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Chemistry with Computers Yingbin Ge Iowa State University Central Washington University October 13, 2007

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Chemistry with Computers. Yingbin Ge Iowa State University. Central Washington University October 13, 2007. coupled-cluster CCSD(T). Perturbation theory MP2. density functional theory (DFT). Accuracy. Hartree Fock (HF). molecular mechanics. Computer time. What has been done?. - PowerPoint PPT Presentation

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Page 1: Chemistry with Computers

Chemistry with

Computers

Yingbin GeIowa State University

Central Washington University

October 13, 2007

Page 2: Chemistry with Computers

2

Accuracy

Computer time

Hartree Fock (HF)

Perturbation theory MP2density

functional theory (DFT)

coupled-cluster CCSD(T)

molecular mechanics

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What has been done?

• Global optimization of silicon nanoclusters.

• Chemical vapor deposition of silicon carbide.

Si14H20

Page 4: Chemistry with Computers

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Global optimization of silicon nanoclusters

•Why Si nanoclusters?•Si nanoclusters exhibit bright room-temperature photoluminescence which could be used in light-emitting devices.

•To model the excitation and emission of the Si nanoclusters, we need to know their thermodynamically stable structures.

A. Meldrum group, Adv. Mater.17, 845 (2005)

Page 5: Chemistry with Computers

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Global vs. local optimization

global minimum

local minimum

local minimum

local optimizationenergy

Energy

conformations

Page 6: Chemistry with Computers

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y = 0.0034e0.9827x

R2 = 0.997

0

200

400

600

800

1000

1200

1400

6 8 10 12 14

# of Ar atoms

# o

f lo

cal m

inim

a

Why is global optimization difficult?

Ar7 Ar8 Ar9 Ar10 Ar11 Ar12 Ar13

#LM 4 8 21 64 152 464 1328

Tsai and Jordan, JPC 97, 11227 (1993)

Page 7: Chemistry with Computers

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Global optimization strategies

*Applications of evolutionary computation in chemistry,Structure and Bonding, Vol. 110 (2004)

• Exhaustive search: too many minima to sample.

• Random sampling:”But there’s one I always miss.”

• Genetic algorithm is based on “the fittest survive” principle. It has been proven efficient for the global optimization of clusters and molecules.*

Page 8: Chemistry with Computers

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Genetic algorithm based global optimization

Produce random structures as initial population.

Evaluate energy (fitness) for each individual.

Repeat following steps until convergence:

Perform competitive selection.

Apply genetic operators* to produce new clusters.

Lower energy clusters replace higher-energy ones.

*Genetic operators: crossover and mutation.

Page 9: Chemistry with Computers

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Biological crossover and mutation

Crossover of 2 DNA strings

Mutation:1 missing nucleotide

missing nucleotidenormal

after mutation

normal after crossover

Page 10: Chemistry with Computers

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Crossover: silicon hydrides

local

opt.crossover

Page 11: Chemistry with Computers

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Mutation methods

Hydrogen shift

Partial rotation

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Mutation methods SiH2 SiH3

a. initial geometryb. after mutationc. final structure

SiH2 SiH3

Page 13: Chemistry with Computers

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Si10H16

Si18H22Si14H18Si10H14

Si14H20 Si18H24

Diamond-lattice SixHy global minima

SixHy-2 global minima

MP2 & DFT

Page 14: Chemistry with Computers

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SixHy global minima

SixFy global minima

Si10H16 Si10H14Si7H14 Si8H14

Si7F14 Si8F14 Si10F16 Si10F14

MP2 & DFT

DFT

Page 15: Chemistry with Computers

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Ligand effect

L= H

CH3 OH F

L2Si=SiL2

L3Si-SiL

MP2 global minimum

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Ligand effect

• Si10(CH3)16 and Si10H16 adopt the same diamond-lattice Si core.

• Si10(OH)16 and Si10F16 adopt same Si core with a 4-membered Si ring.

• Ligand electronegativity affects the Si core structures.

• -SiF3 and -Si(OH)3 are preferred at expenses of forming small 4-membered Si rings.

Page 17: Chemistry with Computers

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What did we learn?

• GA is efficient, scaling O(N4-5).• Well H-passivated Si clusters adopt diamond-lattice Si cores.

• Si core can be tuned with # ligands.• Si core can be tuned with ligand electronegativity. SixCly and SixBry?

• Further study the excitation and photon-emitting mechanism of Si nanoclusters.

• Questions and comments?

Page 18: Chemistry with Computers

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Questions?

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May 18, 2007HomeStead Road, Sunnyvale, CA

http://www.opentravelinfo.com/north-america/gas-price-hike

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Nuclear Energy• Additional energy source: less fight on oil.

• No SO2 - less acid rains.

• No CO2 - less global warming. Let’s try to keep New York & Shanghai above sea.

http://globalwarming--awareness2007.com/globalwarming-awareness2007/

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What about the safety?

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Layer 3. Silicon carbide is impervious to fission products and serves as a pressure vessel.

Layer 4. Pyrolytic carbon to protect SiC.

