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Introduction Methods Results Summary Appendix Molecular Dynamics and the Rouse Model An Introduction to the Physics of Polymers Daniel Bridges 1 Dr. Aniket Bhattacharya 2 1 Department of Physics & Astronomy Middle Tennessee State University 2 Department of Physics University of Central Florida July 28, 2009 Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

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IntroductionMethods

ResultsSummaryAppendix

Molecular Dynamics and the Rouse ModelAn Introduction to the Physics of Polymers

Daniel Bridges1 Dr. Aniket Bhattacharya2

1Department of Physics & AstronomyMiddle Tennessee State University

2Department of PhysicsUniversity of Central Florida

July 28, 2009

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers are essential to life.

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Don’t sneeze; you might lose some polymers.

We can model a simplified DNAstrand as a beaded necklace.

Rouse model: ”Bead-springmodel”

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Don’t sneeze; you might lose some polymers.

We can model a simplified DNAstrand as a beaded necklace.

Rouse model: ”Bead-springmodel”

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”

2 ideal random walk ⇒ ”theta condition”

3 collapses to form compact globule

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”

2 ideal random walk ⇒ ”theta condition”

3 collapses to form compact globule

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”

2 ideal random walk ⇒ ”theta condition”

3 collapses to form compact globule

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”

2 ideal random walk ⇒ ”theta condition”

3 collapses to form compact globule

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”

2 ideal random walk ⇒ ”theta condition”

3 collapses to form compact globule

modeling this behavior ...

~F = −~∇U

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”2 ideal random walk ⇒ ”theta condition”3 collapses to form compact globule

modeling this behavior ...

~F = −~∇U − ~FR(t)

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”2 ideal random walk ⇒ ”theta condition”3 collapses to form compact globule

modeling this behavior ...

~F = −~∇U − ~FR(t)− ζ d~rdt

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”2 ideal random walk ⇒ ”theta condition”3 collapses to form compact globule

Langevin dynamics

~F = −~∇U − ~FR(t)− ζ d~rdt = m d2~r

dt2

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Polymers sit in solvents!

Polymer/solvent interaction (varies with temp.) dictates threesituations:

1 polymer ”stretched” ⇒ ”good solvent”2 ideal random walk ⇒ ”theta condition”3 collapses to form compact globule

Langevin dynamics

~F = −~∇U − ~FR(t)− ζ d~rdt = m d2~r

dt2

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Why am I here?Polymer physics

Relaxation time = MD equilibriation

Want polymer to be ”at rest”.

Important for MD accuracy and clarity

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Verlet Algorithm is ”numerical integration” of motion eqns.

Next position ~r(t + δt) ”future” determined by

current position ~r(t) and acceleration ~a(t): t ⇒ ”now”

previous position ~r(t − δt): t − δt ⇒ ”past”

~r(t + δt) = ~r(t) + δt~v(t) + 12δt2~a(t) + ...

~r(t − δt) = ~r(t)− δt~v(t) + 12δt2~a(t) + ...

The Verlet Algorithm

~r(t + δt) = 2~r(t)−~r(t − δt) + δt2~a(t) + ...

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Verlet Algorithm is ”numerical integration” of motion eqns.

Next position ~r(t + δt) ”future” determined by

current position ~r(t) and acceleration ~a(t): t ⇒ ”now”

previous position ~r(t − δt): t − δt ⇒ ”past”

~r(t + δt) = ~r(t) + δt~v(t) + 12δt2~a(t) + ...

~r(t − δt) = ~r(t)− δt~v(t) + 12δt2~a(t) + ...

The Verlet Algorithm

~r(t + δt) = 2~r(t)−~r(t − δt) + δt2~a(t) + ...

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Verlet Algorithm is ”numerical integration” of motion eqns.

Next position ~r(t + δt) ”future” determined by

current position ~r(t) and acceleration ~a(t): t ⇒ ”now”

previous position ~r(t − δt): t − δt ⇒ ”past”

~r(t + δt) = ~r(t) + δt~v(t) + 12δt2~a(t) + ...

~r(t − δt) = ~r(t)− δt~v(t) + 12δt2~a(t) + ...

The Verlet Algorithm

~r(t + δt) = 2~r(t)−~r(t − δt) + δt2~a(t) + ...

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Verlet Algorithm is ”numerical integration” of motion eqns.

