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Computational Modeling of Macromolecular Systems Dr. GuanHua CHEN Department of Chemistry University of Hong Kong

Computational Modeling of Macromolecular Systems

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Computational Modeling of Macromolecular Systems. Dr. GuanHua CHEN Department of Chemistry University of Hong Kong. Computational Chemistry. Quantum Chemistry Schr Ö dinger Equation H  = E  Molecular Mechanics F = Ma F : Force Field. Computational Chemistry Industry. Company. - PowerPoint PPT Presentation

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Page 1: Computational Modeling of Macromolecular Systems

Computational Modeling of Macromolecular Systems

Dr. GuanHua CHEN

Department of Chemistry

University of Hong Kong

Page 2: Computational Modeling of Macromolecular Systems

Computational Chemistry

• Quantum Chemistry

SchrÖdinger Equation

H = E• Molecular Mechanics

F = Ma

F : Force Field

Page 3: Computational Modeling of Macromolecular Systems

Computational Chemistry Industry

Company Software

Gaussian Inc. Gaussian 94, Gaussian 98Schrödinger Inc. Jaguar Wavefunction SpartanQ-Chem Q-ChemMolecular Simulation Inc. (MSI) InsightII, Cerius2, modelerHyperCube HyperChem

Applications: material discovery, drug design & research

R&D in Chemical & Pharmaceutical industries in 2000: US$ 80 billionSales of Scientific Computing in 2000: > US$ 200 million

Page 4: Computational Modeling of Macromolecular Systems

Cytochrome c (involved in the ATP synthesis)

heme

Cytochrome c is a peripheral membrane protein involved in the long distance electron transfers

1997 Nobel Prizein Biology:

ATP Synthase inMitochondria

Page 5: Computational Modeling of Macromolecular Systems

Simulation of a pair of polypeptides

Duration: 100 ps. Time step: 1 ps (Ng, Yokojima & Chen, 2000)

Page 6: Computational Modeling of Macromolecular Systems

Protein Dynamics

Theoretician leaded the way ! (Karplus at Harvard U.)

1. Atomic Fluctuations 10-15 to 10-11 s; 0.01 to 1 Ao

2. Collective Motions

10-12 to 10-3 s; 0.01 to >5 Ao

3. Conformational Changes10-9 to 103 s; 0.5 to >10 Ao

Page 7: Computational Modeling of Macromolecular Systems

Scanning Tunneling Microscope

Manipulating Atoms by Hand

Page 8: Computational Modeling of Macromolecular Systems

Large Gear Drives Small Gear

G. Hong et. al., 1999

Page 9: Computational Modeling of Macromolecular Systems

Calculated Electron distribution at equator

Page 10: Computational Modeling of Macromolecular Systems

The electron density around the vitamin C molecule. The colors show the electrostatic potential with the negative areas shaded in red and the positive in blue.

Vitamin C

Page 11: Computational Modeling of Macromolecular Systems

Molecular Mechanics (MM) Method

F = MaF : Force Field

Page 12: Computational Modeling of Macromolecular Systems

Molecular Mechanics Force Field

• Bond Stretching Term

• Bond Angle Term

• Torsional Term

• Non-Bonding Terms: Electrostatic Interaction & van der Waals Interaction

Page 13: Computational Modeling of Macromolecular Systems

Bond Stretching PotentialEb = 1/2 kb (l)2

where, kb : stretch force constantl : difference between equilibrium & actual bond length

Two-body interaction

Page 14: Computational Modeling of Macromolecular Systems

Bond Angle Deformation PotentialEa = 1/2 ka ()2

where, ka : angle force constant

: difference between equilibrium & actual bond angle

Three-body interaction

Page 15: Computational Modeling of Macromolecular Systems

Periodic Torsional Barrier PotentialEt = (V/2) (1+ cosn )where, V : rotational barrier

