ESI-3D: Electron Sharing Indexes Program for 3D Molecular Space Partition

Size: px
Start display at page:

Download "ESI-3D: Electron Sharing Indexes Program for 3D Molecular Space Partition"

Transcription

1 ESI-3D: Electron Sharng Indexes Program for 3D Molecular Space Partton Insttute of Computatonal Chemstry (Grona), 006. Report bugs to Eduard Matto: or The Electron Sharng Indexes (ESI) The electron sharng ndexes (ESI) account to some extent for the electron sharng of a gven par of atoms. The ESI computed wth ths program are calculated from the exchange correlaton densty (XCD), whch reads as follows: ( ) ( ) ( ) γ xc ( x, x ) = γ ( x ) γ ( x ) γ ( x, ) () x and measures the dvergences of the par densty, ( γ ) ( x, x ), a true electron par dstrbuton, and a fcttous par densty constructed as the product of two ( ) ( ) ndependent one electron dstrbutons, γ ( x) γ ( x ). Theore, the XCD ntegrates to N, snce the densty and the par densty ntegrate to N and N(N-), respectvely: γ ( x, x ) dxdx xc = N () The XCD s an appealng functon because t fulflls several nterestng propertes. Frst of all, t s non-negatve defnte; the number of fcttous pars of electrons for a model of ndependent electrons s always greater than the actual number of pars. Secondly, by defnton, n the case of non-nteractng electrons the functon s zero. And n thrd place, ntegrated over the whole space, t gves the number of electrons n the system, N, whch enables a chemcal meanngful molecular space decomposton of the N electrons of a gven system. For a sngle determnant wave functon (Hatree-Fock, or a Kohn-Sham orbtals one) the par densty can be wrtten n terms of the frst order reduced densty matrx (- RDM): r r r r ( ) r r γ γ ( xx ) = (3) γ ( ) ( ( x ) ) x γ ( x x ) () r r () r r ( x x ) γ ( x x )

2 () r where γ ( x x r ) s the frst order reduced densty matrx (-RDM) whch for the case r r x = x reproduces the densty. Thus smplfyng the expresson of the XCD to read: r r () r r γ ( x = γ ( x x ) (4) xc, x) In 975, Bader and Stephens ponted out that the ntegraton of the XCD upon two dfferent regons of the space A and B leads to a measure of the correlatve nteractons between the electrons n these two moetes. However, t was not Bader the frst to propose such a quantty as an ESI. In 993 Fulton 3 proposed an ESI based n the framework of the QTAIM by usng the -RDM n followng manner: () r r ( ) ( ) [ / = ] ( ) r r A B x x ( x x ) [ / ] r r δ, γ ; γ ; dxdx, AB (5) whereupon the pseudo-square root of the -RDM reads () r r ( ) [ / ] / * r r γ x x = λ η ( x ) η ( x ), ; k k k k (6) λ k and η k (x) beng the natural occupances and natural spn orbtals, respectvely. It s more convenent to express Eq. (5) n followng manner: (, = δ A λ λ S ( A) S (, / / j (7) S (A) s the dagonal element of the atomc overlap matrx (AOM) n terms of natural spnorbtals ntegrated over the doman of atom A: r r r S ( A) = η ( x ) η j ( x ) dx (8) A For sngle determnant methods Eq. (7) smplfes to: (, = δ A S ( A) S (, (9) where S (A) now corresponds to the AOM n terms of spnorbtals. For monodetermnantal wave functons t concdes wth later defntons gven by Ángyan 4 or by Fradera 5 for the quantum theory of atoms n molecules (QTAIM) partton of the

