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Introduction to Introduction to Computational Chemistry Computational Chemistry Shubin Liu, Ph.D. Research Computing Center University of North Carolina at Chapel Hill

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Introduction to Computational Chemistry. Shubin Liu, Ph.D. Research Computing Center University of North Carolina at Chapel Hill. Outline. Introduction Methods in Computational Chemistry Ab Initio Semi-Empirical Density Functional Theory New Developments (QM/MM) Hands-on Exercises. - PowerPoint PPT Presentation

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Page 1: Introduction to Computational Chemistry

Introduction to Introduction to Computational Computational

Chemistry Chemistry

Introduction to Introduction to Computational Computational

Chemistry Chemistry Shubin Liu, Ph.D.

Research Computing Center

University of North Carolina at Chapel Hill

Page 2: Introduction to Computational Chemistry

its.unc.edu 2

OutlineOutline

Introduction

Methods in Computational Chemistry

•Ab Initio

•Semi-Empirical

•Density Functional Theory

•New Developments (QM/MM)

Hands-on ExercisesThe PDF format of this presentation is available here:http://www.unc.edu/~shubin/Courses/Comp_Chem.pdf

Page 3: Introduction to Computational Chemistry

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About UsAbout Us

ITS – Information Technology Services

• http://its.unc.edu

• http://help.unc.edu

• Physical locations: 401 West Franklin St. 211 Manning Drive

• 10 Divisions/Departments Information Security IT Infrastructure and Operations

Research Computing Center Teaching and Learning

User Support and Engagement Office of the CIO

Communication Technologies Communications

Enterprise Applications Finance and Administration

Page 4: Introduction to Computational Chemistry

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Research ComputingResearch Computing

Where and who are we and what do we do?• ITS Manning: 211 Manning Drive

• Website

http://its.unc.edu/research-computing.html

• Groups

Infrastructure -- Hardware

User Support -- Software

Engagement -- Collaboration

Page 5: Introduction to Computational Chemistry

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About MyselfAbout Myself

Ph.D. from Chemistry, UNC-CH

Currently Senior Computational Scientist @ Research Computing Center, UNC-CH

Responsibilities:

• Support Computational Chemistry/Physics/Material Science software

• Support Programming (FORTRAN/C/C++) tools, code porting, parallel computing, etc.

• Offer short courses on scientific computing and computational chemistry

• Conduct research and engagement projects in Computational Chemistry Development of DFT theory and concept tools

Applications in biological and material science systems

Page 6: Introduction to Computational Chemistry

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About YouAbout You

Name, department, research interest?

Any experience before with high performance computing?

Any experience before with computational chemistry research?

Do you have any real problem to solve with computational chemistry approaches?

Page 7: Introduction to Computational Chemistry

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Think BIG!!!Think BIG!!!

What is not chemistry?• From microscopic world, to nanotechnology, to daily life, to

environmental problems

• From life science, to human disease, to drug design

• Only our mind limits its boundary

What cannot computational chemistry deal with?• From small molecules, to DNA/proteins, 3D crystals and

surfaces

• From species in vacuum, to those in solvent at room temperature, and to those under extreme conditions (high T/p)

• From structure, to properties, to spectra (UV, IR/Raman, NMR, VCD), to dynamics, to reactivity

• All experiments done in labs can be done in silico

• Limited only by (super)computers not big/fast enough!

Page 8: Introduction to Computational Chemistry

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Central Theme of Computational Chemistry

Central Theme of Computational Chemistry

DYNAMICS

REACTIVITY

STRUCTURE CENTRAL DOGMA OF MOLECULAR BIOLOGY

SEQUENCE

STRUCTURE

DYNAMICS

FUNCTION

EVALUTION

Page 9: Introduction to Computational Chemistry

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Multiscale Hierarchy of Modeling

Multiscale Hierarchy of Modeling

Page 10: Introduction to Computational Chemistry

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What is Computational Chemistry?

What is Computational Chemistry?

Application of computational methods and algorithms in chemistry

• Quantum Mechanicali.e., via Schrödinger Equation

also called Quantum Chemistry

• Molecular Mechanical i.e., via Newton’s law F=ma

also Molecular Dynamics

• Empirical/Statisticale.g., QSAR, etc., widely used in clinical and medicinal chemistry

Focus TodayFocus Today

Ht

i ˆ

Ht

i ˆ

Page 11: Introduction to Computational Chemistry

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How Big Systems Can We Deal with?

How Big Systems Can We Deal with?

Assuming typical computing setup (number of CPUs, memory, disk space, etc.)

