Molecular Modeling in Chemical Industry R&D

1. Molecular Modeling in Chemical Industry R&D Brian Peterson May 7, 2004

2. Outline • What Molecular Modeling is and is not • Examples • How MM relates to Chemical Engineering • Thoughts on Curriculum

3. (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

4. 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

5. 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

6. 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

7. 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

8. 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.

9. 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

10. 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.

11. Measure molecular width on computer screen Molecular Size via QM Density Contours • Geometry optimize molecule • Calculate Electron Density Contours

12. 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)

13. 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.

14. 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

15. 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

16. 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.

17. 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.

18. Thank you

19. 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.

20. Binding Equilibrium and Free Energy Occupancy + Input Fugacity  Equilibrium Constant  Free Energy