: torsion angle n : rotational degeneracy

Four-body interaction

Page 16: Computational Modeling of Macromolecular Systems

Non-bonding interaction

van der Waals interactionfor pairs of non-bonded atoms

Coulomb potential

for all pairs of charged atoms

Page 17: Computational Modeling of Macromolecular Systems

MM Force Field Types

• MM2 Small molecules

• AMBER Polymers

• CHAMM Polymers

• BIO Polymers

• OPLS Solvent Effects

Page 18: Computational Modeling of Macromolecular Systems

######################################################## ## ## ## TINKER Atom Class Numbers to CHARMM22 Atom Names ## ## ## ## 1 HA 11 CA 21 CY 31 NR3 ## ## 2 HP 12 CC 22 CPT 32 NY ## ## 3 H 13 CT1 23 CT 33 NC2 ## ## 4 HB 14 CT2 24 NH1 34 O ## ## 5 HC 15 CT3 25 NH2 35 OH1 ## ## 6 HR1 16 CP1 26 NH3 36 OC ## ## 7 HR2 17 CP2 27 N 37 S ## ## 8 HR3 18 CP3 28 NP 38 SM ## ## 9 HS 19 CH1 29 NR1 ## ## 10 C 20 CH2 30 NR2 ## ## ## ########################################################

CHAMM FORCE FIELD FILE

Page 19: Computational Modeling of Macromolecular Systems

atom 1 1 HA "Nonpolar Hydrogen" 1 1.0081atom 2 2 HP "Aromatic Hydrogen" 1 1.0081atom 3 3 H "Peptide Amide HN" 1 1.0081atom 4 4 HB "Peptide HCA" 1 1.0081atom 5 4 HB "N-Terminal HCA" 1 1.0081atom 6 5 HC "N-Terminal Hydrogen" 1 1.0081atom 7 5 HC "N-Terminal PRO HN" 1 1.0081atom 8 3 H "Hydroxyl Hydrogen" 1 1.0081atom 9 3 H "TRP Indole HE1" 1 1.0081atom 10 3 H "HIS+ Ring NH" 1 1.0081atom 11 3 H "HISDE Ring NH" 1 1.0081atom 12 6 HR1 "HIS+ HD2/HISDE HE1" 1 1.0081

Page 20: Computational Modeling of Macromolecular Systems

################################ ## ## ## Van der Waals Parameters ## ## ## ################################

vdw 1 1.3200 -0.0220vdw 2 1.3582 -0.0300vdw 3 0.2245 -0.0460vdw 4 1.3200 -0.0220vdw 5 0.2245 -0.0460vdw 6 0.9000 -0.0460vdw 7 0.7000 -0.0460vdw 8 1.4680 -0.0078vdw 9 0.4500 -0.1000vdw 10 2.0000 -0.1100

/Ao /(kcal/mol)

Page 21: Computational Modeling of Macromolecular Systems

################################## ## ## ## Bond Stretching Parameters ## ## ## ##################################

bond 1 10 330.00 1.1000bond 1 11 340.00 1.0830bond 1 12 317.13 1.1000bond 1 13 309.00 1.1110bond 1 14 309.00 1.1110bond 1 15 322.00 1.1110bond 1 17 309.00 1.1110bond 1 18 309.00 1.1110bond 1 21 330.00 1.0800

/(kcal/mol/Ao2) /Ao

Page 22: Computational Modeling of Macromolecular Systems

################################ ## ## ## Angle Bending Parameters ## ## ## ################################

angle 3 10 34 50.00 121.70angle 13 10 24 80.00 116.50angle 13 10 27 20.00 112.50angle 13 10 34 80.00 121.00angle 14 10 24 80.00 116.50angle 14 10 27 20.00 112.50angle 14 10 34 80.00 121.00angle 15 10 24 80.00 116.50angle 15 10 27 20.00 112.50angle 15 10 34 80.00 121.00angle 16 10 24 80.00 116.50angle 16 10 27 20.00 112.50