Ab initio method: ~100 atoms

DFT method: ~1000 atoms

Semi-empirical method: ~10,000 atoms

MM/MD: ~100,000 atoms

Page 12: Introduction to Computational Chemistry

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ij

n

1i ij

n

1i

N

1 i

2i

2

r

1

r

Z-

2m

h- H

n

ij

n

1i ij

n

1i r

1ih H

Starting Point: Time-Independent Schrodinger

Equation

Starting Point: Time-Independent Schrodinger

Equation

EH

Ht

i ˆ

Ht

i ˆ

Page 13: Introduction to Computational Chemistry

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Equation to Solve in ab initio Theory

Equation to Solve in ab initio Theory

EH

Known exactly:3N spatial variables

(N # of electrons)

To be approximated:1. variationally2. perturbationally

Page 14: Introduction to Computational Chemistry

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Hamiltonian for a Molecule

Hamiltonian for a Molecule

kinetic energy of the electrons kinetic energy of the nuclei electrostatic interaction between the electrons

and the nuclei electrostatic interaction between the electrons electrostatic interaction between the nuclei

nuclei

BA AB

BAelectrons

ji ij

nuclei

A iA

Aelectrons

iA

nuclei

A Ai

electrons

i e

R

ZZe

r

e

r

Ze

mm

22

22

22

2

22ˆ H

nuclei

BA AB

BAelectrons

ji ij

nuclei

A iA

Aelectrons

iA

nuclei

A Ai

electrons

i e

R

ZZe

r

e

r

Ze

mm

22

22

22

2

22ˆ H

Page 15: Introduction to Computational Chemistry

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Ab Initio Methods

Ab Initio Methods

Accurate treatment of the electronic distribution using the full Schrödinger equation

Can be systematically improved to obtain chemical accuracy

Does not need to be parameterized or calibrated with respect to experiment

Can describe structure, properties, energetics and reactivity

What does “ab intio” mean?

• Start from beginning, with first principle Who invented the word of the “ab initio” method?

• Bob Parr of UNC-CH in 1950s; See Int. J. Quantum Chem. 37(4), 327(1990) for details.

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Three Approximations Three Approximations

Born-Oppenheimer approximation

• Electrons act separately of nuclei, electron and nuclear coordinates are independent of each other, and thus simplifying the Schrödinger equation

Independent particle approximation

• Electrons experience the ‘field’ of all other electrons as a group, not individually

• Give birth to the concept of “orbital”, e.g., AO, MO, etc.

LCAO-MO approximation

• Molecular orbitals (MO) can be constructed as linear combinations of atom orbitals, to form Slater determinants

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Born-Oppenheimer Approximation

Born-Oppenheimer Approximation

the nuclei are much heavier than the electrons and move more slowly than the electrons

freeze the nuclear positions (nuclear kinetic energy is zero in the electronic Hamiltonian)

calculate the electronic wave function and energy

E depends on the nuclear positions through the nuclear-electron attraction and nuclear-nuclear repulsion terms

E = 0 corresponds to all particles at infinite separation

nuclei

BA AB

BAelectrons

ji ij

nuclei

A iA

Aelectrons

ii

electrons

i eel r

ZZe

r

e

r

Ze

m

2222

2

2ˆ H

nuclei

BA AB

BAelectrons

ji ij

nuclei

A iA

Aelectrons

ii

electrons

i eel r

ZZe

r

e

r

Ze

m

2222

2

2ˆ H

d

dEE

elel

elelel

elelel *

* ˆ,ˆ

HH

d

dEE

elel

elelel

elelel *

* ˆ,ˆ

HH

Page 18: Introduction to Computational Chemistry

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Approximate Wavefunctions

Approximate Wavefunctions

Construction of one-electron functions (molecular orbitals, MO’s) as linear combinations of one-electron atomic basis functions (AOs) MO-LCAO approach.

Construction of N-electron wavefunction as linear combination of anti-symmetrized products of MOs (these anti-symmetrized products are denoted as Slater-determinants).

down)-(spin

up)-(spin ;

1

iiu ik

N

kklil rq

down)-(spin

up)-(spin ;

1

iiu ik

N

kklil rq

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The Slater DeterminantThe Slater Determinant

zcbazcba

zzzz

cccc

bbbb

aaaa

n

zcbazcban

zcba

n

n

n

n

n

nn

n

321

321

321

321

321

312321

321 Α̂

!1

!1

zcbazcba

zzzz

cccc

bbbb

aaaa

n

zcbazcban

zcba

n

n

n

n

n

nn

n

321

321

321

321

321

312321

321 Α̂

!1

!1

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The Two Extreme Cases

The Two Extreme Cases

One determinant: The Hartree–Fock method.

All possible determinants: The full CI method.

NN 321 321HF NN 321 321HF

There are N MOs and each MO is a linear combination of N AOs. Thus, there are nN coefficients ukl, which are determined by making stationary the functional:

The ij are Lagrangian multipliers.