Next position ~r(t + δt) ”future” determined by

current position ~r(t) and acceleration ~a(t): t ⇒ ”now”

previous position ~r(t − δt): t − δt ⇒ ”past”

~r(t + δt) = ~r(t) + δt~v(t) + 12δt2~a(t) + ...

~r(t − δt) = ~r(t)− δt~v(t) + 12δt2~a(t) + ...

The Verlet Algorithm

~r(t + δt) = 2~r(t)−~r(t − δt) + δt2~a(t) + ...

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Basic assumptions of the the ”Bead-Spring” Model

N ”beads” (monomers are points) with

spring constant ksp = 3kBTb2

No hydrodynamic interactions betweenbeads

Proposed by P. E. Rouse, J Chem Phys 21,1272 (1953).

Drawbacks: Does not accurately express diffusion coefficient orrelaxation time.

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Basic assumptions of the the ”Bead-Spring” Model

N ”beads” (monomers are points) with

spring constant ksp = 3kBTb2

No hydrodynamic interactions betweenbeads

Proposed by P. E. Rouse, J Chem Phys 21,1272 (1953).

Drawbacks: Does not accurately express diffusion coefficient orrelaxation time.

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Basic assumptions of the the ”Bead-Spring” Model

N ”beads” (monomers are points) with

spring constant ksp = 3kBTb2

No hydrodynamic interactions betweenbeads

Proposed by P. E. Rouse, J Chem Phys 21,1272 (1953).

Drawbacks: Does not accurately express diffusion coefficient orrelaxation time.

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Building on Rouse: the Zimm model

addresses hydrodynamicinteractions between beads

the diffusion coefficient andrelaxation times agree withexperiment.

Proposed by B. H. Zimm, J ChemPhys 24, 269 (1956).

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Building on Rouse: the Zimm model

addresses hydrodynamicinteractions between beads

the diffusion coefficient andrelaxation times agree withexperiment.

Proposed by B. H. Zimm, J ChemPhys 24, 269 (1956).

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Building on Rouse: the Zimm model

addresses hydrodynamicinteractions between beads

the diffusion coefficient andrelaxation times agree withexperiment.

Proposed by B. H. Zimm, J ChemPhys 24, 269 (1956).

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Verlet AlgorithmThe Rouse ModelThe Zimm ModelSome Topics Considered

Interesting Quantities (Rouse)

Average Radius of Gyration:

R2g = 1

npart

∑npartn=1

⟨(~Rn − ~RG )2

⟩End-to-end Distance:

∥∥∥~R1N

∥∥∥ =∥∥∥∑N

n=1~rn

∥∥∥Diffusion Constant:DG = kBT

Nζ = 16t

⟨‖~ri (t)−~ri (0)‖2

⟩Relaxation Time: τr ' Nb2

DG

ζ, (viscous) frictioncoefficient

N, number ofmonomers

kB , Boltzmannconstant

T , temperature

τ , relaxation time

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Diffusion Constant

Diffusion Constant

DG = kBTNζ = 1

6t

⟨‖~ri (t)−~ri (0)‖2

⟩Rahman: 2.43× 10−5 cm2

sMine: 2.47× 10−5 cm2

s

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Future goals:

understanding forced translocation

manipulate polymer movement

gene therapyvirus injectionprotein sequencing

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Future goals:

understanding forced translocation

manipulate polymer movement

gene therapyvirus injectionprotein sequencing

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Future goals:

understanding forced translocation

manipulate polymer movement

gene therapy

virus injectionprotein sequencing

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Future goals:

understanding forced translocation

manipulate polymer movement

gene therapyvirus injection

protein sequencing

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Future goals:

understanding forced translocation

manipulate polymer movement

gene therapyvirus injectionprotein sequencing

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

Future Outlook

Thank you!

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

For Further Reading

For Further Reading I

Allen, M.P., Tildesley, D.J. (1987).Computer Simulation of Liquids.Oxford: Clarendon Press.

Doi, M. (1995).Introduction to Polymer Physics.Oxford: Clarendon Press

Jones, R. A. L. (2002).Soft Condensed Matter.Oxford: Oxford University Press.

Teraoka, I. (2002).Polymer Solutions: An Introduction to Physical PropertiesJohn Wiley and Sons, Inc.

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model

IntroductionMethods

ResultsSummaryAppendix

For Further Reading

For Further Reading II

Rahman, A. (1964).Correlations in the Motion of Atoms in Liquid Argon.Physical Review, 136, Number 2A

Daniel Bridges, Dr. Aniket Bhattacharya Molecular Dynamics and the Rouse Model