/(kcal/mol/rad2) /deg

Page 23: Computational Modeling of Macromolecular Systems

############################ ## ## ## Torsional Parameters ## ## ## ############################torsion 1 11 11 1 2.500 180.0 2torsion 1 11 11 11 3.500 180.0 2torsion 1 11 11 22 3.500 180.0 2torsion 2 11 11 2 2.400 180.0 2torsion 2 11 11 11 4.200 180.0 2torsion 2 11 11 14 4.200 180.0 2torsion 2 11 11 15 4.200 180.0 2torsion 2 11 11 22 3.000 180.0 2torsion 2 11 11 35 4.200 180.0 2torsion 2 11 11 36 4.200 180.0 2torsion 11 11 11 11 3.100 180.0 2torsion 11 11 11 14 3.100 180.0 2torsion 11 11 11 15 3.100 180.0 2torsion 11 11 11 22 3.100 180.0 2torsion 11 11 11 35 3.100 180.0 2torsion 11 11 11 36 3.100 180.0 2

/(kcal/mol) /deg

Page 24: Computational Modeling of Macromolecular Systems

Algorithms for Molecular Dynamics

Runge-Kutta methods:

x(t+t) = x(t) + (dx/dt) t

Fourth-order Runge-Kutta

x(t+t) = x(t) + (1/6) (s1+2s2+2s3+s4) t +O(t5) s1 = dx/dt s2 = dx/dt [w/ t=t+t/2, x = x(t)+s1t/2] s3 = dx/dt [w/ t=t+t/2, x = x(t)+s2t/2] s4 = dx/dt [w/ t=t+t, x = x(t)+s3 t]

Very accurate but slow!

Page 25: Computational Modeling of Macromolecular Systems

Algorithms for Molecular Dynamics

Verlet Algorithm:

x(t+t) = x(t) + (dx/dt) t + (1/2) d2x/dt2 t2 + ... x(t -t) = x(t) - (dx/dt) t + (1/2) d2x/dt2 t2 - ...

x(t+t) = 2x(t) - x(t -t) + d2x/dt2 t2 + O(t4)

Efficient & Commonly Used!

Page 26: Computational Modeling of Macromolecular Systems

Calculated Properties

• Structure, Geometry

• Energy & Stability

• Mechanic Properties: Young’s Modulus

• Vibration Frequency & Mode

Page 27: Computational Modeling of Macromolecular Systems

Crystal Structure of C60 solid

Crystal Structure of K3C60

Page 28: Computational Modeling of Macromolecular Systems

Vibration Spectrum of K3C60

GH Chen, Ph.D. Thesis, Caltech (1992)

Page 29: Computational Modeling of Macromolecular Systems

Quantum Chemistry Methods

• Ab initio Molecular Orbital Methods

Hartree-Fock, Configurationa Interaction (CI)

MP Perturbation, Coupled-Cluster, CASSCF

• Density Functional Theory

• Semiempirical Molecular Orbital Methods Huckel, PPP, CNDO, INDO, MNDO, AM1

PM3, CNDO/S, INDO/S

Page 30: Computational Modeling of Macromolecular Systems

H E

SchrÖdinger Equation

HamiltonianH = (h2/2mh2/2me)ii

2 + ZZeri e2/ri

ije2/rij

Wavefunction

Energy

Page 31: Computational Modeling of Macromolecular Systems

f(1)+ J2(1) K2(1)1(1)11(1)f(2)+ J1(2) K1(2)2(2)22(2)

F(1) f(1)+ J2(1) K2(1) Fock operator for 1F(2) f(2)+ J1(2) K1(2) Fock operator for 2

Hartree-Fock Equation:

Fock Operator:

+e-

e-

Page 32: Computational Modeling of Macromolecular Systems

f(1) h2/2me)12 N ZNr1N

one-electron term if no Coulomb interactionJ2(1) dr2

e2/r122Ave. Coulomb potential on electron 1 from 2 K2(1) 2 dr2

*e2/r12 Ave. exchange potential on electron 1 from 2f(2) h2/2me)2

2 N ZNr2NJ1(2) dr1

e2/r121K1(2) 1 dr1

*e2/r12 Average Hamiltonian for electron 1 F(1) f(1)+ J2(1) K2(1)

Average Hamiltonian for electron 2 F(2) f(2)+ J1(2) K1(2)