N

lkijljklki

N

jiij uSuHE

1,

*

1,HFHFHF ˆ

N

lkijljklki

N

jiij uSuHE

1,

*

1,HFHFHF ˆ

Page 21: Introduction to Computational Chemistry

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The Full CI MethodThe Full CI Method

The full configuration interaction (full CI) method expands the wavefunction in terms of all possible Slater determinants:

There are possible ways to choose n molecular orbitals from a set of 2N AO basis functions.

The number of determinants gets easily much too large. For example:

n

N2

1ˆ ;

2

1,CICICI

2

1CI

cScHEc

n

N

*n

N

1ˆ ;

2

1,CICICI

2

1CI

cScHEc

n

N

*n

N

91010

40

91010

40

Davidson’s method can be used to find one or a few eigenvalues of a matrix of rank 109.

Page 22: Introduction to Computational Chemistry

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NN 321 321HF NN 321 321HF

N

lkijljklki

N

jiij uSuHE

1,

*

1,HFHFHF ˆ

N

lkijljklki

N

jiij uSuHE

1,

*

1,HFHFHF ˆ

N

ilikikl

N

lkklmn

N

nmmn uuPnlmkPhPEH

1

*

1,21

1,nucHFHF ; ˆ

N

ilikikl

N

lkklmn

N

nmmn uuPnlmkPhPEH

1

*

1,21

1,nucHFHF ; ˆ

0HF

Euki

0HF

Euki

Hartree–Fock equations

The Hartree–Fock MethodThe Hartree–Fock Method

Page 23: Introduction to Computational Chemistry

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|S Overlap integral

|

2

1|PHF

ii

occ

i

cc2PDensity Matrix

SF iii cc

The Hartree–Fock Method

The Hartree–Fock Method

Page 24: Introduction to Computational Chemistry

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1. Choose start coefficients for MO’s

2. Construct Fock Matrix with coefficients

3. Solve Hartree-Fock-Roothaan equations

4. Repeat 2 and 3 until ingoing and outgoing

coefficients are the same

Self-Consistent-Field (SCF)

Self-Consistent-Field (SCF)

SF iii cc

Page 25: Introduction to Computational Chemistry

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Semi-empirical methods(MNDO, AM1, PM3, etc.)

Semi-empirical methods(MNDO, AM1, PM3, etc.)

Full CIFull CI

perturbational hierarchy(CASPT2, CASPT3)

perturbational hierarchy(CASPT2, CASPT3)

perturbational hierarchy(MP2, MP3, MP4, …)

perturbational hierarchy(MP2, MP3, MP4, …)

excitation hierarchy(MR-CISD)

excitation hierarchy(MR-CISD)

excitation hierarchy(CIS,CISD,CISDT,...)

(CCS, CCSD, CCSDT,...)

excitation hierarchy(CIS,CISD,CISDT,...)

(CCS, CCSD, CCSDT,...)

Multiconfigurational HF(MCSCF, CASSCF)

Multiconfigurational HF(MCSCF, CASSCF)

Hartree-Fock(HF-SCF)

Hartree-Fock(HF-SCF)

Ab Initio MethodsAb Initio Methods

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Who’s WhoWho’s Who

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Size vs AccuracySize vs Accuracy

Number of atoms

0.1

1

10

1 10 100 1000

Acc

urac

y (k

cal/m

ol) Coupled-cluster,

Multireference

Nonlocal density functional,Perturbation theory

Local density functional,Hartree-Fock

Semiempirical Methods

Full CI

Page 28: Introduction to Computational Chemistry

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ROO,e= 291.2 pm 96.4 pm

95.7 pm 95.8 pm

symmetry: Cs

Equilibrium structure of (HEquilibrium structure of (H22O)O)22

W.K., J.G.C.M. van Duijneveldt-van de Rijdt, and W.K., J.G.C.M. van Duijneveldt-van de Rijdt, and

F.B. van Duijneveldt, F.B. van Duijneveldt, Phys. Chem. Chem. Phys.Phys. Chem. Chem. Phys. 22, 2227 (2000)., 2227 (2000).

Experimental [J.A. Odutola and T.R. Dyke, J. Chem. Phys 72, 5062 (1980)]: ROO

2 ½ = 297.6 ± 0.4 pm

SAPT-5s potential [E.M. Mas et al., J. Chem. Phys. 113, 6687 (2000)]: ROO

2 ½ – ROO,e= 6.3 pm ROO,e(exptl.) = 291.3 pm

AN EXAMPLE

Page 29: Introduction to Computational Chemistry

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Experimental and Computed Enthalpy Changes He in

