Molecular Modeling in Chemical Industry R&D

Brian Peterson May 7, 2004

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Outline

What Molecular Modeling is and is not

Examples

How MM relates to Chemical Engineering

Thoughts on Curriculum

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(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

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MacroscopicPlant

ProcessFab

Hierarchy of Models

Distance

1A 10A 100A 1m 1cm km

10-15 s

10-12 s

ns

ms

year

min.

Time

Quantitative Structure/PropertyRelationships (QSPR) &Theory

ContinuumCFD

MechanicalKinetic

Emag

Mesoscale

MolecularMolecular Dynamics

Monte CarloQuantum

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Molecules

Gases

Perfect Crystals

Liquids

Polymers

Crystal Defects

Amorphous Solids

Easy

Hard

Small

Medium

Large

Organic

Inorganic

Hybrid

Equilibrium

Fast ( < ns)

Intermediate

Size

Structure

Energy

Enthalpy

Dipole Moments

Polarizability

Binding Energy

IR Spectra

Transition States

Activation Energy

NMR Spectra

Elastic Modulus

uv Spectra

Free Energy

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Enthalpy of Formation via QMMolecule G2 Theory

(kcal/mol)

Literature(kcal/mol)

ethylene 12.9 12.5cyclopropane 13.8 12.7bicyclobutane 55.2 51.9ammonia -10.8 -11.0

phosgene Cl2C=O -54.9 -51.9, -52.4

methyl fluoride -58.3 -59aminotriazole 45.2 47.6tetraHpyrimidine 18.9 13.2acrylonitrile 46.2 43.2

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

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Example: Molecular Sieving

“Real Difficult”Separation

DetailedModeling

OtherSeparationMethods

Desired Product + Impurities

Calculate Characteristic Sizes

Dprod – Dimp > ~ 0.3 A ?

“Easy” Separation

Find Zeolite such thatDprod > Dzeo > Dimp

yes

nono

Difficult Separation

Calculate Polarizability,Dipole, Quadrupole Moments

Significant Difference ?

Find Appropriate Material

Experiment

yes

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Grand Canonical Monte CarloA classical force-field method where molecules interact and are ...

X

MOVED

INSERTED

DESTROYED

... 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.

,V,T N,E

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Molecular Size via Computational SievingUse a Force-Field and Grand Canonical Monte Carlo to adsorba 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.

Dslit

Dtube

Nthreshold

D = Dslit -

0

4

8

12

16

20

5.6 5.7 5.8 5.9 6 6.1 6.2

Dslit (A)

Nu

mb

er

Ad

so

rbe

d

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Zeolite Pore Diameter + Molecular Sizes

0

1

2

3

4

5

6

7

8

9

Siz

e (A

)

Zeolites Molecules

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.

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Molecular Size via QM Density Contours

1. Geometry optimize molecule

2. Calculate Electron Density Contours

3. Measure molecular width on computer screen

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Example: Hydrogen Storageab initio MD of H2 in SWNT

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)

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.

HExpt (kcal/mol) HSim (kcal/mol)

(95% swnt, 7-14Å) (7.8Å, 11.8Å) 4 – 4.8 4.8, 3.3

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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.

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Mass Clouds and Clusters in COMP Xenon (blue) & Water (red)

Blue = Simulation, Black = X-Ray Diffraction

Cluster Number

Occupancy Minimum Energy

(kcal/mol)

Extent (Å)

Rmin (Å)

Ravg (Å)

* 2 0.97 -7.30 2.3 0.20 0.26 * 3 0.98 -6.89 3.7 0.71 0.39 * 26 0.94 -5.91 3.8 0.54 0.29 * 11 1.00 -6.30 3.9 0.31 0.31 * 15 0.92 -6.14 3.9 0.26 0.43 * 10 0.96 -6.37 4.0 0.40 0.24 * 24 0.94 -5.93 4.3 1.91 0.38 4 0.97 -6.68 4.5 6 0.98 -6.56 5.4 8 0.91 -6.46 5.5 1 1.03 -7.31 5.8 * 45 1.03 -5.63 5.9 1.50 0.67 Clusters with Occupancy > 0.9 sorted by extent. Clusters assigned with Rmax = 3Å, Rtest = 1Å, Etest = 0 kcal/mole. fXe = 10 bar. * Cluster corresponds closely to a site found in experiment.

•GCMC + Force Field Reproduces Experimental Binding Locations

•Occupancy + Input Fugacity Equilibrium Constant Free Energy

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Thinking Like a Chemical Engineer

Process Analysis Optimization Control

Quantum, Classical & Statistical Mechanics

Transport Thermodynamics Kinetics

Unit Operations

Particles

Processes

time applicationSpecific ProblemFood

Materials Design Environment

Biological Systems

Production

Surface SciencePetroleum

Energy

Semiconductors

Pharmaceuticals

Polymers

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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.

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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.

Thank you

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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.

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Binding Equilibrium and Free Energy

Source

Equilibrium Constants

Binding Free Energies

(kcal/mol)

Cavity Occupancy

@ 8 bar T (K) K1 K2 K12 G1 G2 G12

a 273 5.0 8.4 1.7 -0.87 -1.15 -0.28 1.95 b 273 1.4 a 298 2.0 1.3 0.65 -0.41 -0.17 0.24 1.82 c 300 0.31 0.56d 0.70 0.35 0.80e

-0.35f 1.88g

a. GCMC @ 8 bar xenon in T4 lysozyme. Ki from slopes pi/po vs f/Po. b. Quillen et al. XRD. c. Mann & Hermans MD simulations. d. Corrected. e. G12 from independent simulation. f. G12 calculated self-consistently from G1 and G2. g. Recalculated from correct K1, K2.

Occupancy + Input Fugacity Equilibrium Constant Free Energy