Layer 2. Pyrolytic carbon to trap fission products.

http://www.iaea.org/inis/aws/htgr/fulltext/xa54410.08.pdf

Layer 1. Porous carbon to accommodate fission products andkernel swelling.

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Chemical vapor deposition

• CVD: gas phase molecules break down at high T; fragments deposit on a substrate to account for the solid growth.

CH4 C H2

inlets

outlet

substrate

http://www.ieee-virtual-museum.org/collection/tech.php?taid=&id=2345958&lid=1

diamond growth

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Silicon carbide (SiC) coating process

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Coater Wall

Deposition ZoneAnnealing Zone

Uranium Particles

precursors

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Why silicon carbide?

• High melting point: 2700 C. • Mohs’ hardness: 9.3/10.• Imperviousness to fission products.• Lower reactivity at high temperature.

• Low cost.• SiC made by chemical vapor deposition is ideal material for the protective layer of nuclear energy pellets.

Page 25: Chemistry with Computers

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P: Defects in the SiC layer cause cracks on the surfaces of nuclear energy pellets.

Q: How to reduce defects in SiC?

A: Understand the mechanism of the SiC chemical vapor deposition. Propose ideal production condition.

Page 26: Chemistry with Computers

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• Detailed Reaction Kinetics for Modeling of Nuclear Fuel Pellet Coating for High Temperature Reactors.

• Drs. Gordon and Ge from the chemistry department.

• Drs. Fox and Gao from the chemical engineering department.

• Drs. Battaglia and Vedula from the mechanical engineering department.

Page 27: Chemistry with Computers

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Chemical vapor deposition of SiC

Precursors: CH3SiCl3 (methyltrichlorosilane)Temperature: 1000-2000 KPressure: ~1 atm Complex gas-phase and surface chemistryCH3SiCl3 SiC (solid) + 3HCl

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CH3SiCl3 decomposition pathways G = H - TS in

kcal/mol at 0 K (left) and 1400 K

(right)

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50 gas phase species

Cl, Cl2, H, H2, HCl, C2H, C2H2, C2H3, C2H3Cl,

C2H4, C2H5, C2H5Cl, C2H6(e), C2H6, 1CH2, 3CH2,

CH2C, CH2Cl, CH2Cl2, CH3, CH3CH(s), CH3Cl,

CH4, HCHC, Si2Cl4, Si2Cl5, Si2Cl6, SiCl2,

SiCl3, SiCl4, SiH2Cl, SiH2Cl2, SiH3Cl, SiHCl,

SiHCl2, SiHCl3, CH2SiCl2, CH2SiCl3, CH2SiHCl,

CH2SiHCl2, CH3SiCl, CH3SiCl2, CH3SiCl2Cl,

CH3SiCl3, CH3SiH2Cl, CH3SiHCl, CH3SiHCl2,

HCSiCl, 1CHSiCl3, 3CHSiCl3

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41 reactions without a transition state

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To be continued …

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73 reactions with a transition state

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Reduced mechanism• Our collaborators, including the chemical engineers and mechanical engineers, also complained about the long lists.

• How to reduce it?• Remove the species whose concentration is very low at high temperatures.

• Keep important species such as 3CH2, CH3, SiCl2, and SiCl3 as target molecules.

• Remove 1 species at a time and compare the reduced and full mechanisms.

• Reduced to 28 species and 29 reactions.

Page 33: Chemistry with Computers

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Time (s)

[C2H3]

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Time (s)

[SiHCl]

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Surface reactions: deposition

• Surface reactions involve thousands of atoms.

• Hybrid quantum mechanics/molecular mechanics (QM/MM) method.

Accuracy System size

Quantum mechanics

1 kcal/mol

tens of atoms

Molecular mechanics

10 kcal/mol

millions of atoms

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(bulk)-C3SiCl

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QM + MM regions

QM region

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CH

Si

Cl

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MM region

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MM region

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H attacks Cl

HCl leaving

H3C attacks Si*

Forming H3C-Si bond

1). Production of Si*. 2). Si-C growth.

MM region

MM region

Page 38: Chemistry with Computers

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What did we learn?

• A gas phase mechanism was proposed in the silicon carbide chemical vapor deposition.

• The gas phase mechanism was reduced to 28 species and 29 reactions.

• How temperature and precursor concentration affect gas phase chemistry.

• Surface chemistry under investigation.• Questions and comments?

Page 39: Chemistry with Computers

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Research plan

• Atomic layer deposition of Al2O3, TiO2, and SiO2.

• Global optimization of protein structures.

• Astrochemistry in ice.• Chemical vapor deposition of diamond C, pyrolytic C, and bulk Si.

• Fast global optimization of large silicon clusters.

Page 40: Chemistry with Computers

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Atomic layer deposition

• ALD is based on sequential, self-limiting surface chemical reactions.

• Precise atomic layer control: no defects!

http://www.colorado.edu/chemistry/GeorgeResearchGroup/intro/aldcartoon.GIF

repeat

A

B

Page 41: Chemistry with Computers

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Vanadium oxide (VxOy) catalyzed oxidative

dehydrogenation• Experimental energy barrier: 20-30 kcal/mol.