kJ/mol

Experimental and Computed Enthalpy Changes He in

kJ/mol

Exptl. CCSD(T) SCF G2 DFT

CH4 CH2 + H2 544(2) 542 492 534 543

C2H4 C2H2 + H2 203(2) 204 214 202 208

H2CO CO + H2 21(1) 22 3 17 34

2 NH3 N2 + 3 H2 164(1) 162 149 147 166

2 H2O H2O2 + H2 365(2) 365 391 360 346

2 HF F2 + H2 563(1) 562 619 564 540

Exptl. CCSD(T) SCF G2 DFT

CH4 CH2 + H2 544(2) 542 492 534 543

C2H4 C2H2 + H2 203(2) 204 214 202 208

H2CO CO + H2 21(1) 22 3 17 34

2 NH3 N2 + 3 H2 164(1) 162 149 147 166

2 H2O H2O2 + H2 365(2) 365 391 360 346

2 HF F2 + H2 563(1) 562 619 564 540

Gaussian-2 (G2) method of Pople and co-workers is a combination of MP2 and QCISD(T)

Page 30: Introduction to Computational Chemistry

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LCAO Basis FunctionsLCAO Basis Functions

’s, which are atomic orbitals, are called basis functions

usually centered on atoms

can be more general and more flexible than atomic orbital functions

larger number of well chosen basis functions yields more accurate approximations to the molecular orbitals

c

c

Page 31: Introduction to Computational Chemistry

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Basis FunctionsBasis Functions

Slaters (STO)

Gaussians (GTO)

Angular part *

Better behaved than Gaussians

2-electron integrals hard

2-electron integrals simpler

Wrong behavior at nucleus

Decrease too fast with r

r)exp( r)exp(

2nml rexp*zyx 2nml rexp*zyx

Page 32: Introduction to Computational Chemistry

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Contracted Gaussian Basis Set

Contracted Gaussian Basis Set

Minimal

STO-nG

Split Valence: 3-21G,4-31G,6-31G

• Each atom optimized STO is fit with n GTO’s

• Minimum number of AO’s needed

• Each atom optimized STO is fit with n GTO’s

• Minimum number of AO’s needed

• Contracted GTO’s optimized per atom• Doubling of the number of valence AO’s

• Contracted GTO’s optimized per atom• Doubling of the number of valence AO’s

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Polarization / Diffuse Functions

Polarization / Diffuse Functions

Polarization: Add AO with higher angular momentum (L) to give more flexibility

Example: 3-21G*, 6-31G*, 6-31G**, etc.

Diffusion: Add AO with very small exponents for systems with very diffuse electron densities such as anions or excited statesExample: 6-31+G*, 6-311++G**

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Correlation-Consistent Basis Functions

Correlation-Consistent Basis Functions

a family of basis sets of increasing size

can be used to extrapolate to the basis set limit

cc-pVDZ – DZ with d’s on heavy atoms, p’s on H

cc-pVTZ – triple split valence, with 2 sets of d’s and one set of f’s on heavy atoms, 2 sets of p’s and 1 set of d’s on hydrogen

cc-pVQZ, cc-pV5Z, cc-pV6Z

can also be augmented with diffuse functions (aug-cc-pVXZ)

Page 35: Introduction to Computational Chemistry

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Pseudopotentials, Effective Core Potentials

Pseudopotentials, Effective Core Potentials

core orbitals do not change much during chemical interactions

valence orbitals feel the electrostatic potential of the nuclei and of the core electrons

can construct a pseudopotential to replace the electrostatic potential of the nuclei and of the core electrons

reduces the size of the basis set needed to represent the atom (but introduces additional approximations)

for heavy elements, pseudopotentials can also include of relativistic effects that otherwise would be costly to treat

Page 36: Introduction to Computational Chemistry

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Correlation EnergyCorrelation Energy

HF does not include correlations anti-parallel electrons

Eexact – EHF = Ecorrelation

Post HF Methods:

• Configuration Interaction (CI, MCSCF, CCSD)

• Møller-Plesset Perturbation series (MP2, MP4)

Density Functional Theory (DFT)

Page 37: Introduction to Computational Chemistry

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Configuration-Interaction (CI)

Configuration-Interaction (CI)

In Hartree-Fock theory, the n-electron wavefunction is approximated by one single Slater-determinant, denoted as:

This determinant is built from n orthonormal spin-orbitals. The spin-orbitals that form are said to be occupied. The other orthonormal spin-orbitals that follow from the Hartree-Fock calculation in a given one-electron basis set of atomic orbitals (AOs) are known as virtual orbitals. For simplicity, we assume that all spin-orbitals are real.

In electron-correlation or post-Hartree-Fock methods, the wavefunction is expanded in a many-electron basis set that consists of many determinants. Sometimes, we only use a few determinants, and sometimes, we use millions of them:

In this notation, is a Slater-

determinant that is obtained by replacing a certain number of

occupied orbitals by virtual ones.