3 molecular space nto atomc domans. 6 Fradera et al. 5 also proposed the localzaton ndex as: A) δ λ ( A ) = = S ( A) S ( A), (0) Chemcally nterestng can be also the atomc delocalzaton whch s smply calculated as follows: ( A ) = δ δ, B A () and some authors recognze ths quantty as the atomc valence. 7 Fnally s worth notcng that these XCD based ESI can be calculated from dfferent parttons. The frst partton attempted to calculate ths ndex was reported by Wberg 8 for a Mullken-lke decomposton. 9 Ths program has been desgned to deal wth any AOM properly formatted. In partcular we have adapted the AIMPAC format, 0 whch s also the format of FUZZY package. Theore, by usng the *.nt fles generated by these programs, one can calculate QTAIM-ESI and FUZZY-ESI respectvely. The program s prepared for the calculaton of unrestrcted and restrcted sngle determnant ESI. In addton, the program provdes the possblty of splttng the ESI accordng to orbtal contrbutons. In general the ESI can be decomposed as follows: δ ( A, = ( ) ( ) S A S B = δ () j whch may gve further nsde n the orbtal contrbuton to the chemcal bondng. 3 In general an exact orbtal decomposton s done for those cases where S (A)=0 when and j belong to dfferent sets of orbtals. Ths s the case of σ-π separaton of a planar molecule. In the cases where the decomposton s not exact t s explctly ndcated n the output. For an extensve revson of these quanttes check for example erence 4.

4 Aromatcty Indexes: FLU and PDI From these ESI the calculaton of the aromatcty ndexes PDI 5, 6 and FLU 7 s also possble wth ESI-3D. The PDI s a specfc measure of local aromatcty for sx member rngs (6-MRs), where three para-related postons exst, namely (,4), (,5), and (3,6). By defnton, the PDI of a 6-MR s gven by: (,4 ) + δ (,5) δ ( 3,6) δ + PDI = (3) 3 Whle the FLU ndex of aromatcty reads: FLU = n RING A B δ ( δ ( A) α δ δ δ, (4) where the summaton runs over all adjacent pars of atoms around the rng, n s equal to the number of atoms of the rng, δ s the ESI for the atomc par A and B from the aromatc molecule chosen as a erence, and α s a constant to ensure the rato of atomc valences s greater than one, δ ( > δ ( A) α = (5) δ ( δ ( A) The program recognzes the bonds C-C, C-N and B-N for whch t takes the erence parameters from benzene, pyrdne and borazne, whch are respectvely.4,. and 0.86, for QTAIM, and.4,.5 and.63 for FUZZY parttons. If the bond s not recognzed the program uses C-C value by default and gves a warnng message. The program needs for atomc overlap matrces accordng to the format of FUZZY or AIMPAC 0 programs; 8 both programs are also freeware. When the orbtals are decomposed nto two groups the program attempts the calculaton of PDI π and FLU π. For a recent revew on electronc aromatcty ndexes check erence 9.

5 Accuracy In order to check the accuracy of the calculaton one must check that that the sum rule s fulflled: δ ( A ) ( ), Aj + λ A = δ ( A ) + λ( A ) = N( A ) = j N (6) It s of recommendable practce to ensure the accuracy acheved for N to be at least 0-3 electrons. Snce the ndexes are calculated from the AOM n Eq. (8), t s also recommendable that the followng quantty: Error( S) = S ( A) δ (7) A never ncreases over 0-3. Addtonally AIMPAC program gves the value of the laplacan of the densty wthn a basn. For an accurate calculaton ths value s usually below 0-3. Quotaton Usage Program: E. Matto, n 'ESI-3D: Electron Sharng Indexes Program for 3D Molecular Space Partton.' Grona, IQC, 006. PDI ndex: J. Poater, X. Fradera, M. Duran, and M. Solà, Chem. Eur. J., 003, 9, 400. FLU ndex: E. Matto, M. Duran, and M. Solà, J. Chem. Phys., 005,, PDI or FLU from FUZZY-partton: E. Matto, P. Salvador, M. Duran, and M. Solà, J. Phys. Chem. A, 006, 0, 508. Downloads ESI.x fle.n Tests: test.x ESI-3D: FUZZY: AIMPAC:

