Opportunities in Scientific Computing with Emphasis on Atomistic Simulations

Texas Tech University, February 5, 2008

Scientific Computing/Computational Science

CDSSIM – Cyberinfrastructure for Chemical Dynamics Simulations; Sailesh Baidya, U. Lourderaj, Yu Zhuang

Chemical Dynamics Simulations ● Cl- + CH3I → ClCH3 + I- SN2 nucleophilic substitution reaction; Jiaxu Zhang, Upakarasamy Lourderaj ● Protonated peptide ion surface-induced dissociation; Bipasha Deb, Kyoyeon Park, Wenfang Hu,Kihyung Song

Acknowledgements

Research Group S. V. Addepalli Sailesh Baidya Wenfang Hu Kyoyeon Park Khatuna Kakhiani U. Lourderaj Mingying Xue Li Yang Jiaxu Zhang

Funding National Science Foundation Welch Foundation

Computer Programs VENUS VENUS/MOPAC VENUS/NWChem VENUS/GAMESS

Simulation Collaborations Kihyung Song NSF-PIRE Researchers Phil Smith (HPCC) Yu Zhuang

WEBSERVICES FOR CHEMICAL DYNAMICSRESEARCH AND EDUCATION

• monte.chem.ttu.edu – animations of chemical dynamics simulations; tutorials for chemical dynamics instruction (high school, undergraduate, graduate), current tutorial for gas-phase SN2 reactions and more are planned.

• cdssim.chem.ttu.edu – library of chemical dynamics computer programs and simulation models; user interface for modifying simulation models; resources for performing a chemical dynamics simulation and animating the result.

• pire-europe.chem.ttu.edu – NSF Partnership in International Research and Education (PIRE), “Simulations of Electronic Non-Adiabatice for Reactions with Hydrocarbon Liquids, Macromolecules and Surfaces”

COMPUTATIONAL SCIENCE/SCIENTIFIC COMPUTING

The term “computational science” was first used by Ken Wilson (awarded a Nobel Prize in physics) to refer to those activities in science and engineering (and now also medicine!) that exploit computing as their main tool.

In the spring of 1994, the IEEE began publishing a magazine called “IEEE Computational Science and Engineering”

COMPUTATIONAL SCIENCE/SCIENTIFIC COMPUTING

Applied Math Computer Science

Engineering/Science/Medicine

Scientific Computing

ATOMISTIC SIMULATION RESEARCH AT TTU

ALGORITHM DEVELOPMENT FOR SCIENTIFIC COMPUTING Edward J. Allen (Mathematics) - development and analysis of numerical methods to solve nuclear engineering problems such as neutron transport and reactor kinetics. Thomas L. Gibson (Physics) - parallelization of sequential Monte Carlo code for  modelling lipids in cell membranes; development of the distributed Positron Model. William L. Hase (Chemistry) – efficient numerical integration algorithms for solving coupled, non-linear differential equations.

Rajesh Khare (Chemical Engineering) – Efficient methods for saddle point determination on potential energy surfaces, multi-scale simulations. Jorge A. Morales (Chemistry) – efficient numerical integration algorithms for solving coupled, non-linear differential equations. Bill Poirier (Chemistry) – efficient linear solvers and eigensolvers for terascale parallel computing, linear partial differential equations, sparse linear algebra.

COMPUTATIONAL BIOLOGY

William L. Hase (Chemistry) – dissociation of peptide ions, dynamics of enzyme catalysis.

Rajesh Khare (Chemical Engineering) – molecular simulations of DNA dynamics, lubrication in human joints. Jorge A. Morales (Chemistry) – time-dependent, coherent-states dynamics of inter- and intra- molecular electron transfers coupled to nuclear motion in prototypical organic and biochemical molecules (e.g. small bridged donor- acceptor systems, small peptides) Mark Vaughn (Chemical Engineering) – 1. Molecular dynamics of lipid bilayer membranes with and without embedded protein. Single, binary and ternary lipid compositions. Particularly interested in mechanically strained membranes. 2. Molecular dynamics of solid-tethered DNA oligomers.