Three questions: 1. Which determinants should we include? 2. How do we determine the expansion coefficients? 3. How do we evaluate the energy (or other properties)?

HF

HF

cHFCI

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Truncated configuration interaction: CIS, CISD, CISDT, etc.

Truncated configuration interaction: CIS, CISD, CISDT, etc.

We start with a reference wavefunction, for example the Hartree-Fock determinant.

We then select determinants for the wavefunction expansion by substituting orbitals of the reference determinant by orbitals that are not occupied in the reference state (virtual orbitals).

Singles (S) indicate that 1 orbital is replaced, doubles (D) indicate 2 replacements, triples (T) indicate 3 replacements, etc., leading to CIS, CISD, CISDT, etc.

NNkji 321HF NNkji 321HF

etc. ,321 ,321 NN NkbaabijNkja

ai etc. ,321 ,321 NN Nkba

abijNkja

ai

Page 39: Introduction to Computational Chemistry

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Truncated Configuration Interaction

Truncated Configuration Interaction

Level of excitation

Number of parameters

Example

CIS n (2N – n) 300

CISD … + [n (2N – n)] 2 78,600

CISDT …+ [n (2N – n)] 3 18106

… … …

Full CI

n

N2 109

Number of linear variational parametersin truncated CI for n = 10 and 2N = 40.

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Multi-Configuration Self-Consistent Field (MCSCF)

Multi-Configuration Self-Consistent Field (MCSCF)

The MCSCF wavefunctions consists of a few selected determinants or CSFs. In the MCSCF method, not only the linear weights of the determinants are variationally optimized, but also the orbital coefficients.

One important selection is governed by the full CI space spanned by a number of prescribed active orbitals (complete active space, CAS). This is the CASSCF method. The CASSCF wavefunction contains all determinants that can be constructed from a given set of orbitals with the constraint that some specified pairs of - and -spin-orbitals must occur in all determinants (these are the inactive doubly occupied spatial orbitals).

Multireference CI wavefunctions are obtained by applying the excitation operators to the individual CSFs or determinants of the MCSCF (or CASSCF) reference wave function.

kCCck

kkk )ˆˆ(CISD-MR 21 k

kk

kk kdCkCc 21ˆ)ˆ(MRCI-IC

Internally-contracted MRCI:

Page 41: Introduction to Computational Chemistry

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Coupled-Cluster Theory

Coupled-Cluster Theory

System of equations is solved iteratively (the convergence is accelerated by utilizing Pulay’s method, “direct inversion in the iterative subspace”, DIIS).

CCSDT model is very expensive in terms of computer resources. Approximations are introduced for the triples: CCSD(T), CCSD[T], CCSD-T.

Brueckner coupled-cluster (e.g., BCCD) methods use Brueckner orbitals that are optimized such that singles don’t contribute.

By omitting some of the CCSD terms, the quadratic CI method (e.g., QCISD) is obtained.

Page 42: Introduction to Computational Chemistry

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Møller-Plesset Perturbation Theory

Møller-Plesset Perturbation Theory

The Hartree-Fock function is an eigenfunction of the

n-electron operator .

We apply perturbation theory as usual after decomposing the Hamiltonian into two parts:

More complicated with more than one reference determinant (e.g., MR-PT, CASPT2, CASPT3, …)

FHH

FH

HHH

ˆˆˆ

ˆˆ

ˆˆ

1

0

10

FHH

FH

HHH

ˆˆˆ

ˆˆ

ˆˆ

1

0

10

MP2, MP3, MP4, …etc.number denotes order to which energy is computed (2n+1 rule)

Page 43: Introduction to Computational Chemistry

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Semi-Empirical MethodsSemi-Empirical Methods

These methods are derived from the Hartee–Fock model, that is, they are MO-LCAO methods.

They only consider the valence electrons. A minimal basis set is used for the valence shell. Integrals are restricted to one- and two-center integrals and

subsequently parametrized by adjusting the computed results to experimental data.

Very efficient computational tools, which can yield fast quantitative estimates for a number of properties. Can be used for establishing trends in classes of related molecules, and for scanning a computational poblem before proceeding with high-level treatments.