6 Keywords $ATOMS. Number of atoms and fles contanng the overlap matrx for each atom. PROAIM (AIMPAC) or FUZZY format expected. $ATOMS number of atoms (n) fle_.nt fle_.nt... fle_n.nt $BASIS. The number of occuped molecular orbtals (or spnorbtals f open-shell). $BASIS number of occuped (spn)orbtals $TYPE. Type of wave functon, restrcted- HF or KS wave functon (hf) or unrestrcted one (uhf) expected. If uhf then the program reads the number of the frst beta spnorbtal. $TYPE hf or uhf (number of the frst beta spnorbtal) $RING. Needed for the calculaton of aromatc propertes. Holds the nformaton of the connectvty of the rng(s). Important: the atoms of each rng must be specfed accordng to the connectvty of each rng. $RING number of rngs (nr) number of members n the frst rng (nm)... nm (the atom number accordng to ther connectvty)... number of members n the n-th rng (nmn)... nmn $GROUPS. Needed for the orbtal decomposton of the ESI and the aromatc ndexes. Frst we must provde the number of groups, and afterwards the number assgned to each orbtal accordng to the group they belong. For σ π separaton of 5 occuped orbtals t mght read: $GROUPS $FUZZY. When the program founds such keyword t uses the erences of FUZZY- ESI for the calculaton of FLU.

7 Example Benzene s gven as an example of a molecule wth a rng, whose decomposton s attempted by dfferent symmetry of occuped orbtals (benzene.es.d6h), and afterwards by dong the sgma-p separaton (benzene.es.cs). Here below a b explanaton: INPUT: $BASIS $ATOMS benze_c.nt benze_c.nt benze_c3.nt benze_c4.nt benze_c5.nt benze_c6.nt benze_h7.nt benze_h8.nt benze_h9.nt benze_h0.nt benze_h.nt benze_h.nt $TYPE hf $RING rngs found 6 members of th rng $GROUPS Ths nput requests a sngle Slater determnant closed-shell calculaton (hf) of all ESI accordng to Eq. (9) and (0). Snce the keyword $RING s specfed, the program wll attempt the calculaton of FLU, and provdng the number of members n the rng s sx, he wll also provde the PDI measure. ESI-3D wll perform an orbtal analyss of the ESI based on the decomposton gven. The FLU π and PDI π wll be also calculated because there are two groups of orbtals, and, whch wll be supposed σ and π respectvely.

8 OUTPUT Populaton and ESI analyss: Eq. (0) Eq. () Atom N (nt) loc. deloc. N N C C C C C C H H H H H H N(nt) s the populaton gven n the nput fles whch contan the AOM (*.nt) N corresponds to the populaton of orbtals n group N corresponds to the populaton of orbtals n group Eq. (9) Par DI DI DI C -C C -C C -C C -C C -C C -C C -H C -H C -H H -H H -H TOTAL: LOCAL: DELOC: TOTAL Total number of electrons calculated by Eq (6). LOCAL Total localzaton DELOC Total delocalzaton

9 δ ( δ (A) α δ δ δ RING - 6 bonds Atom par delta -->j j--> Exc(,j) dff Flu(,j) C -C C -C C 3-C C 4-C C 5-C C -C FLU = FLU = n RING A B Flu(, j) = n RING A B δ ( δ ( A) α δ δ δ PDI = References K. Ruedenberg, Rev. Mod. Phys., 96, 34, 36. R. F. W. Bader and M. E. Stephens, J. Am. Chem. Soc., 975, 97, 739. R. L. Fulton, J. Phys. Chem., 993, 97, 756. J. G. Ángyán, M. Loos, and I. Mayer, J. Phys. Chem., 994, 98, 544. X. Fradera, M. A. Austen, and R. F. W. Bader, J. Phys. Chem. A, 999, 03, 304. R. F. W. Bader, 'Atoms n Molecules: A Quantum Theory', Clarendon, 990. I. Mayer, Chem. Phys. Lett., 983, 97, 70. K. B. Wberg, Tetrahedron, 968, 4, 083. R. S. Mullken, J. Chem. Phys., 955, 3, 833. F. W. Begler-Köng, R. F. W. Bader, and T.-H. Tang, J. Comput. Chem., 98, 3, 37. I. Mayer and P. Salvador, n 'Program 'FUZZY'. Avalable from < Grona, 003. E. Matto, J. Poater, M. Solà, M. Duran, and P. Salvador, J. Phys. Chem. A, 005, 09, M. Güell, E. Matto, J. M. Lus, J. Poater, and M. Solà, J. Phys. Chem. A, 006, (submtted). E. Matto, M. Solà, P. Salvador, and M. Duran, Faraday Dscussons, 006, (accepted). J. Poater, X. Fradera, M. Duran, and M. Solà, Chem. Eur. J., 003, 9, 400. J. Poater, X. Fradera, M. Duran, and M. Solà, Chem. Eur. J., 003, 9, 3. E. Matto, M. Duran, and M. Solà, J. Chem. Phys., 005,, E. Matto, P. Salvador, M. Duran, and M. Solà, J. Phys. Chem. A, 006, 0, 508. J. Poater, M. Duran, M. Solà, and B. Slv, Chem. Rev., 005, 05, 39.

