• > X\w )&i (i)'"a ( )' (U weSn If/ is an irreducible character of Sn, these functions are known as immanants; if/ is an irreducible character of some subgroup G of Sn (extended trivially to all of Sn by defining /(vv) = 0 for w$G), these are known as generalized matrix functions. Note that the determinant and permanent are obtained by choosing / to be the sign character and trivial character of Sn, respectively. We should point out that it is more traditional to use /(vv) in (1) where we have used /(W1). This change can be undone by transposing the matrix. If/ happens to be a character, then /(w-1) = x(w), so the generalized matrix function we have indexed by / is the complex conjugate of the traditional one. Since the characters of Sn are real (and integral), it follows that there is no difference between our indexing of immanants and the traditional one. It is convenient to associate with each AeMn(k) the following element of the group algebra kSn: [A]:= £ flliW(1)...flBiW(B)-w~\ In terms of this notation, the matrix function defined by (1) can be described more simply as A\—+x\Ai\, provided that we extend / linearly to kSn. Note that if we had used w in place of vv"1 here and in (1), then the restriction of [•] to the group of permutation matrices would have been an anti-homomorphism, rather than a homomorphism. -
Some Classes of Hadamard Matrices with Constant Diagonal
BULL. AUSTRAL. MATH. SOC. O5BO5. 05B20 VOL. 7 (1972), 233-249. • Some classes of Hadamard matrices with constant diagonal Jennifer Wallis and Albert Leon Whiteman The concepts of circulant and backcircul-ant matrices are generalized to obtain incidence matrices of subsets of finite additive abelian groups. These results are then used to show the existence of skew-Hadamard matrices of order 8(1*f+l) when / is odd and 8/ + 1 is a prime power. This shows the existence of skew-Hadamard matrices of orders 296, 592, 118U, l6kO, 2280, 2368 which were previously unknown. A construction is given for regular symmetric Hadamard matrices with constant diagonal of order M2m+l) when a symmetric conference matrix of order km + 2 exists and there are Szekeres difference sets, X and J , of size m satisfying x € X =» -x £ X , y £ Y ~ -y d X . Suppose V is a finite abelian group with v elements, written in additive notation. A difference set D with parameters (v, k, X) is a subset of V with k elements and such that in the totality of all the possible differences of elements from D each non-zero element of V occurs X times. If V is the set of integers modulo V then D is called a cyclic difference set: these are extensively discussed in Baumert [/]. A circulant matrix B = [b. •) of order v satisfies b. = b . (j'-i+l reduced modulo v ), while B is back-circulant if its elements Received 3 May 1972. The authors wish to thank Dr VI.D. Wallis for helpful discussions and for pointing out the regularity in Theorem 16. -
Quantum Crystallography†
Chemical Science View Article Online MINIREVIEW View Journal | View Issue Quantum crystallography† Simon Grabowsky,*a Alessandro Genoni*bc and Hans-Beat Burgi*de Cite this: Chem. Sci.,2017,8,4159 ¨ Approximate wavefunctions can be improved by constraining them to reproduce observations derived from diffraction and scattering experiments. Conversely, charge density models, incorporating electron-density distributions, atomic positions and atomic motion, can be improved by supplementing diffraction Received 16th December 2016 experiments with quantum chemically calculated, tailor-made electron densities (form factors). In both Accepted 3rd March 2017 cases quantum chemistry and diffraction/scattering experiments are combined into a single, integrated DOI: 10.1039/c6sc05504d tool. The development of quantum crystallographic research is reviewed. Some results obtained by rsc.li/chemical-science quantum crystallography illustrate the potential and limitations of this field. 1. Introduction today's organic, inorganic and physical chemistry. These tools are usually employed separately. Diffraction and scattering Quantum chemistry methods and crystal structure determina- experiments provide structures at the atomic scale, while the Creative Commons Attribution 3.0 Unported Licence. tion are highly developed research tools, indispensable in techniques of quantum chemistry provide wavefunctions and aUniversitat¨ Bremen, Fachbereich 2 – Biologie/Chemie, Institut fur¨ Anorganische eUniversitat¨ Zurich,¨ Institut fur¨ Chemie, Winterthurerstrasse 190, CH-8057 Zurich,¨ Chemie und Kristallographie, Leobener Str. NW2, 28359 Bremen, Germany. Switzerland E-mail: [email protected] † Dedicated to Prof. Louis J. Massa on the occasion of his 75th birthday and to bCNRS, Laboratoire SRSMC, UMR 7565, Vandoeuvre-l`es-Nancy, F-54506, France Prof. Dylan Jayatilaka on the occasion of his 50th birthday in recognition of their cUniversit´e de Lorraine, Laboratoire SRSMC, UMR 7565, Vandoeuvre-l`es-Nancy, F- pioneering contributions to the eld of X-ray quantum crystallography. -
Quantum Reform
