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Molecular Modeling in Chemical Industry R&D Brian Peterson May 7, 2004 Outline What Molecular Modeling is and is not Examples How MM relates to Chemical Engineering Thoughts on Curriculum (Chemical) Industry Drivers Societal Needs & Wants Potential to make or lose $

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Presentation Transcript
outline
Outline
  • What Molecular Modeling is and is not
  • Examples
  • How MM relates to Chemical Engineering
  • Thoughts on Curriculum
chemical industry drivers
(Chemical) Industry Drivers
  • Societal Needs & Wants
  • Potential to make or lose $
  • Competition- increasing and global
  • Efficient Management of
    • Time
    • Capital
    • Knowledge
    • Creativity
  • Compression of R&D timescales
  • Rapid & Accurate Evaluation of Complete Solutions
    • Rapid & Parallel Development (e.g. Materials, Process, Environmental, & Economic)
    • Go/NoGo decisions, “Stage Gate”
    • Modeling & Optimization
slide4

Time

Quantitative Structure/Property

Relationships (QSPR) &

Theory

year

min.

Macroscopic

Plant

Process

Fab

ms

Continuum

CFD

Mechanical

Kinetic

Emag

ns

Mesoscale

Properties / Parameters

10-12 s

Molecular

Molecular Dynamics

Monte Carlo

10-15 s

Quantum

Distance

1A 10A 100A 1mm 1cm km

Hierarchy of Models

slide5

Size

Structure

Energy

Enthalpy

Dipole Moments

Polarizability

Binding Energy

IR Spectra

Transition States

Activation Energy

NMR Spectra

Elastic Modulus

uv Spectra

Free Energy

Easy

Molecules

Gases

Perfect Crystals

Liquids

Polymers

Crystal Defects

Amorphous Solids

Small

Medium

Large

Organic

Inorganic

Hybrid

Equilibrium

Fast (t < ns)

Intermediate

Hard

enthalpy of formation via qm
Enthalpy of Formation via QM

D. Frurip et al., ACS SS 677, 1998

  • Heats of reaction can be more accurate than heats of formation
  • Can use QM to calculate group contributions for new groups
  • Outliers are not always predictable a priori
  • Many outliers are the result of experimental problems
example molecular sieving

Detailed

Modeling

Difficult Separation

Desired Product + Impurities

Calculate Polarizability,

Dipole, Quadrupole Moments

Calculate Characteristic Sizes

no

“Real Difficult”

Separation

no

Dprod – Dimp > ~ 0.3 A ?

Significant Difference ?

yes

yes

“Easy” Separation

Find Appropriate Material

Other

Separation

Methods

Find Zeolite such that

Dprod > Dzeo > Dimp

Experiment

Example: Molecular Sieving
slide8

MOVED

INSERTED

X

DESTROYED

m,V,T

N,E

Grand Canonical Monte Carlo

A classical force-field method where molecules interact and are ...

... such that the proper distribution of positions, energies, and numbers of molecules is achieved for a system at fixed chemical potential (or fugacity or pressure) and temperature. GCMC is often used to study the adsorption of small molecules in inorganic materials such as zeolites.

molecular size via computational sieving

Nthreshold

Dslit

Dtube

Molecular Size via Computational Sieving

Use a Force-Field and Grand Canonical Monte Carlo to adsorb

a gas molecule into a confined system at a standard T & P. If significant numbers of the molecule fit into the system, the characteristic size of the system is related to the size of the molecule.

An unbiased method which uses the information inherent in the FF.

D = Dslit - d

zeolite pore diameter molecular sizes

Molecules

Zeolites

Zeolite Pore Diameter + Molecular Sizes

Engineer had already tried 5A and did not get a good separation. After seeing this analysis, they repeated the experiment, found good separation, and commercialized the process.

slide11

Measure molecular width on computer screen

Molecular Size via QM Density Contours

  • Geometry optimize molecule
  • Calculate Electron Density Contours
example hydrogen storage
Example: Hydrogen Storage

ab initio MD of H2 in SWNT

  • SWNT highly fluxional; large C-C-C bond angle deformations are observed.
  • Adsorption energies much higher than previously published calculations using classical simulation methods: enhanced potential from curved carbon surface.
  • Improved, curvature-dependent potentials were created.