A not of elements, especially transition metals, have not be parametrized

Page 44: Introduction to Computational Chemistry

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Semi-Empirical MethodsSemi-Empirical Methods

Number 2-electron integrals () is n4/8, n = number of basis

functions

Treat only valence electrons explicit

Neglect large number of 2-electron integrals

Replace others by empirical parameters

Models:

• Complete Neglect of Differential Overlap (CNDO)

• Intermediate Neglect of Differential Overlap (INDO/MINDO)

• Neglect of Diatomic Differential Overlap (NDDO/MNDO, AM1, PM3)

Page 45: Introduction to Computational Chemistry

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AB

ABVUH

AB

ABVUH Ufrom atomic spectraVvalue per atom pair

0H 0H on the same atom

SH AB SH AB BAAB 21 BAAB 21

One parameter per element

Approximations of 1-e integrals

Approximations of 1-e integrals

Page 46: Introduction to Computational Chemistry

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Popular DFTPopular DFT

Noble prize in Chemistry, 1998

In 1999, 3 of top 5 most cited journal articles in chemistry (1st, 2nd, & 4th)

In 2000-2004, top 3 most cited journal articles in chemistry

In 2005, 4 of top 5 most cited journal articles in chemistry

• 1st, Becke’s hybrid exchange functional (1993)

• 2nd, Lee-Yang-Parr correlation functional (1988)

• 3rd, Becke’s exchange functional (1988)

• 5th, PBE correlation functional (1996)

http://www.cas.org/spotlight/bchem.html

Page 47: Introduction to Computational Chemistry

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Advantageous DFTAdvantageous DFT

Computationally efficient

Hartree-Fock-like computationally (~N3) , but included electron correlation effects

Theoretically rigorous

Two Hohenberg-Kohn theorems guarantee an exact theory in ground state

Conceptually insightful

Provides basis to understand chemical reactivity and other chemical properties

Page 48: Introduction to Computational Chemistry

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Brief History of DFTBrief History of DFT

First speculated 1920’

• Thomas-Fermi (kinetic energy) and Dirac (exchange energy) formulas

Officially born in 1964 with Hohenberg- Kohn’s original proof

GEA/GGA formulas available later 1980’

Becoming popular later 1990’

Pinnacled in 1998 with a chemistry Nobel prize

Page 49: Introduction to Computational Chemistry

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What could expect from DFT?

What could expect from DFT?

LDA, ~20 kcal/mol error in energy

GGA, ~3-5 kcal/mol error in energy

G2/G3 level, some systems, ~1kcal/mol

Good at structure, spectra, & other properties predictions

Poor in H-containing systems, TS, spin, excited states, etc.

Page 50: Introduction to Computational Chemistry

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Density Functional TheoryDensity Functional Theory

Hohenberg-Kohn theorems:

• “Given the external potential, we know the ground-state energy of the molecule when we know the electron density ”.

• The energy density functional is variational.

EEnergy

EEnergy

00 ifEE

Page 51: Introduction to Computational Chemistry

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Can we work with E[]?

Can we work with E[]?

How do we compute the energy if the density is known?

The Coulombic interactions are easy to compute:

But what about the kinetic energy TS[] and exchange-correlation energy Exc[]?

How do we determine the density variationally? We must make sure that the density is derived from a proper N-electron wavefunction (N-representability problem) and a given external potential vext (v-representability problem).

, , , 2

1Coulombextextnuc rr

rr

rrrrr

ddEdVEr

ZZE

nuclei

BA AB

BA

Page 52: Introduction to Computational Chemistry

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The Kohn-Sham (KS) Scheme

The Kohn-Sham (KS) Scheme

Suppose, we know the exact density.

Then, we can formulate a Slater determinant that generates this exact density (= Slater determinant of system of N non-interacting electrons with same density ).

We know how to compute the kinetic energy from a Slater determinant.

The N-representability problem will then be solved (density is obtained from an anti-symmetric N-electron function).

Then, the only thing unknown is to calculate Exc[].

mn

N

nmmn

n

iiin tPtTEdddn

1,1

kin32

2

1 ˆ ˆ , rrrr

Page 53: Introduction to Computational Chemistry

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Kohn-Sham EquationsKohn-Sham Equations

,|)(|)(

,)(

,||

)()(

,||

)(

,2

1

and

)()()(ˆ

where

2

3

2

nknknk

xcxc

ee

a a

ane

xceene

nknknk

rfr

ErV

rdrr

rrV

Rr

ZrV

K

rVrVrVKH

H

The Only Unknown

Page 54: Introduction to Computational Chemistry

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All about Exchange-Correlation

Energy Density Functional

All about Exchange-Correlation

Energy Density Functional

LDA – f is a function of (r) only

GGA – f is a function of (r) and ∇(r)

Mega-GGA – f is also a function of ts(r), kinetic energy density

Hybrid – f is GGA functional with extra contribution from Hartree-Fock exchange energy

rrrr dfEXC ,,, 2

Page 55: Introduction to Computational Chemistry

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LDA FunctionalsLDA Functionals

Thomas-Fermi formula (Kinetic) – 1 parameter

Slater form (exchange) – 1 parameter

Wigner correlation – 2 parameters

3/223/5 310

3, FFTF CdCT rr

3/13/23/13/4 438

3, XX

SX CdCE rr

rr

r

db

aEWC 3/1

3/2

1

Page 56: Introduction to Computational Chemistry

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GGA Functional: BLYPGGA Functional: BLYP