CHEMICAL REACTION DYNAMICS David Birney (Chemistry) – potential energy surfaces of organic reactions,pseudopericyclic, and pericyclic reactions. William L. Hase (Chemistry) – dynamics of organic reactions, gas-surfacecollisions, dynamics of unimolecular dissociation. Jorge A. Morales (Chemistry) – Development of a unifying quantum/classical coherent states (CS) dynamics for all molecular particles (nuclei and electrons) and for all the molecular degrees of freedom (i. e. translational, rotational, vibrational, and electronic CS) along with its computational implementation. Theory applied to reactive collisions and charge transfer processes. Bill Poirier (Chemistry) – exact quantum dynamics of gas phase reactions, thermal rate constants relevant to atmospheric and combustion chemistry, rovibrational dynamics of rare gas clusters.

MATERIALS SCIENCE Stefan K. Estreicher (Physics) – properties of defects in group IV and III-N semiconductors, free energies, vibrational dynamics, vibrational lifetimes, MD simulations.

William L. Hase (Chemistry) – tribology dynamics, heat transfer and structures at interfaces.

Rajesh Khare – molecular simulations of nanofluidic devices, lubrication, phase equilibria, properties of supercooled liquids and glassy polymers, rates of activated processes. Charles W. Myles (Physics) - theoretical and computational materials physics, with emphasis on semiconductor materials. High electric field transport. clathrates and other exotic materials. Electronic properties of defects, electronic bandstructures, properties of semiconductor alloys. Molecular Dynamics and Monte Carlo computer simulations. Mark Vaughn (Chemical Engineering) - probabilistic potential theory (a stochastic method for solving elliptic PDEs) to compute averaged properties of dense, reactive suspensions.

SOFTWARE DEVELOPMENT

Thomas L. Gibson (Physics) - MPI computer programs (PATMOL) to calculate the positron-molecule interaction potential for use in quantum scattering calculations.

William L. Hase (Chemistry) – computer programs for chemical dynamicssimulations, VENUS and VENUS/NWChem; scientific computing website“cdsism.chem.ttu.edu”.

Rajesh Khare (Chemical Engineering) – Molecular dynamics simulation codes (in FORTRAN) for nanofluidics and interfacial heat transfer. Jorge A. Morales (Chemistry) – development of the code CSTech (“Coherent States at Tech”) to implement the above-mentioned coherent states dynamics. This involves compute grid development and code parallelization inter alia.

Bill Poirier (Chemistry) - development of the ScalIT package for performing sparse iterative linear algebra on massively parallel computers.

ENERGY OF MOLECULES

( ) ( )E T p V q

Molecular Mechanics (MM) Potential Energy Function

bond stretch

20( )V f r r

r2

0( )V f

bond angle

torsion angle bend

0V V n sin

non-bonded r12 6/ /V a r b r

ENERGY OF MOLECULES

ˆ ( ; ) ( ; ) ( ; )rH r q r q E r q

Time-independent Schrödinger Equation from Quantum Mechanics

r - coordinates of electrons, q - coordinates of nuclei

- Hamiltonian operator, - wave functionEr - energy of electronsH

( ) ( ) ( )r NV q E q V q

VN – nuclear-nuclear repulsion

ENERGY OF MOLECULES

Solving ˆ ( ; ) ( ; ) ( ; ),rH r q r q E r q (q are fixed)

22ˆ ˆ ˆ( ) ( ) ( ) ( )

2 ii i

H r T r V r V rm

2 2 2

22 2 2ii i ix y z

ˆ( ) ( ) ( )E q r H r

Expand in a basis set, developed from experience and chemical intuition:

r( )

i ir c r ( ) ( )

Variational Theorem: Minimize E(q) with respect to ci : 0i

E q

c

( )

Leads to a set of secular equations which are solved by linear algebra (i.e. matrix operations)

QUANTUM THEORY OF MATTER

“The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact applications of these laws leads to equations much too complicated to be soluble.”