feature Quantum reform Leonie Mueck Quantum computers potentially offer a faster way to calculate chemical properties, but the exact implications of this speed-up have only become clear over the last year. The first quantum computers are likely to enable calculations that cannot be performed classically, which might reform quantum chemistry — but we should not expect a revolution. t had been an exhausting day at the 2012 International Congress of Quantum IChemistry with countless presentations reporting faster and more accurate methods for calculating chemical information using computers. In the dry July heat of Boulder, Colorado, a bunch of young researchers decided to end the day with a cool beer, but the scientific discussion didn’t fade away. Thoughts on the pros and cons of all the methods — the approximations they involved and the chemical problems that they could solve — bounced across the table, until somebody said “Anyway, in a few years we will have a quantum computer and our approximate methods will be obsolete”. An eerie silence followed. What a frustrating thought! They were devoting their careers to quantum chemistry — working LIBRARY PHOTO RICHARD KAIL/SCIENCE tirelessly on applying the laws of quantum mechanics to treat complex chemical of them offering a different trade-off between cannot do on a ‘classical’ computer, it does problems with a computer. And in one fell accuracy and computational feasibility. not look like all the conventional quantum- swoop, would all of those efforts be wasted Enter the universal quantum computer. chemical methods will become obsolete or as chemists turn to quantum computers and This dream machine would basically work that quantum chemists will be out of their their enormous computing power? like a normal digital computer; it would jobs in the foreseeable future. -
An Infinite Dimensional Birkhoff's Theorem and LOCC-Convertibility
An infinite dimensional Birkhoff's Theorem and LOCC-convertibility Daiki Asakura Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan August 29, 2016 Preliminary and Notation[1/13] Birkhoff's Theorem (: matrix analysis(math)) & in infinite dimensinaol Hilbert space LOCC-convertibility (: quantum information ) Notation H, K : separable Hilbert spaces. (Unless specified otherwise dim = 1) j i; jϕi 2 H ⊗ K : unit vectors. P1 P1 majorization: for σ = a jx ihx j, ρ = b jy ihy j 2 S(H), P Pn=1 n n n n=1 n n n ≺ () n # ≤ n # 8 2 N σ ρ i=1 ai i=1 bi ; n . def j i ! j i () 9 2 N [ f1g 9 H f gn 9 ϕ n , POVM on Mi i=1 and a set of LOCC def K f gn unitary on Ui i=1 s.t. Xn j ih j ⊗ j ih j ∗ ⊗ ∗ ϕ ϕ = (Mi Ui ) (Mi Ui ); in C1(H): i=1 H jj · jj "in C1(H)" means the convergence in Banach space (C1( ); 1) when n = 1. LOCC-convertibility[2/13] Theorem(Nielsen, 1999)[1][2, S12.5.1] : the case dim H, dim K < 1 j i ! jϕi () TrK j ih j ≺ TrK jϕihϕj LOCC Theorem(Owari et al, 2008)[3] : the case of dim H, dim K = 1 j i ! jϕi =) TrK j ih j ≺ TrK jϕihϕj LOCC TrK j ih j ≺ TrK jϕihϕj =) j i ! jϕi ϵ−LOCC where " ! " means "with (for any small) ϵ error by LOCC". ϵ−LOCC TrK j ih j ≺ TrK jϕihϕj ) j i ! jϕi in infinite dimensional space has LOCC been open. -
Quantum Chemistry and Spectroscopy – Spring 2018
CHE 336 – Quantum Chemistry and Spectroscopy – Spring 2018 Instructor: Dr. Jessica Sarver Email: [email protected] Office: HSC 364 (email hours: 8 am – 10 pm) Office Hours: 10am-12pm Tuesday, 2-3pm Wednesday, 10:30-11:30am Friday, or by appointment Course Description (from Westminster undergraduate catalog) Quantum chemistry and spectroscopy is the study of the microscopic behavior of matter and its interaction with electromagnetic radiation. Topics include the formulation and application of quantum mechanical models, atomic and molecular structure, and various spectroscopic techniques. Laboratory activities demonstrate the fundamental principles of physical chemistry. Methods that will be used during the laboratory portion include: polarimetry, UV-Vis and fluorescence spectroscopies, electrochemistry, and computational/molecular modeling. Prerequisites: C- grade in CHE 117 and MTH 152 and PHY 152. Course Outcomes The chemistry major outcomes are: • Describe and explain quantum theory and the three quantum mechanical models of motion. • Describe and explain how these theories/models are applied and incorporated to applications of atomic/molecular structure and spectroscopy. • Describe and explain the principles of four major types of spectroscopy and their applications. • Describe and explain the measurable thermodynamic parameters for physical and chemical processes and equilibria. • Describe and explain how chemical kinetics can be used to investigate time-dependent processes. • Solve mathematical problems based on physical chemical principles and connect the numerical results with these principles. Textbook The text for this course is Physical Chemistry, 10th Ed. by Atkins and de Paula. Readings and problem set questions will be assigned from this text. You are strongly urged to get a copy of this textbook and complete all the assigned readings and homework. -
Alternating Sign Matrices and Polynomiography