Page 33: Computational Modeling of Macromolecular Systems

1. Many-Body Wave Function is approximated by Single Slater Determinant

2. Hartree-Fock EquationF i = i i

  F Fock operator

i the i-th Hartree-Fock orbital

i the energy of the i-th Hartree-Fock orbital

Hartree-Fock Method

Page 34: Computational Modeling of Macromolecular Systems

3. Roothaan Method (introduction of Basis functions)i = k cki k LCAO-MO

  {k } is a set of atomic orbitals (or basis functions)

4. Hartree-Fock-Roothaan equation j ( Fij - i Sij ) cji = 0

  Fij iF j Sij ij

5. Solve the Hartree-Fock-Roothaan equation self-consistently (HFSCF)

Page 35: Computational Modeling of Macromolecular Systems

Graphic Representation of Hartree-Fock Solution

0 eV

IonizationEnergy

ElectronAffinity

Page 36: Computational Modeling of Macromolecular Systems

The energy required to remove an electron from aclosed-shell atom or molecules is well approximatedby minus the orbital energy of the AO or MO fromwhich the electron is removed.

Koopman’s Theorem

Page 37: Computational Modeling of Macromolecular Systems

Slater-type orbitals (STO)  nlm = N rn-1exp(r/a0) Ylm(,)

 the orbitalexponent

Gaussian type functions (GTF)gijk = N xi yj zk exp(-r2)

(primitive Gaussian function)p = u dup gu

(contracted Gaussian-type function, CGTF)u = {ijk} p = {nlm}

Basis Set i = p cip p

Page 38: Computational Modeling of Macromolecular Systems

Basis set of GTFs STO-3G, 3-21G, 4-31G, 6-31G, 6-31G*, 6-31G**------------------------------------------------------------------------------------- complexity & accuracy

Minimal basis set: one STO for each atomic orbital (AO)

STO-3G: 3 GTFs for each atomic orbital3-21G: 3 GTFs for each inner shell AO 2 CGTFs (w/ 2 & 1 GTFs) for each valence AO 6-31G: 6 GTFs for each inner shell AO 2 CGTFs (w/ 3 & 1 GTFs) for each valence AO 6-31G*: adds a set of d orbitals to atoms in 2nd & 3rd rows6-31G**: adds a set of d orbitals to atoms in 2nd & 3rd rows

and a set of p functions to hydrogen Polarization Function

Page 39: Computational Modeling of Macromolecular Systems

Diffuse Basis Sets:For excited states and in anions where electronic densityis more spread out, additional basis functions are needed.

Diffuse functions to 6-31G basis set as follows: 6-31G* - adds a set of diffuse s & p orbitals to atoms in 1st & 2nd rows (Li - Cl). 6-31G** - adds a set of diffuse s and p orbitals to atoms in 1st & 2nd rows (Li- Cl) and a set of diffuse s functions to H Diffuse functions + polarisation functions:6-31+G*, 6-31++G*, 6-31+G** and 6-31++G** basis sets.

Double-zeta (DZ) basis set: two STO for each AO

Page 40: Computational Modeling of Macromolecular Systems

6-31G for a carbon atom: (10s12p) [3s6p]

1s 2s 2pi (i=x,y,z)

6GTFs 3GTFs 1GTF 3GTFs 1GTF

1CGTF 1CGTF 1CGTF 1CGTF 1CGTF (s) (s) (s) (p) (p)

Page 41: Computational Modeling of Macromolecular Systems

Electron Correlation: avoiding each other

Two reasons of the instantaneous correlation:(1) Pauli Exclusion Principle (HF includes the effect)(2) Coulomb repulsion (not included in the HF)

Beyond the Hartree-FockConfiguration Interaction (CI)*Perturbation theory*Coupled Cluster MethodDensity functional theory

Page 42: Computational Modeling of Macromolecular Systems

Configuration Interaction (CI)

+

+ …

Page 43: Computational Modeling of Macromolecular Systems

Single Electron Excitation or Singly Excited

Page 44: Computational Modeling of Macromolecular Systems

Double Electrons Excitation or Doubly Excited

Page 45: Computational Modeling of Macromolecular Systems

Singly Excited Configuration Interaction (CIS): Changes only the excited states

+

Page 46: Computational Modeling of Macromolecular Systems

Doubly Excited CI (CID):Changes ground & excited states

+

Singly & Doubly Excited CI (CISD):Most Used CI Method

Page 47: Computational Modeling of Macromolecular Systems

Full CI (FCI):Changes ground & excited states

++

+ ...