Supporting information.

Supporting information. Response to Comment on the paper "Restrcted Geometry Optmzaton: A Dfferent Way to Estmate Stablzaton Energes for Aromatc Molecules of Varous Types" Zhong-Heng Yu* and Peng Bao Supportng nformaton. Contents:

More information

A how to guide to second quantization method.

A how to guide to second quantization method. Phys. 67 (Graduate Quantum Mechancs Sprng 2009 Prof. Pu K. Lam. Verson 3 (4/3/2009 A how to gude to second quantzaton method. -> Second quantzaton s a mathematcal notaton desgned to handle dentcal partcle

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

Probabilistic method to determine electron correlation energy

Probabilistic method to determine electron correlation energy Probablstc method to determne electron elaton energy T.R.S. Prasanna Department of Metallurgcal Engneerng and Materals Scence Indan Insttute of Technology, Bombay Mumba 400076 Inda A new method to determne

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quantum Mechancs for Scentsts and Engneers Davd Mller Types of lnear operators Types of lnear operators Blnear expanson of operators Blnear expanson of lnear operators We know that we can expand functons

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Introduction to Density Functional Theory. Jeremie Zaffran 2 nd year-msc. (Nanochemistry)

Introduction to Density Functional Theory. Jeremie Zaffran 2 nd year-msc. (Nanochemistry) Introducton to Densty Functonal Theory Jereme Zaffran nd year-msc. (anochemstry) A- Hartree appromatons Born- Oppenhemer appromaton H H H e The goal of computatonal chemstry H e??? Let s remnd H e T e

More information

CHE450G Final Exam. CP-109 December 11, :30-12:30 AM

CHE450G Final Exam. CP-109 December 11, :30-12:30 AM CH450G Fnal xam CP-09 December, 2006 0:30-2:30 AM Last name Frst Name Score [ /5] 00 = % () Construct a physcally realstc molecular orbtal dagram for CS. Draw all SALC s, molecular orbtals, and provde

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

5.03, Inorganic Chemistry Prof. Daniel G. Nocera Lecture 2 May 11: Ligand Field Theory

5.03, Inorganic Chemistry Prof. Daniel G. Nocera Lecture 2 May 11: Ligand Field Theory 5.03, Inorganc Chemstry Prof. Danel G. Nocera Lecture May : Lgand Feld Theory The lgand feld problem s defned by the followng Hamltonan, h p Η = wth E n = KE = where = m m x y z h m Ze r hydrogen atom

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

5. Response properties in ab initio schemes

5. Response properties in ab initio schemes 5. Response propertes n ab nto schemes A number of mportant physcal observables s expressed va dervatves of total energy (or free energy) E. Examples are: E R 2 E R a R b forces on the nucle; crtcal ponts

More information

Effects of Ignoring Correlations When Computing Sample Chi-Square. John W. Fowler February 26, 2012

Effects of Ignoring Correlations When Computing Sample Chi-Square. John W. Fowler February 26, 2012 Effects of Ignorng Correlatons When Computng Sample Ch-Square John W. Fowler February 6, 0 It can happen that ch-square must be computed for a sample whose elements are correlated to an unknown extent.

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

ECEN 5005 Crystals, Nanocrystals and Device Applications Class 19 Group Theory For Crystals

ECEN 5005 Crystals, Nanocrystals and Device Applications Class 19 Group Theory For Crystals ECEN 5005 Crystals, Nanocrystals and Devce Applcatons Class 9 Group Theory For Crystals Dee Dagram Radatve Transton Probablty Wgner-Ecart Theorem Selecton Rule Dee Dagram Expermentally determned energy

More information

763622S ADVANCED QUANTUM MECHANICS Solution Set 1 Spring c n a n. c n 2 = 1.

