# Presentation: 311432

## Molecular Modeling and Simulation:Emerging Tools for Physical Properties Prediction

- Peter T. Cummings*
- Departments of Chemical Engineering, Chemistry and Computer ScienceUniversity of Tennessee
- Chemical Technology DivisionOak Ridge National Laboratory
- 14th Symposium on Thermophysical Properties
*Boulder, CO*
- June 25-30, 2000

## If you believe “The Matrix”.....

- There are no experimental data, only simulated data!

## My Perspective

- Theoretician (mid 1970s to mid 1980’s)
*Integral equations*
- Analytic (leading to equations of state), numerical
- Molecular fluids, chemically reacting systems
- Experiment (mid 1980’s to early 1990s)
*Mixed solvent electrolyte systems*
- Phase equilibria, density measurements
- Process Design (mid 1980’s to early 1990s)
*Introduced simulated annealing to chemical process optimization*
- Heat exchanger networks, pressure relied header networks, batch scheduling
- Molecular simulation (mid 1980’s onwards)
*Began with transport properties (NEMD)*
*Now...*
- Phase equilibria, force fields
- Lubricants, polymers, reversed micelles, supercritcal fluids, aqueous solutions
- Hi-fidelity, frequently on parallel computers

## Molecular Modeling

- Molecular simulation
*Molecular dynamics*
- Solve dynamical equations of motion for positions, velocities of atoms
*Monte Carlo*
- Generate configurations of equilibrium system stochastically according to know distribution
*Both require intermolecular and intramolecular potentials (force fields) as input*
- Computational quantum chemistry
*Solve Schrödinger equation numerically*
- Computationally intensive even for small molecules
- In principle, yields exact electronic structure and energy as limiting case of increasingly accurate methods (HF, MP2, MP4,...)
- Density functional theory (DFT) is approximate but fast

## Hierarchyof Scales

- Hierarchyof Scales

## Molecular Simulation

- Force field development dominated by requirements for drug design
*25-35**o**C, <5 bar*
- Chemical processing requires force fields valid over wide ranges of temperature and density/pressure
*Vapor-liquid equilibrium, supercritical fluids,.... *
- Development of such force fields is rapidly growing in wake of new methods developments
*GEMC, Gibbs-Duhem, ...*

## Molecular Simulation vs Theory

- Advances in computational hardware and algorithms
*Moore’s law*
- Computing speeds double every 18 months = order of magnitude every 5 years
- Add 2-3 orders of magnitude from parallelization (cheap today)
- Costs driven by consumer market
- Costs for experiment?
*Labor-intensive, high capital costs*
- Costs for theory?
*Labor-intensive**2** *

*Do graduate students and/or lab personnel/equipment improve by an order of magnitude every five years?*

## Molecular Modeling

- International comparative study on applying molecular modeling
*Goal to evaluate ways in which molecular modeling being applied, primarily in industry, throughout the world*
*US funding agencies*
- NSF, DOE, _x0008_NIST, DARPA, AFOSR, NIH,...
*Over 75 sites visited worldwide*
- Primarily companies, mostly in Europe, Japan and US)
*Report to be published in late 2000*
*Web site*
-  http://www.itri.loyola.edu/molmodel

## Molecular Modeling

- Three main roles:
*Predicting fundamental properties used in engineering correlations*
- E.g., critical constants, molecular structure, dipole moment
*Predicting required properties directly*
- E.g., phase equilibrium of mixture
*Providing conceptual molecular-level understanding of properties*
- E.g., developing correlations, evaluate theory,guide/supplement/replace experiment
- Additional roles
*Intellectual property protection (defense and offense)*
*Development of QSAR/QSPR for product design applications*

## The Chemical Engineering Imperative

- Where were the fluids people (theory, simulation, experiment) in the past (US)?
*Chemistry**, physics, mechanical engineering, **chemical engineering*
- Where are the fluids people today (US)?
*Chemical engineering**, chemistry, **mechanical engineering**, **physics*
*Part of a general emphasis on molecular processes in chemical engineering*
- Biochemical engineering, catalysis, polymers, ....
*Evident in the changing demographics of the symposium attendees*
- Influx of non-ChEs into ChE

## Process Design Overview

- Three stages (Zeck and Wolf, 1993; Douglas, 1988)
*Stage I: **	**Process screening*
- Elimination of most alternatives
- Only ~1% of proposed chemical processes result in commercial production
- Use of shortcut methods for design calculations (material and energy balances)
- Modest data accuracy requirements (total cost accurate to ±25%)
- 90% or more of designs eliminated using these methods
*Stage II:**	**Process development*
- Detailed economic assessment of several process alternatives
*Stage III:**	**Process design*
- Detailed design and optimization of chosen process