Page 48: Computational Modeling of Macromolecular Systems

H = H0 + H’H0n

(0) = En(0)n

(0)

n(0) is an eigenstate for unperturbed system

H’ is small compared with H0

Perturbation Theory

Page 49: Computational Modeling of Macromolecular Systems

Moller-Plesset (MP) Perturbation Theory

The MP unperturbed Hamiltonian H0

H0 = m F(m)

where F(m) is the Fock operator for electron m.And thus, the perturbation H’  

H’ = H - H0

 Therefore, the unperturbed wave function is simply the Hartree-Fock wave function . Ab initio methods: MP2, MP3, MP4

Page 50: Computational Modeling of Macromolecular Systems

= eT(0)

(0): Hartree-Fock ground state wave function: Ground state wave functionT = T1 + T2 + T3 + T4 + T5 + …Tn : n electron excitation operator

Coupled-Cluster Method

=T1

Page 51: Computational Modeling of Macromolecular Systems

CCD = eT2(0)

(0): Hartree-Fock ground state wave functionCCD: Ground state wave functionT2 : two electron excitation operator

Coupled-Cluster Doubles (CCD) Method

=T2

Page 52: Computational Modeling of Macromolecular Systems

Complete Active Space SCF (CASSCF)

Active space

All possible configurations

Page 53: Computational Modeling of Macromolecular Systems

Density-Functional Theory (DFT)Hohenberg-Kohn Theorem: The ground state electronic density (r) determines uniquely all possible properties of an electronic system (r) Properties P (e.g. conductance), i.e. P P[(r)]

Density-Functional Theory (DFT)E0 = h2/2me)i <i |i

2 |i > dr e2(r) /

r1 dr1 dr2 e2/r12 + Exc[(r)]

Kohn-Sham Equation: FKS i = i i

FKS h2/2me)ii2 e2 / r1jJj + Vxc

Vxc Exc[(r)] / (r)

Page 54: Computational Modeling of Macromolecular Systems

Semiempirical Molecular Orbital Calculation

Extended Huckel MO Method (Wolfsberg, Helmholz, Hoffman)

Independent electron approximation

Schrodinger equation for electron i 

Hval = i Heff(i)

Heff(i) = -(h2/2m) i2 + Veff(i)

Heff(i) i = i i

Page 55: Computational Modeling of Macromolecular Systems

LCAO-MO: i = r cri r

  s ( Heff

rs - i Srs ) csi = 0

  Heffrs rHeff s Srs

rs Parametrization: Heff

rr rHeff r minus the valence-state ionization potential (VISP)

Page 56: Computational Modeling of Macromolecular Systems

Atomic Orbital Energy VISP--------------- e5 -e5

--------------- e4 -e4

--------------- e3 -e3

--------------- e2 -e2

--------------- e1 -e1

 Heff

rs = ½ K (Heffrr + Heff

ss) Srs K:

13

Page 57: Computational Modeling of Macromolecular Systems

CNDO, INDO, NDDO(Pople and co-workers)

Hamiltonian with effective potentialsHval = i [ -(h

2/2m) i2 + Veff(i) ] + ij>i e

2 / rij

two-electron integral:(rs|tu) = <r(1) t(2)| 1/r12 | s(1) u(2)>

 CNDO: complete neglect of differential overlap (rs|tu) = rs tu (rr|tt) rs tu rt

Page 58: Computational Modeling of Macromolecular Systems

INDO: intermediate neglect of differential overlap(rs|tu) = 0 when r, s, t and u are not on the same atom.