763622S ADVANCED QUANTUM MECHANICS Solution Set 1 Spring c n a n. c n 2 = 1. 7636S ADVANCED QUANTUM MECHANICS Soluton Set 1 Sprng 013 1 Warm-up Show that the egenvalues of a Hermtan operator  are real and that the egenkets correspondng to dfferent egenvalues are orthogonal (b)

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

Density matrix. c α (t)φ α (q)

Density matrix. c α (t)φ α (q) Densty matrx Note: ths s supplementary materal. I strongly recommend that you read t for your own nterest. I beleve t wll help wth understandng the quantum ensembles, but t s not necessary to know t n

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

Advanced Quantum Mechanics

Advanced Quantum Mechanics Advanced Quantum Mechancs Rajdeep Sensarma! sensarma@theory.tfr.res.n ecture #9 QM of Relatvstc Partcles Recap of ast Class Scalar Felds and orentz nvarant actons Complex Scalar Feld and Charge conjugaton

More information

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2 ISQS 6348 Fnal Open notes, no books. Ponts out of 100 n parentheses. 1. The followng path dagram s gven: ε 1 Y 1 ε F Y 1.A. (10) Wrte down the usual model and assumptons that are mpled by ths dagram. Soluton:

More information

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density.

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density. 1 Unform Electron Gas Ths chapter llustrates the dea that all propertes of the homogeneous electron gas (HEG) can be calculated from electron densty. Intutve Representaton of Densty Electron densty n s

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

SPANC -- SPlitpole ANalysis Code User Manual

SPANC -- SPlitpole ANalysis Code User Manual Functonal Descrpton of Code SPANC -- SPltpole ANalyss Code User Manual Author: Dale Vsser Date: 14 January 00 Spanc s a code created by Dale Vsser for easer calbratons of poston spectra from magnetc spectrometer

More information

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

More information

Lecture 14: Forces and Stresses

Lecture 14: Forces and Stresses The Nuts and Bolts of Frst-Prncples Smulaton Lecture 14: Forces and Stresses Durham, 6th-13th December 2001 CASTEP Developers Group wth support from the ESF ψ k Network Overvew of Lecture Why bother? Theoretcal

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

Lecture 10: May 6, 2013

Lecture 10: May 6, 2013 TTIC/CMSC 31150 Mathematcal Toolkt Sprng 013 Madhur Tulsan Lecture 10: May 6, 013 Scrbe: Wenje Luo In today s lecture, we manly talked about random walk on graphs and ntroduce the concept of graph expander,

More information

2 More examples with details

2 More examples with details Physcs 129b Lecture 3 Caltech, 01/15/19 2 More examples wth detals 2.3 The permutaton group n = 4 S 4 contans 4! = 24 elements. One s the dentty e. Sx of them are exchange of two objects (, j) ( to j and

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

where the sums are over the partcle labels. In general H = p2 2m + V s(r ) V j = V nt (jr, r j j) (5) where V s s the sngle-partcle potental and V nt

where the sums are over the partcle labels. In general H = p2 2m + V s(r ) V j = V nt (jr, r j j) (5) where V s s the sngle-partcle potental and V nt Physcs 543 Quantum Mechancs II Fall 998 Hartree-Fock and the Self-consstent Feld Varatonal Methods In the dscusson of statonary perturbaton theory, I mentoned brey the dea of varatonal approxmaton schemes.