DHExpt(kcal/mol)DHSim(kcal/mol) (95% swnt, 7-14Å) (7.8Å, 11.8Å)

4 – 4.8 4.8, 3.3

H. Cheng, G. P. Pez, A. C. Cooper, J. Am. Chem. Soc.123, 5845 (2001).

M. K. Kostov, H. Cheng, A. C. Cooper, G. P.Pez Phys. Rev. Lett. 89, 6105 (2002)

xenon binding to proteins
Xenon binding to Proteins
  • Xenon/protein interactions are important...
    • Xenon is an anesthetic
    • Xenon is a neuroprotectant
    • Xenon is used to prepare “heavy atom” derivatives for X-ray diffraction
    • 129Xe is used in NMR studies of cavities
  • Predictive methods for binding of xenon would enable better understanding of ...
    • the mechanisms of physiological activity
    • the behavior of xenon in NMR and XRD experiments
    • binding sites not visible by XRD (due to resolution, occupancy, disorder)
  • The goal of this work was to show how grand canonical Monte Carlo Simulations (GCMC) coupled with a clustering algorithm can determine the positions, occupancy, and free energies of binding of small molecules.
mass clouds and clusters in comp xenon blue water red

Blue = Simulation, Black = X-Ray Diffraction

Mass Clouds and Clusters in COMP Xenon (blue) & Water (red)
  • GCMC + Force Field Reproduces
  • Experimental Binding Locations
  • Occupancy + Input Fugacity 
  • Equilibrium Constant  Free Energy
slide15

Processes

Process Analysis Optimization Control

Unit Operations

Petroleum

Surface Science

Biological Systems

Semiconductors

Production

time

Specific Problem

application

Energy

Food

Pharmaceuticals

Materials Design

Environment

Polymers

Transport

Thermodynamics

Kinetics

Quantum, Classical & Statistical Mechanics

Particles

Thinking Like a Chemical Engineer

summary mol modeling che
Summary: Mol. Modeling & ChE
  • Molecular Modeling (Computational Chemistry, Computational Materials Science, “Theory”, “Modeling”) is the natural extension and limit of the reductionist approach to chemical engineering.
  • MM is broadly applicable because “everything” is made of atoms and molecules.
  • The power of MM is growing rapidly with the continuing development of computer power, new algorithms, and the availability of software.
  • Today MM can sometimes provide useful estimates of the properties and behavior of materials- even before they have been synthesized. (materials design)
  • Today MM can sometimes provide useful estimates of the parameters and behavior needed to do traditional chemical engineering process development & design.
  • Today MM is sometimes the most efficient way to obtain these estimates.
  • MM works best in partnership with experiments and with traditional estimation and design approaches.
molecular modeling and the ug chemical engineering curriculum
Molecular Modeling and the UG Chemical Engineering Curriculum
  • Minimum: UG Chemical Engineers should be aware of the possibility that useful estimates of some material properties can be calculated for some systems and that the number of such properties and systems is continually increasing.
  • Optimum (?) : UG Chemical Engineers should have some familiarity with the techniques of MM and should be able to make informed guesses as to whether any given properties and materials are amenable to MM.
  • “Win/Win” MM techniques are wonderful pedagogical tools for understanding the fundamental physical processes which underlie thermodynamics, transport phenomena, and chemical kinetics. Much of the requisite familiarity could be obtained via the permeation, throughout the undergraduate curriculum, of computation and simulation as methods of understanding complementary to experiment and theory.
a black box is not a silver bullet

MM

A black box is not a silver bullet
  • Molecular Modeling will not replace all other scientists and engineers. Among other reasons, the techniques of computational chemistry and molecular modeling employ approximations. The validity of these approximations varies with the method and with the system considered. Therefore, one cannot blindly apply a given method to all systems and rationally expect useful answers.
  • Molecular Modeling can and does replace some unnecesary experimentation and it can lead to insights which initiate new experiments. Some approximations are quite valid for some systems and one can expect useful results when a suitable method is used to predict some subset of properties for those systems.
  • A given method will typically supply only some of the properties and information needed to solve a given problem. MM techniques are most useful when used in combination with each other and with experiment.
binding equilibrium and free energy
Binding Equilibrium and Free Energy

Occupancy + Input Fugacity  Equilibrium Constant  Free Energy