NDDO: neglect of diatomic differential overlap(rs|tu) = 0 if r and s (or t and u) are not on the same atom.

CNDO, INDO are parametrized so that the overallresults fit well with the results of minimal basis abinitio Hartree-Fock calculation.

CNDO/S, INDO/S are parametrized to predict optical spectra.

Page 59: Computational Modeling of Macromolecular Systems

MINDO, MNDO, AM1, PM3(Dewar and co-workers, University of Texas, Austin) MINDO: modified INDOMNDO: modified neglect of diatomic overlap AM1: Austin Model 1PM3: MNDO parametric method 3 *based on INDO & NDDO *reproduce the binding energy

Page 60: Computational Modeling of Macromolecular Systems

Relativistic Effects

Speed of 1s electron: Zc / 137

Heavy elements have large Z, thus relativistic effects areimportant.

Dirac Equation:Relativistic Hartree-Fock w/ Dirac-Fock operator; orRelativistic Kohn-Sham calculation; orRelativistic effective core potential (ECP).

Page 61: Computational Modeling of Macromolecular Systems

Ground State: ab initio Hartree-Fock calculation

Page 62: Computational Modeling of Macromolecular Systems

Computational Time: protein w/ 10,000 atoms

ab initio Hartree-Fock ground state calculation:

~20,000 years on CRAY YMP

Page 63: Computational Modeling of Macromolecular Systems
Page 64: Computational Modeling of Macromolecular Systems

In 2010: ~24 months on 100 processor machine

One Problem: Transitor with a few atoms

Current Computer Technology will fail !

Page 65: Computational Modeling of Macromolecular Systems

Quantum Chemist’s Solution

Linear-Scaling Method: O(N)

Computational time scales linearly with system size

Time

Size

Page 66: Computational Modeling of Macromolecular Systems

Linear Scaling Calculation for Ground State

W. Yang, Phys. Rev. Lett. 1991

Divide-and-Conqure (DAC)

Page 67: Computational Modeling of Macromolecular Systems

Density-Matrix Minimization (DMM) Method

Li, Nunes and Vanderbilt, Phy. Rev. B. 1993

Minimize the Energy or the Grand Potential:

= Tr [ (32 - 23) (H-I) ]

Page 68: Computational Modeling of Macromolecular Systems

Orbital Minimization (OM) Method

Mauri (1993), Ordejon (1993), Galii (1994), Kim (1995)

Minimize the Energy or the Grand Potential:

= 2 nij cni (H-I)ij cn

j - nmij cn

i (H-I)ij cmj l cn

l cml

Page 69: Computational Modeling of Macromolecular Systems

Fermi Operator Expansion (FOE) Method

Goedecker & Colombo (1994)

Expand Density Matrix in Chebyshev Polynomial:

(H) = c0I + c1H + c2H2 + … = c0I / 2 + cjTj(H) + …

T0(H) = IT1(H) = H

Tj+1 (H) = 2HTj(H) - Tj-1(H)

Page 70: Computational Modeling of Macromolecular Systems

Superoxide Dismutase (4380 atoms)

York, Lee & Yang, JACS, 1996

Page 71: Computational Modeling of Macromolecular Systems

Linear Scaling First Principle Method

Two-electron integrals :

Vabcd = abe2 / r12 dc

Coulomb Integrals: Fast Multiple Method (FMM)

Exchange-Correlation (XC):Use of Locality

Strain, Scuseria & Frisch, Science (1996):LSDA / 3-21G DFT calculation on 1026 atom RNA Fragment

Page 72: Computational Modeling of Macromolecular Systems

Linear Scaling Calculation for Ground State

Yang, Phys. Rev. Lett. 1991Li, Nunes & Vanderbilt, Phy. Rev. B. 1993Baroni & Giannozzi, Europhys. Lett. 1992. Gibson, Haydock & LaFemina, Phys. Rev. B 1993.Aoki, Phys. Rev. Lett. 1993.Cortona, Phys. Rev. B 1991.Galli & Parrinello, Phys. Rev. Lett. 1992.Mauri, Galli & Car, Phys. Rev. B 1993.Ordejón et. al., Phys. Rev. B 1993.Drabold & Sankey, Phys. Rev. Lett. 1993.