More information

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 16 STAT 39: MATHEMATICAL COMPUTATIONS I FALL 218 LECTURE 16 1 why teratve methods f we have a lnear system Ax = b where A s very, very large but s ether sparse or structured (eg, banded, Toepltz, banded plus

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980

Chat eld, C. and A.J.Collins, Introduction to multivariate analysis. Chapman & Hall, 1980 MT07: Multvarate Statstcal Methods Mke Tso: emal mke.tso@manchester.ac.uk Webpage for notes: http://www.maths.manchester.ac.uk/~mkt/new_teachng.htm. Introducton to multvarate data. Books Chat eld, C. and

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Supplemental document

Supplemental document Electronc Supplementary Materal (ESI) for Physcal Chemstry Chemcal Physcs. Ths journal s the Owner Socetes 01 Supplemental document Behnam Nkoobakht School of Chemstry, The Unversty of Sydney, Sydney,

More information

From Biot-Savart Law to Divergence of B (1)

From Biot-Savart Law to Divergence of B (1) From Bot-Savart Law to Dvergence of B (1) Let s prove that Bot-Savart gves us B (r ) = 0 for an arbtrary current densty. Frst take the dvergence of both sdes of Bot-Savart. The dervatve s wth respect to

More information

Army Ants Tunneling for Classical Simulations

Army Ants Tunneling for Classical Simulations Electronc Supplementary Materal (ESI) for Chemcal Scence. Ths journal s The Royal Socety of Chemstry 2014 electronc supplementary nformaton (ESI) for Chemcal Scence Army Ants Tunnelng for Classcal Smulatons

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS) Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

A quote of the week (or camel of the week): There is no expedience to which a man will not go to avoid the labor of thinking. Thomas A.

A quote of the week (or camel of the week): There is no expedience to which a man will not go to avoid the labor of thinking. Thomas A. A quote of the week (or camel of the week): here s no expedence to whch a man wll not go to avod the labor of thnkng. homas A. Edson Hess law. Algorthm S Select a reacton, possbly contanng specfc compounds

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

Number of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k

Number of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k ANOVA Model and Matrx Computatons Notaton The followng notaton s used throughout ths chapter unless otherwse stated: N F CN Y Z j w W Number of cases Number of factors Number of covarates Number of levels

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

CHAPTER 14 GENERAL PERTURBATION THEORY

CHAPTER 14 GENERAL PERTURBATION THEORY CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Using TranSIESTA (II): Integration contour and tbtrans

Using TranSIESTA (II): Integration contour and tbtrans Usng TranSIESTA (II): Integraton contour and tbtrans Frederco D. Novaes December 15, 2009 Outlne Usng TranSIESTA The ntegraton contour Electron densty from GF Why go complex? The non-equlbrum stuaton Usng

More information

ψ ij has the eigenvalue

ψ ij has the eigenvalue Moller Plesset Perturbaton Theory In Moller-Plesset (MP) perturbaton theory one taes the unperturbed Hamltonan for an atom or molecule as the sum of the one partcle Foc operators H F() where the egenfunctons

More information

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values

However, since P is a symmetric idempotent matrix, of P are either 0 or 1 [Eigen-values Fall 007 Soluton to Mdterm Examnaton STAT 7 Dr. Goel. [0 ponts] For the general lnear model = X + ε, wth uncorrelated errors havng mean zero and varance σ, suppose that the desgn matrx X s not necessarly

More information

Slater-Condon Rules. Antisymmetrization Operator APPENDIX M

Slater-Condon Rules. Antisymmetrization Operator APPENDIX M APPENDIX M Slater-Condon Rules The Slater determnants represent somethng lke the daly bread of quantum chemsts. Our goal s to learn how to use the Slater determnants when they are nvolved n calculaton

More information

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model Lecture 4. Thermodynamcs [Ch. 2] Energy, Entropy, and Avalablty Balances Phase Equlbra - Fugactes and actvty coeffcents -K-values Nondeal Thermodynamc Property Models - P-v-T equaton-of-state models -

More information

Analysis of Electron Delocalization in Aromatic Systems: Individual Molecular Orbital Contributions to Para-Delocalization Indexes (PDI)

Analysis of Electron Delocalization in Aromatic Systems: Individual Molecular Orbital Contributions to Para-Delocalization Indexes (PDI) J. Phys. Chem. A 2006, 110, 11569-11574 11569 Analyss of Electron Delocalzaton n Aromatc Systems: Indvdual Molecular Orbtal Contrbutons to Para-Delocalzaton Indexes (PDI) Mrea Gu1ell, Eduard Matto, Josep

More information

Turing Machines (intro)