## Properties Required for Process Screening

- Physical properties frequently estimated by engineering correlations or measured by simple, low-cost experiment (e.g., infinite dilution activity coefficients by gas chromatography)
- Accuracy of ±25% acceptable in cost estimates
- Demands for data accuracy vary (Larsen, 1986)
*20% error in density ---> 16% error in equipment size/cost*
*20% error in diffusivity ---> 4% error in equipment size/cost*
- Errors in density usually small for liquids, errors in diffusivity frequently large (factor of two or more)
*10% error in activity coefficient results in negligible error in equipment size/cost for easily separated mixtures, but for close-boiling mixtures (relative volatility <1.1) 10% error can result in equipment sizes off by factor of 2 or more*
- Thermophysical properties data must be accompanied by accuracy assessment

## Potential Impact of Molecular Modelingon Process Design

- Provision of physical properties data at Stage I and possibly Stage II
- Guidance for experimental studies at Stages II and III
*What are the troublesome mixtures and/or state conditions?*
- Dual role
*Provide raw physical properties "data" required for correlations (indirect)*
*Provide directly properties of pures and mixtures*
- Example from thermochemistry - enthalpy of reaction
*“**Now in maintenance mode”*
- Five years ago: Experiment cost $50,000, computation $20,000
- Today: Experiments cost over $100,000, computation $5,000

## Computational Chemistry

- Industrial Case Histories
*Compiled by Phil Westmoreland, UMass*

## Computational Chemistry

- Industrial Case Histories
*Compiled by Phil Westmoreland, UMass*

- Hydrocarbon-chlorine reactions, anti-corrosion additives

- Monolithic catalysts

- Designed detergent enzyme

- Diffusion in porous media

- Setting of cements

- Solvent separations, catalysis

- Catalysis, materials

- Modify and develop materials

## Critical Properties of Alkanes

- Siepmann *et al.* (1993)

## Lubricant Characterizations

- Viscosity number (VN) (McCabe et al., *FOMMS 2000 Proceedings*, accepted (2000))
*Definition:*
- Use kinematic viscosities at 40oC and 100oC, fit Walther equation

## Pressure-viscosity coefficient (McCabe et al., Fluid Phase Equilibria, submitted (2000))
Definition

Interest is at high pressure (GPa  level)
For 9-octylheptadecane: 
Experiment:  = 5.88 GPa-1
NEMD simulation:  = 5.66(0.04) GPa-1

*Definition*
*Interest is at high pressure (GPa  level)*
- For 9-octylheptadecane:
- Experiment:  = 5.88 GPa-1
- NEMD simulation:  = 5.66(0.04) GPa-1

- Other Lubricant Characterizations

## Rheology of Perfluoroalkanes

- Discrepancy between DIPPR correlation [1] and Oak Ridge correlation [2]
*Simulations clearly show DIPPR correlation incorrect*
*Perfluorobutane flagged by DIPPR for review*

*[1] D. Van Velzen; Lopes; H. Langenkamp; R. Cardozo “Liquid Viscosity and Chemical Constitution of Organic Compounds. A New Correlation and a Compilation of Literature Data,” Commission of the European Communities, 1972*
*[2] D. Harkins “Evaluation of Available Perfluorobutane data for selected physical properties,” Oak Ridge Gaseous Diffusion Plant, 1990*

## 2,6,10,15,19,23-hexamethyltetracosane (squalane)

Prediction [1]
Tc = 800K, c =  0.219 g/cm3
Engineering correlation (DIPPR database)
Tc = 863K (8% higher than simulation), c =  0.258 g/cm3 (18% higher than simulation)
Measurement (Bill Steele, 2000, private commun.)
Within -1% of simulation for Tc,  within few % of simulation for c

*Prediction [1]*
*T**c** *= 800K, ***c* =  0.219 g/cm3
*Engineering correlation (DIPPR database)*
*T**c** *= 863K (8% higher than simulation), ***c* =  0.258 g/cm3 (18% higher than simulation)
*Measurement (Bill Steele, 2000, private commun.)*
- Within -1% of simulation for *T**c*,  within few % of simulation for ***c*

- [1] Cui, S. T., Cochran, H. D. and Cummings, P. T., “Configurational Bias Gibbs-Ensemble Monte Carlo Simulation of Vapor-Liquid Equilibria of Linear and Short-Branched Alkanes,” *Fluid Phase Equilibria*, **141** (1997) 45-61.

- Phase Equilibria

## The Future

- By 2020, many routine thermophysical and thermochemical properties of low molecular weight systems will be predictable computationally at better accuracy and higher precision than experiment
- _x0008_Increasing emphasis on prediction by simulation
- Theory and experiment will continue to be essential for systems challenged by simulation
*Long relaxation times (polymers, glasses)*
*Materials whose properties are determined at scales larger than molecular (mesocale)*
*Materials in which quantum physical and/or chemical processes are important *
- Progress in simulation is possible only with new theories for bridging time scales

## Web Site: http://www.ecs.umass.edu/FOMMS