Page 73: Computational Modeling of Macromolecular Systems

Linear Scaling Calculation for EXCITED STATE ?

A Much More Difficult Problem !

Page 74: Computational Modeling of Macromolecular Systems
Page 75: Computational Modeling of Macromolecular Systems

Localized-Density-Matrix (LDM) Method

ij(0) = 0 rij > r0

ij = 0 rij > r1Yokojima & Chen, Phys. Rev. B, 1999

Principle of the nearsightedness of equilibrium systems (Kohn, 1996)

Linear-Scaling Calculation for excited states

t

Page 76: Computational Modeling of Macromolecular Systems

,Hi

Heisenberg Equation of Motion

Time-Dependent Hartree-Fock Random Phase Approximation

Page 77: Computational Modeling of Macromolecular Systems

PPP Semiempirical Hamitonian

Polyacetylene

1

2

3

4

5

6

7

8

9

10

11

12

N-3

N-2

N-1

N

...

CH CH2N

extcckeluH HHHH ˆˆˆˆ

Page 78: Computational Modeling of Macromolecular Systems

Liang, Yokojima & Chen, JPC, 2000

Page 79: Computational Modeling of Macromolecular Systems

0 5000 10000 15000 200000

10,000

20,000

30,000

40,000

LDM

=50

0=20

LDM

=80

c=30

HF

CP

U T

ime

(s)

Number of Atoms

0 200 400 600 8000

1000

2000

3000

LDM

=50

c=20

LDM

=80

c=30

HF

CP

U T

ime

(s)

Number of Atoms

Yokojima, Zhou & Chen, Chem. Phys. Lett., 1999

Page 80: Computational Modeling of Macromolecular Systems

Liang, Yokojima & Chen, JPC, 2000

Page 81: Computational Modeling of Macromolecular Systems

Flat Panel Display

Page 82: Computational Modeling of Macromolecular Systems

Cambridge Display Technology

Weight: 15 gramResolution: 800x236Size: 45x37 mmVoltage: DC, 10V

Page 83: Computational Modeling of Macromolecular Systems
Page 84: Computational Modeling of Macromolecular Systems

Energy

Inte

nsi

ty

electron

hole

Page 85: Computational Modeling of Macromolecular Systems
Page 86: Computational Modeling of Macromolecular Systems
Page 87: Computational Modeling of Macromolecular Systems

Carbon Nanotube

Page 88: Computational Modeling of Macromolecular Systems
Page 89: Computational Modeling of Macromolecular Systems
Page 90: Computational Modeling of Macromolecular Systems

Liang, Wang, Yokojima & Chen, JACS (2000)

Surprising!DFT: no or very small gap

Page 91: Computational Modeling of Macromolecular Systems

Absorption Spectra of (9,0) SWNTs

Page 92: Computational Modeling of Macromolecular Systems

Smallest SWNT: 0.4 nm in diameter

Wang, Tang & etc., Nature (2000)

Three possibilities:

(4,2), (3,3) & (5,0) SWNTs

Page 93: Computational Modeling of Macromolecular Systems

Tang et. al, 2000

Page 94: Computational Modeling of Macromolecular Systems

Absorption of SWNTs (4,2), (3,3) & (5,0)

C332H12

C420H12

C330

Liang, & Chen (2001)

Page 95: Computational Modeling of Macromolecular Systems

Quantum Mechanics / Molecular Mechanics (QM/MM) Method

Combining quantum mechanics and molecular mechanics methods:

QM

MM

Page 96: Computational Modeling of Macromolecular Systems

GENOMICSHuman Genome Project

Page 97: Computational Modeling of Macromolecular Systems

Design of Aldose Reductase Inhibitors

Aldose Reductase

Page 98: Computational Modeling of Macromolecular Systems

Goddard, CaltechGoddard, Caltech