Turing Machines (intro) CHAPTER 3 The Church-Turng Thess Contents Turng Machnes defntons, examples, Turng-recognzable and Turng-decdable languages Varants of Turng Machne Multtape Turng machnes, non-determnstc Turng Machnes,

More information

Thermodynamics General

Thermodynamics General Thermodynamcs General Lecture 1 Lecture 1 s devoted to establshng buldng blocks for dscussng thermodynamcs. In addton, the equaton of state wll be establshed. I. Buldng blocks for thermodynamcs A. Dmensons,

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

arxiv:quant-ph/ Jul 2002

arxiv:quant-ph/ Jul 2002 Lnear optcs mplementaton of general two-photon proectve measurement Andrze Grudka* and Anton Wóck** Faculty of Physcs, Adam Mckewcz Unversty, arxv:quant-ph/ 9 Jul PXOWRZVNDR]QDRODQG Abstract We wll present

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology Int. J. Pure Appl. Sc. Technol., 4() (03), pp. 5-30 Internatonal Journal of Pure and Appled Scences and Technology ISSN 9-607 Avalable onlne at www.jopaasat.n Research Paper Schrödnger State Space Matrx

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2 Physcs 607 Exam 1 Please be well-organzed, and show all sgnfcant steps clearly n all problems. You are graded on your wor, so please do not just wrte down answers wth no explanaton! Do all your wor on

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

7. Products and matrix elements

7. Products and matrix elements 7. Products and matrx elements 1 7. Products and matrx elements Based on the propertes of group representatons, a number of useful results can be derved. Consder a vector space V wth an nner product ψ

More information

THE SUMMATION NOTATION Ʃ

THE SUMMATION NOTATION Ʃ Sngle Subscrpt otaton THE SUMMATIO OTATIO Ʃ Most of the calculatons we perform n statstcs are repettve operatons on lsts of numbers. For example, we compute the sum of a set of numbers, or the sum of the

More information

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force. The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,

More information

CIVL 8/7117 Chapter 10 - Isoparametric Formulation 42/56

CIVL 8/7117 Chapter 10 - Isoparametric Formulation 42/56 CIVL 8/77 Chapter 0 - Isoparametrc Formulaton 4/56 Newton-Cotes Example Usng the Newton-Cotes method wth = ntervals (n = 3 samplng ponts), evaluate the ntegrals: x x cos dx 3 x x dx 3 x x dx 4.3333333

More information

Pressure Measurements Laboratory

Pressure Measurements Laboratory Lab # Pressure Measurements Laboratory Objectves:. To get hands-on experences on how to make pressure (surface pressure, statc pressure and total pressure nsde flow) measurements usng conventonal pressuremeasurng

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Lecture 12: Classification

Lecture 12: Classification Lecture : Classfcaton g Dscrmnant functons g The optmal Bayes classfer g Quadratc classfers g Eucldean and Mahalanobs metrcs g K Nearest Neghbor Classfers Intellgent Sensor Systems Rcardo Guterrez-Osuna

More information

Implicit Integration Henyey Method

Implicit Integration Henyey Method Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

Probability Theory (revisited)

Probability Theory (revisited) Probablty Theory (revsted) Summary Probablty v.s. plausblty Random varables Smulaton of Random Experments Challenge The alarm of a shop rang. Soon afterwards, a man was seen runnng n the street, persecuted

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x

More information

Programming Project 1: Molecular Geometry and Rotational Constants

Programming Project 1: Molecular Geometry and Rotational Constants Programmng Project 1: Molecular Geometry and Rotatonal Constants Center for Computatonal Chemstry Unversty of Georga Athens, Georga 30602 Summer 2012 1 Introducton Ths programmng project s desgned to provde

More information

Polynomials. 1 More properties of polynomials

Polynomials. 1 More properties of polynomials Polynomals 1 More propertes of polynomals Recall that, for R a commutatve rng wth unty (as wth all rngs n ths course unless otherwse noted), we defne R[x] to be the set of expressons n =0 a x, where a

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland

More information

Supplementary Notes for Chapter 9 Mixture Thermodynamics

Supplementary Notes for Chapter 9 Mixture Thermodynamics Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information