P. A. M. Dirac, 1929

COMPUTATIONAL AND THEORETICAL CHEMISTRY

Development of approximate models that are instructive and, after careful testing and further improvement, often give results in agreement with experiment and are predictive.

Nobel Prize in Quantum Chemistry Walter Kohn – Density Functional Theory John Pople – Method of Quantum Chemistry Computations (embodied in the Gaussian computer program)

ATOMIC-LEVEL MOTION

Classical Mechanics (approximate)

Newton’s equations of motion2

2i

ii

d qV qF m

q dt

( )

i = 1,..3N for N - atoms

Quantum Mechanics (exact)

q ti H q t

t

( , ) ˆ ( , )

CHEMICAL DYNAMICS SOFTWARE AND SIMULATION (CDSSIM) SYSTEM

(http://cdssim.chem.ttu.edu)

Library of open source computer programs, documentation, and

simulation models (input files) for distribution.

Software Tools

•To upload, modify, and build simulation models

•Animate chemical dynamics simulations.

Computational Resources for performing chemical dynamics simulations. Goal is to have grid computing resources.

Cyberinfrastructure for sharing chemical dynamics software.

COMPUTER PROGRAMS

Current

•RRKM – microcanonical RRKM calculations.

•VENUS96 – classical trajectory simulations for a variety of initial conditions, and assortment of analytic potentials.

Under Development

•VENUS06 – gas/surface scattering, new integration algorithms, electronic non-adiabatic transitions (?), and other options.

•VENUS/MOPAC – semiempirical QM and QM/MM direct dynamics.

•VENUS/NWChem and VENUS/GAMESS – interfaces for QM and QM/MM direct dynamics.

•Links – to other websites like POTLIB (Ron Duchovic) a library of analytic potentials to link to VENUS.

CLASSICAL TRAJECTORY SIMULATION

Solve Hamilton’s classical equations of motion

Energy = E = H = T(p) + V(q) ∂p i/∂t = -∂H/∂q i and ∂q i/∂t = ∂H/∂q i

or Newton’s classical equations of motion, - ∂V/∂q i = mi ∂2V/∂t 2

General classical trajectory computer program VENUS I. Potential Energy Surface V A. Analytic potential energy function – ab initio calculations and/or experimental data such as equilibrium geometries, vibrational frequencies, activation energies, potential well depths, and heats of reaction. B. Direct dynamics simulations – obtain the gradient ∂V/∂q i directly from electronic structure theory; i.e. time-independent quantum mechanics. II. Initial Conditions – select random initial conditions, for an ensemble of trajectories to model experiment. III. Numerically Integrate the Classical Equations of Motion IV. Trajectory Results – transform final momenta and coordinates into product energies, structures, etc.

DIRECT DYNAMICS COMPUTER CODES The general chemical dynamics computer program VENUS is interfaced with different electronic structure computer programs VENUS has:

Many different analytic potential energy functions, including reactive and non-reactive MM functions.

A suite of numerical integrators.

A variety of options for choosing trajectory initial conditions to model different

kinds of experiments.

Algorithms for analyses of trajectory results. VENUS/Electronic Structure Packages for Direct Dynamics:

VENUS/NWChem and VENUS/GAMESS under development.

VENUS/MOPAC, developed but needs documentation.

VENUS/MNDO99 ( with updates) under development.

SIMULATION MODELS FOR VENUS06

Select Type of Simulation Model Bimolecular Collisions

Collision Between Two Gas-Phase Molecule Unimolecular Decomposition

Decomposition of a Vibrationally Excited Molecule Intramolecular Dynamics 

Dynamics of Vibrational Energy Flow Within a Molecule Reaction Path Following and VTST

Following the Reaction Path and Calculating the Variational Transition State Theory (VTST) Rate Constant for association of Two Particles

Normal Mode Analysis Vibrational Frequencies for a Molecule, Surface, Cluster, etc.