Two most well-known functionals are the Becke exchange functional Ex[] with 2 extra parameters &

the Lee-Yang-Parr correlation functional Ec[] with 4 parameters a-d

Together, they constitute the BLYP functional:

rrrr dedeEEE cxcxxc , , LYPBLYPBBLYP

3/4

2

2

23/4 ,1

LDA

XBX EE

rdettCbd

aE cWWF

LYPc

3/123/53/23/1 18

1

9

12

1

1

Page 57: Introduction to Computational Chemistry

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Hybrid Functional: B3LYP

Hybrid Functional: B3LYP

FxB and Fc

LYP have been fitted against ab initio data (one could call this computational approach a “semi-ab-initio method”).

In a very popular variant, denoted B3LYP, the functional is augmented with a little of Hartree-Fock-type exchange:

nlkmPPbEEaEN

lkkl

N

nmmncxxc

1,1,

LYPBB3LYP

Page 58: Introduction to Computational Chemistry

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Other Popular Functionals

Other Popular Functionals

LDA

• SVWN

GGA

• PBE

• PW91

• HCTH

• Mega-GGA

Hybrid functionals

Page 59: Introduction to Computational Chemistry

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Disadvantageous DFTDisadvantageous DFT

ground-state theory only

universal functional unknown

no systematic way to improve approximations like LDA, GGA, etc.

Page 60: Introduction to Computational Chemistry

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Examples DFT vs. HFExamples DFT vs. HF

Hydrogen molecules - using the LSDA (LDA)

Page 61: Introduction to Computational Chemistry

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DFT Reactivity IndicesDFT Reactivity Indices

Electronegativity (chemical potential)

Hardness / Softness

HSAB Principle and Maximum Hardness Principle

2LUMOHOMO

N

E

2LUMOHOMO

N

E

/1,22

2

SN

E HOMOLUMO

/1,22

2

SN

E HOMOLUMO

FOR MORE INFO...

Parr & Yang, Density Functional Theory of Atoms and Molecules (Oxford Univ. Press, New York, 1989).

Page 62: Introduction to Computational Chemistry

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DFT Concept: Fukui Function

DFT Concept: Fukui Function

rrr 1 NNf rrr 1

NNf

Fukui function

N

fr

r

N

fr

r

Nucleophilic attack

rrr NNf

1 rrr NNf

1

Electrophilic attack

Free radical activity

2

rrr

fff

2

rrr

fff

Page 63: Introduction to Computational Chemistry

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Fukui Function: An Example

Fukui Function: An Example

Page 64: Introduction to Computational Chemistry

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New Development: Electrophilicity IndexNew Development:

Electrophilicity Index

Physical meaning: suppose an electrophile is immersed in an electron sea

The maximal electron flow and accompanying energy decrease are

2

2

1NNE

2

2

2

2

max N

2

2

minE

Parr, Szentpaly, Liu, J. Am. Chem. Soc. 121, 1922(1999).

Page 65: Introduction to Computational Chemistry

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New Development:Philicty and Spin-

Philicity

New Development:Philicty and Spin-

Philicity

Philicity: defined as ·f(r)• Chattaraj, Maiti, & Sarkar, J. Phys. Chem. A 107,

4973(2003)

• Still a very controversial concept, see JPCA 108, 4934(2004); Chattaraj, et al. JPCA, in press.

Spin-Philicity: defined same as but in spin resolution• Perez, Andres, Safont, Tapia, & Contreras. J. Phys.

Chem. A 106, 5353(2002)

Page 66: Introduction to Computational Chemistry

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New Development: Steric Effect

New Development: Steric Effect

r

r

rdEs

2

8

1

r

r

r

r

rr

22

4

1

8

1

s

s

E

S.B. Liu, J. Chem. Phys. 126, 244103(2007).

Page 67: Introduction to Computational Chemistry

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BLACK CIRCLE: Total Energy Difference; RED SQUARE: Electrostatic; GREEN DIMOND: Quantum; BLUE TRIANGLE: Steric

New Development: Steric Effect

S.B. Liu and N. Govind, J. Phys. Chem. A 112, 6690(2008).

Ethane H-C-C-H Dihedral Angle Rotation

Page 68: Introduction to Computational Chemistry

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What’s New: QM/MMWhat’s New: QM/MM

Focus: Enzyme catalytic reactions

Strategy: QM for active site and MM for the rest

Main Issue: boundary between QM and MM.

Models: Link-atom, pseudo-orbital, pseudo-bond, etc.