Alternating Sign Matrices and Polynomiography Bahman Kalantari Department of Computer Science Rutgers University, USA [email protected] Submitted: Apr 10, 2011; Accepted: Oct 15, 2011; Published: Oct 31, 2011 Mathematics Subject Classifications: 00A66, 15B35, 15B51, 30C15 Dedicated to Doron Zeilberger on the occasion of his sixtieth birthday Abstract To each permutation matrix we associate a complex permutation polynomial with roots at lattice points corresponding to the position of the ones. More generally, to an alternating sign matrix (ASM) we associate a complex alternating sign polynomial. On the one hand visualization of these polynomials through polynomiography, in a combinatorial fashion, provides for a rich source of algo- rithmic art-making, interdisciplinary teaching, and even leads to games. On the other hand, this combines a variety of concepts such as symmetry, counting and combinatorics, iteration functions and dynamical systems, giving rise to a source of research topics. More generally, we assign classes of polynomials to matrices in the Birkhoff and ASM polytopes. From the characterization of vertices of these polytopes, and by proving a symmetry-preserving property, we argue that polynomiography of ASMs form building blocks for approximate polynomiography for polynomials corresponding to any given member of these polytopes. To this end we offer an algorithm to express any member of the ASM polytope as a convex of combination of ASMs. In particular, we can give exact or approximate polynomiography for any Latin Square or Sudoku solution. We exhibit some images. Keywords: Alternating Sign Matrices, Polynomial Roots, Newton’s Method, Voronoi Diagram, Doubly Stochastic Matrices, Latin Squares, Linear Programming, Polynomiography 1 Introduction Polynomials are undoubtedly one of the most significant objects in all of mathematics and the sciences, particularly in combinatorics. -
Counterfactual Explanations for Graph Neural Networks
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei University of Amsterdam University of Amsterdam Sapienza University of Rome Amsterdam, Netherlands Amsterdam, Netherlands Rome, Italy [email protected] [email protected] [email protected] Maarten de Rijke Fabrizio Silvestri University of Amsterdam Sapienza University of Rome Amsterdam, Netherlands Rome, Italy [email protected] [email protected] ABSTRACT that result in an alternative output response (i.e., prediction). If Given the increasing promise of Graph Neural Networks (GNNs) in the modifications recommended are also clearly actionable, this is real-world applications, several methods have been developed for referred to as achieving recourse [12, 28]. explaining their predictions. So far, these methods have primarily To motivate our problem, we consider an ML application for focused on generating subgraphs that are especially relevant for computational biology. Drug discovery is a task that involves gen- a particular prediction. However, such methods do not provide erating new molecules that can be used for medicinal purposes a clear opportunity for recourse: given a prediction, we want to [26, 33]. Given a candidate molecule, a GNN can predict if this understand how the prediction can be changed in order to achieve molecule has a certain property that would make it effective in a more desirable outcome. In this work, we propose a method for treating a particular disease [9, 19, 32]. If the GNN predicts it does generating counterfactual (CF) explanations for GNNs: the minimal not have this desirable property, CF explanations can help identify perturbation to the input (graph) data such that the prediction the minimal change one should make to this molecule, such that it changes. -
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Harvard University - DASH Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Rappoport, Dmitrij, Cooper J. Galvin, Dmitry Zubarev, and Alán Aspuru-Guzik. 2014. “Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.” Journal of Chemical Theory and Computation 10 (3) (March 11): 897–907. Published Version doi:10.1021/ct401004r Accessed February 19, 2015 5:14:37 PM EST Citable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:12697373 Terms of Use This article was downloaded from Harvard University's DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP (Article begins on next page) Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry Dmitrij Rappoport,∗,† Cooper J Galvin,‡ Dmitry Yu. Zubarev,† and Alán Aspuru-Guzik∗,† Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA, and Pomona College, 333 North College Way, Claremont, CA 91711, USA E-mail: [email protected]; [email protected] Abstract While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for mod- eling complex structures and large-scale chemical systems. Here we present heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in metabolism and prebiotic chemistry. -
Introduction to Quantum Chemistry