SN2 Dynamics

Dynamics of Gas-phase X- + CH3Y SN2 Nucleophilic Substitution Reactions

Minimum Energy Geometry Optimization Finding a Minimum Energy Geometry for a Molecule, Surface, Cluster, etc.

Neutral Atom Small Molecule Collision with a Surface Projectile Ion Collision with a Surface

Ar + H2O Collision

Initial Conditions for H2O

Movie Maker

Diglycine-H+ + Diamond {111}

Animation

CDSSIM: WEB-BASED CHEMICAL DYNAMICS SIMULATIONS

A Tool for On-line Chemical Dynamics Simulations from

Your Desk Computer

•Chemical dynamics computer programs and documentation.

•Tools for using existing, modifying, and building simulation

models (input files).

•Computational resources for performing simulations.

•Tools to animate the simulations.

Goal: Facilitate access to high-level chemical dynamics simulation

tools and software, and motivate collaborative science.

Dynamics of the Cl- + CH3I → ClCH3 + I-

SN2 Nucleophilic Substitution Reaction

Collaboration: between Roland Wester Research Group, University of Freiburg, Germany (Experiments); and Bill Hase Research Group, Texas Tech University (Chemical Dynamics Simulations).

Traditional Atomic-Level Reaction Mechanism:

Cl- + CH3I → Cl----CH3I → ClCH3---I- → ClCH3 + I-

Science 319, 183 (2008)

Direct mechanism

Roundabout mechanism

SURFACE-INDUCED DISSOCIATION (SID)

I+(I+)*

(I+)* → Product

Mass spectrometry technique to determine ion (I+) structure and energetics. Need to know energy transfer efficiencies to interpret experiment.

intsurfacef

transitrans EEEE

Trajectory Simulations Ions: Si(Me)3

+, Cr(CO)6+, protonated glycine and alanine

peptide ions Surfaces: CH3(CH2)nS & CF3(CF2)nS/Au{111}, diamond{111} Experimental Collaborators Luke Hanley, Julia Laskin and Jean Futrell, Vicki Wysocki

+

TOP VIEW

SIDE VIEW

Surface: CF3(CF2)7S-Au

Protonated peptide

Energy Transfer Dynamics

Cr+(CO)6, Ei = 30 eV, Θi = 45o.

Samy Meroueh

PCCP 3, 2306 (2001)

Average per-cent transfer to

∆Eint for the H-SAM.

Simulation: 10 %.

Experiment: 11-12%; Cooks

and co-workers.

Energy Transfer Dynamics

Cr+(CO)6, Ei = 30 eV, Θi = 45o.

Samy Meroueh

PCCP 3, 2306 (2001)

Average per-cent transfer to

∆Eint for the H-SAM.

Simulation: 10 %.

Experiment: 11-12%; Cooks

and co-workers.

Fragmentation Dynamics

Diglycine + Diamond {111}, Ei = 70 eV, θi = 0o

QM+MM, Peptide described by AM1 (VENUS/MOPAC)

Diglycine + Diamond {111}, Ei = 70 eV, θi = 0o

QM+MM, Peptide described by AM1 (VENUS/MOPAC)

SHATTERING FRACTION VERSUS COLLISION ENERGY FOR (gly)2-H+ + DIAMOND {111}a

Y. Wang and K. Song, J. Am. Soc. Mass Spectrom. 2003, 14, 1402.

Collision Energy (eV) Shattering Fractionb

30 0.08 50 0.13 70 0.44 100 0.71

a. The collision angle is 0 degrees, perpendicular to the surface. The trajectories are QM+MM, with QM AM1 for the peptide.

b. Fraction of the trajectories which shatter.

Thanks!

Questions?