Limitation: active site should be small;

• long-range charge transfer

• conformation change (protein folding)

Page 69: Introduction to Computational Chemistry

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QM/MM Example: Triosephosphate Isomerase (TIM)

QM/MM Example: Triosephosphate Isomerase (TIM)

494 Residues, 4033 Atoms, PDB ID: 7TIM

Function: DHAP (dihydroxyacetone phosphate) GAP (glyceraldehyde 3-phosphate)

GAP

DHAPH2O

Page 70: Introduction to Computational Chemistry

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Glu 165 (the catalytic base), His 95 (the proton shuttle)

DHAP GAP

TIM 2-step 2-residue Mechanism

TIM 2-step 2-residue Mechanism

Page 71: Introduction to Computational Chemistry

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QM/MM: 1st Step of TIM Mechanism

QM/MM: 1st Step of TIM Mechanism

QM/MM size: 6051 atoms QM Size: 37 atoms

QM: Gaussian’98 Method: HF/3-21G

MM: Tinker Force field: AMBER all-atom

Number of Water: 591 Model for Water: TIP3P

MD details: 20x20x20 Å3 box, optimize until the RMS energy

gradient less than 1.0 kcal/mol/Å. 20 psec MD. Time step 2fs.

SHAKE, 300 K, short range cutoff 8 Å, long range cutoff 15 Å.

Page 72: Introduction to Computational Chemistry

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QM/MM: Transition State

QM/MM: Transition State

=====================

Energy Barrier (kcal/mol)

-------------------------------------

QM/MM 21.9

Experiment 14.0

=====================

Page 73: Introduction to Computational Chemistry

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What’s New: Linear Scaling O(N) MethodWhat’s New: Linear

Scaling O(N) Method

Numerical Bottlenecks:

• diagonalization ~N3

• orthonormalization ~N3

• matrix element evaluation ~N2-N4

Computational Complexity: N log N

Theoretical Basis: near-sightedness of density matrix or orbitals

Strategy:

• sparsity of localized orbital or density matrix

• direct minimization with conjugate gradient

Models: divide-and-conquer and variational methods

Applicability: ~10,000 atoms, dynamics

Page 74: Introduction to Computational Chemistry

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0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900

Atoms

CP

U s

ec

on

ds

pe

r C

G s

tep

OLMONOLMO

Diagonalization

O(N) Method: An Example

O(N) Method: An Example

Page 75: Introduction to Computational Chemistry

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What Else … ? What Else … ?

Solvent effect

•Implicit model vs. explicit model

Relativity effect

Transition state

Excited states

Temperature and pressure

Solid states (periodic boundary condition)

Dynamics (time-dependent)

Page 76: Introduction to Computational Chemistry

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Limitations and Strengths of ab initio

quantum chemistry

Limitations and Strengths of ab initio

quantum chemistry

Page 77: Introduction to Computational Chemistry

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Popular QM codesPopular QM codes

Gaussian (Ab Initio, Semi-empirical, DFT)

Gamess-US/UK (Ab Initio, DFT)

Spartan (Ab Initio, Semi-empirical, DFT)

NWChem (Ab Initio, DFT, MD, QM/MM)

MOPAC/2000 (Semi-Empirical)

DMol3/CASTEP (DFT)

Molpro (Ab initio)

ADF (DFT)

ORCA (DFT)

Page 78: Introduction to Computational Chemistry

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Reference BooksReference Books

Computational Chemistry (Oxford Chemistry Primer) G. H. Grant and W. G. Richards (Oxford University Press)

Molecular Modeling – Principles and Applications, A. R. Leach (Addison Wesley Longman)

Introduction to Computational Chemistry, F. Jensen (Wiley)

Essentials of Computational Chemistry – Theories and Models, C. J. Cramer (Wiley)

Exploring Chemistry with Electronic Structure Methods, J. B. Foresman and A. Frisch (Gaussian Inc.)

Page 79: Introduction to Computational Chemistry

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Questions & Comments Questions & Comments

Please direct comments/questions about research computing to

E-mail: [email protected]

Please direct comments/questions pertaining to this presentation to

E-Mail: [email protected]

Please direct comments/questions about research computing to

E-mail: [email protected]

Please direct comments/questions pertaining to this presentation to

E-Mail: [email protected]

The PDF format of this presentation is available here:http://www.unc.edu/~shubin/Courses/Comp_Chem.pdf

Page 80: Introduction to Computational Chemistry

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Hands-on: Part IHands-on: Part I

Purpose: to get to know the available ab initio and semi-empirical methods in the Gaussian 03 / GaussView package

• ab initio methods Hartree-Fock

MP2

CCSD

• Semiempirical methods AM1

Page 81: Introduction to Computational Chemistry

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Hands-on: Part IIHands-on: Part II

Purpose: To use LDA and GGA DFT methods to calculate IR/Raman spectra in vacuum and in solvent. To build QM/MM models and then use DFT methods to calculate IR/Raman spectra

• DFT LDA (SVWN)

GGA (B3LYP)

• QM/MM