• Theoretical energy barrier: 45-80 kcal/mol.

• What’s wrong? Vanadium oxide is supported by the ALD produced Al2O3, SiO2, or TiO2 surfaces.

• How to model an ALD surface?• How does the ALD surface help lower the energy barrier of C3H8 + 1/2O2 C3H6 + H2O?

Page 42: Chemistry with Computers

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Global optimization of protein structures:

important for drug design

primary structure

secondary structure

quaternary structure

tertiary structure

Page 43: Chemistry with Computers

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Global optimization methods

• Random sampling: 30 dihedral angles each with 5 possible values. 530 (~1 billion trillion) conformations.

• Molecular dynamics: some proteins fold in minutes; energy and force need to be evaluated 1018 times (t=10-15s).

• Genetic algorithm + Tabu + In situ adaptive tabulation.

Page 44: Chemistry with Computers

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• Genetic algorithm.

crossover mutation

dihedral angles

a). Enew Eoldb). Enew wiEioldc). compute Enew

1

2

• Tabu (taboo): to penalize the moves to previously visited conformations.• In situ adaptive tabulation. {1… N} -> E

Page 45: Chemistry with Computers

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Astrochemistry in ice

Europa

Ganymede

Callisto

?

Page 46: Chemistry with Computers

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Jupiter’s Magnetic Field

Page 47: Chemistry with Computers

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Potential energy surface of 1H2O2

1O+H2O 50.8

1O-H2O 50.0

1H2O-O 15.7

1TS1 19.2

1O2 + H2 29.7

1HOOH -30.0

1TS2 70.2

1TS3

2OH + 2OH 16.1

2H + 2HOO 54.9 1TS4

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CCSD(T)(kcal/mol)

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Probable Reaction Paths to HOOH

• 1O + H2O 1H2O-O HOOH• 1O2 + H2 1H2O-O HOOH

• 1O (3O) + H2O 2OH + 2OH HOOH

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Future work

• Study the reaction paths at higher level of theories.

• Study the potential energy surfaces that involves cations such as 2O+.

• Reaction rate constant calculations.• Molecular dynamics calculations.• Elucidation of H2O2 formation mechanism.

• Study of H2O2 reaction paths in a biological environment.

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Acknowledgements

Prof. John D. Head at University of Hawaii

Prof. Mark S. Gordon at Iowa State University

Department of Energy Grant# DE-FC07-05ID14661

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Questions and comments are welcome.

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Crossover and mutation: Si only cluster

Deaven and Ho, PRL 75, 288 (1995)

A

B

a

b

a

B

A

b

crossover

mutation

local opt.

local opt.

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Reaction rate constant

k(T) kB T

h G / RT

e

kB -- Boltzmann constantT -- temperatureh -- Planck constantR -- Gas constant

G -- Free energy barrier (some times hard to obtain)

Page 55: Chemistry with Computers

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GTS

-20

0

20

40

60

80

100

120

1 1.5 2 2.5 3 3.5 4

R(H---CH3) (angstrom)

Rela

tive G

(kcal/m

ol)

400

1200

2000

T (K)

R=3.0 Åat 2000 K

Free Energy Profile of CH4 H + CH3 R=3.6 Åat 400 K

R=3.4 Åat 1200 K

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Molecular dynamics approximations for A + B A-B

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k(T) ge (T)(8kBT

)1/ 2(bmax

2 )(Preact )

Elec. degeneracy

Collision area

Reaction probability

Relative velocity

k(T) ge (T)() 1/ 2bmax2

: reduced mass.: symmetry factor.

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Predict k: from CH3 + H H-CH3

to CX3 + Y Y-CX3

-12

-11

-10

-9

-8

0 1 2 3 4

log

(k)

predictionexperiment

CH3+F

CH3+Cl

CF3+F

CCl3+Cl

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Predict k

• k1 (2CH3 + 2H 1CH4)• k2 (3CH2 + 2H 2CH3) Free energy barrier is hard

to get.

k2(T) /k1(T) [ge 2(T) /ge1(T)](2 /1) 1/ 2( 2 /1)

k2(T) /k1(T)

(1

3/1

4)(

14

15/15

16) 1/ 2(1/2)

0.668

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(0.0)

(19.3)

(7.9)

(47.7)

(16.9)

(75.9)

(59.8)

(69.7)

(64.6)

(34.2)

PES of SiCl3 + H2

Si: blueCl: greenH: light grey

G at 0 K(kcal/mol)

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Predict k: from CH3 + CH3 CH3-CH3

to CX3 +

CY3 CX3-CY3

-12

-11

-10

-9

-8

0 1 2 3 4

log

(k)

predictionexperiment

CH3+CCl3

CCl3+CCl3

CH3+SiH3

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Potential energy surface of 3H2O2

3O+H2O 0.0 3O-H2O

-1.2

3H2O-O -0.9

3TS1 17.6

trans-3HOOH 16.2

3TS2 70.9

3TS4 69.9

2H + 2HOO 54.9

cis- 3HOOH 20.8

3TS5 69.2

3OH-OH 13.6

2OH + 2OH 16.1

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CCSD(T)//CASSCF(kcal/mol)