Chem. 140B Dr. J.A. Mack Introduction to Quantum Chemistry Why as a chemist, do you need to learn this material? 140B Dr. Mack 1 Without Quantum Mechanics, how would you explain: • Periodic trends in properties of the elements • Structure of compounds e.g. Tetrahedral carbon in ethane, planar ethylene, etc. • Bond lengths/strengths • Discrete spectral lines (IR, NMR, Atomic Absorption, etc.) • Electron Microscopy & surface science Without Quantum Mechanics, chemistry would be a purely empirical science. (We would be no better than biologists…) 140B Dr. Mack 2 1 Classical Physics On the basis of experiments, in particular those performed by Galileo, Newton came up with his laws of motion: 1. A body moves with a constant velocity (possibly zero) unless it is acted upon by a force. 2. The “rate of change of motion”, i.e. the rate of change of momentum, is proportional to the impressed force and occurs in the direction of the applied force. 3. To every action there is an equal and opposite reaction. 4. The gravitational force of attraction between two bodies is proportional to the product of their masses and inversely proportional to the square of the distance between them. m m F = G × 1 2 r 2 140B Dr. Mack 3 The Failures of Classical Mechanics 1. Black Body Radiation: The Ultraviolet Catastrophe 2. The Photoelectric Effect: Einstein's belt buckle 3. The de Broglie relationship: Dude you have a wavelength! 4. The double-slit experiment: More wave/particle duality 5. Atomic Line Spectra: The 1st observation of quantum levels 140B Dr. Mack 4 2 Black Body Radiation Light Waves: Electromagnetic Radiation Light is composed of two perpendicular oscillating vectors waves: A magnetic field & an electric field As the light wave passes through a substance, the oscillating fields can stimulate the movement of electrons in a substance. -
MATLAB Array Manipulation Tips and Tricks
MATLAB array manipulation tips and tricks Peter J. Acklam E-mail: [email protected] URL: http://home.online.no/~pjacklam 14th August 2002 Abstract This document is intended to be a compilation of tips and tricks mainly related to efficient ways of performing low-level array manipulation in MATLAB. Here, “manipu- lation” means replicating and rotating arrays or parts of arrays, inserting, extracting, permuting and shifting elements, generating combinations and permutations of ele- ments, run-length encoding and decoding, multiplying and dividing arrays and calcu- lating distance matrics and so forth. A few other issues regarding how to write fast MATLAB code are also covered. I'd like to thank the following people (in alphabetical order) for their suggestions, spotting typos and other contributions they have made. Ken Doniger and Dr. Denis Gilbert Copyright © 2000–2002 Peter J. Acklam. All rights reserved. Any material in this document may be reproduced or duplicated for personal or educational use. MATLAB is a trademark of The MathWorks, Inc. (http://www.mathworks.com). TEX is a trademark of the American Mathematical Society (http://www.ams.org). Adobe PostScript and Adobe Acrobat Reader are trademarks of Adobe Systems Incorporated (http://www.adobe.com). The TEX source was written with the GNU Emacs text editor. The GNU Emacs home page is http://www.gnu.org/software/emacs/emacs.html. The TEX source was formatted with AMS-LATEX to produce a DVI (device independent) file. The PS (PostScript) version was created from the DVI file with dvips by Tomas Rokicki. The PDF (Portable Document Format) version was created from the PS file with ps2pdf, a part of Aladdin Ghostscript by Aladdin Enterprises. -
Computational Analysis of the Mechanism of Chemical Reactions
Computational Analysis of the Mechanism of Chemical Reactions in Terms of Reaction Phases: Hidden Intermediates and Hidden Transition States ELFI KRAKA* AND DIETER CREMER Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314 RECEIVED ON JANUARY 9, 2009 CON SPECTUS omputational approaches to understanding chemical reac- Ction mechanisms generally begin by establishing the rel- ative energies of the starting materials, transition state, and products, that is, the stationary points on the potential energy surface of the reaction complex. Examining the intervening spe- cies via the intrinsic reaction coordinate (IRC) offers further insight into the fate of the reactants by delineating, step-by- step, the energetics involved along the reaction path between the stationary states. For a detailed analysis of the mechanism and dynamics of a chemical reaction, the reaction path Hamiltonian (RPH) and the united reaction valley approach (URVA) are an effi- cient combination. The chemical conversion of the reaction complex is reflected by the changes in the reaction path direction t(s) and reaction path curvature k(s), both expressed as a function of the path length s. This information can be used to partition the reaction path, and by this the reaction mech- anism, of a chemical reaction into reaction phases describing chemically relevant changes of the reaction complex: (i) a contact phase characterized by van der Waals interactions, (ii) a preparation phase, in which the reactants prepare for the chemical processes, (iii) one or more transition state phases, in which the chemical processes of bond cleavage and bond formation take place, (iv) a product adjustment phase, and (v) a separation phase.