Process Systems Engineering

Edited by Efstratios N. Pi sti ко рои los, Michael С Georgiadis, and Vivek Dua

Volume 6: Molecular Systems Engineering

Volume Edited by Claire S. Adjiman and Amparo Galindo

WILEY-VCH

WILEY-VCH Verlag GmbH & Co. KGaA

Contents

Preface XI

List of Contributors XV

1 CrystalOptimizer: An Efficient Algorithm for Lattice Energy

Minimization of Organic Crystals Using Isolated-Molecule Quantum

Mechanical Calculations 1

A.V. Kazantsev, P.G. Karamertzanis, C.C. Pantelides and C.S. Adjiman 1

1.1 Introduction and Background 1 1.1.1 Polymorphism 1

1.1.2 Structure Determination and Thermodynamic Stability 3 1.2 Lattice Energy Calculation 5 1.2.1 Intermolecular Energy Calculation 6 1.2.2 Intramolecular Energy Calculation: From the Rigid-Body

Assumption to Inclusion of Molecular Flexibility 6 1.2.3 Accurate Lattice Energy Minimization of Crystals Containing

Flexible Molecules (DMAflex) 8 1.3 CrystalOptimizer: Minimization Using LAMs 10 1.3.1 LAM for the Intramolecular Energy 11 1.3.2 LAM for the Intermolecular Electrostatic Potential 17 1.3.3 LAM-Based Lattice Energy Minimization Algorithm 20 1.4 Results and Discussion 26 1.4.1 Model Systems and Computational Methodology 26 1.4.2 Lattice Energy Minimization 28 1.5 Conclusions 38

2 An Introduction to Coarse-Graining Approaches: Linking Atomistic

and Mesoscales 43

N. Chennamsetty, H. Bock, M. Lisal andJ.K. Brennan 43 2.1 Introduction 43 2.2 Rigorous Coarse Graining: Partition Function Matching 45

Process Systems Engineering. Vol. 6 Molecular Systems Engineering Edited by Claire S. Adjiman and Amparo Galindo Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-31695-3

VI Contents

2.3 Coarse Graining by Matching a Specific Property 54 2.3.1 Structure Matching 54 2.3.1.1 Potential of Mean Force 55 2.3.1.2 Integral Equations 56 2.3.1.3 Iterative Boltzmann Inversion 57 2.3.2 Force Matching 59 2.4 Coarse Graining for Specific Mesoscale Simulation Techniques 62 2.4.1 Bottom-Up Coarse Graining for DPD 62 2.4.1.1 Coarse Graining onto DPD Particles 62 2.4.1.2 Structure-Matching Approach 63 2.4.2 Bottom-Up Coarse Graining for DDFT 67 2.4.2.1 Coarse Graining onto Mesoscopic Beads 67 2.4.2.2 Flory-Huggins Interaction Parameters for Mesoscopic Beads 69 2.4.3 Top-Down Coarse Graining for DPD and DDFT 70 2.4.4 Illustrative Examples 73 2.5 Conclusions and Future Outlook 77 A Dissipative Particle Dynamics 79 В Dynamic Mean-Field Density Functional Theory 80

3 Hierarchical Modeling of Polymeric Systems at Multiple Time and Length Scales 85 G. Tsolou and V.G. Mavrantzas 85

3.1 Introduction 85 3.2 Atomistic Molecular Dynamics and Monte Carlo Simulation of

Polymers: Basic Concepts and Recent Developments 86 3.2.1 Molecular Model and Initial Configuration 86 3.2.2 Molecular Dynamics 87 3.2.3 Mapping Atomistic MD Data onto the Rouse and Reptation Models

of Polymer Dynamics 88 3.2.4 Monte Carlo 91 3.2.5 Iterative Boltzmann Inversion and Monte Carlo 93 3.2.6 Parallel Tempering and Monte Carlo 94 3.3 Atomistic Molecular Dynamics and Monte Carlo Simulation of

Polymers: Applications 95 3.3.1 Chain Self-Diffusion Coefficient D and Monomer Friction

Coefficient t, 95 3.3.2 Tube Diameter 96 3.3.3 Zero-Shear Rate Viscosity щ 98 3.3.4 Temperature and Pressure Effects on Segmental and Terminal

Relaxation 99 3.3.5 Simulation of Alkanethiol-Au(lll) Self-Assembled Monolayers 103 3.4 Techniques for the Simulation of the Solubility and Permeability

Properties of Polymers 106 3.4.1 Gibbs Ensemble Monte Carlo 107

3.4.2 Test Particle Insertion 107 3.4.3 Configurational Bias Monte Carlo 110 3.4.4 Chain Increment Ansatz 111 3.4.5 Fusion and Scission MC 112 3.4.6 Inverse Widom - Test Particle Deletion 112 3.4.7 Grand Canonical MD 113 3.4.8 Thermodynamic Integration 113 3.4.9 Extended Ensemble MD 114 3.4.10 Fast-Growth Thermodynamic Integration 115 3.4.11 Diffusion in Rubbery and Amorphous Polymers Above the Glass

Transition 115 3.4.12 Diffusion in Glassy Polymers 116

3.4.13 Gusev-Suter's TST Method 117 3.4.14 Multidimensional TST 119

3.4.15 Kinetic Monte Carlo 120 3.5 Current Trends 121 3.5.1 Expanded Ensemble MC Coupled with NEMD 121 3.5.2 Dissipative Particle Dynamics 123 3.5.3 Simulations Based on Self-Consistent Field Theories -

Coarse-Grained MC Simulations of Block Copolymers and Nanocomposites 225

3.6 Conclusions and Outlook 127

4 Group Contribution Methodologies for the Prediction of

Thermodynamic Properties and Phase Behavior in Mixtures

V. Papaioannou, CS. Adjiman, G.Jackson and A. Galindo 4.1 Introduction 135

4.2 Pure Component GC Methods 136 4.2.1 First-Order Pure Component Methods 136 4.2.2 Second-Order Pure Component Methods 138 4.2.3 Higher Level Pure Component Methods 139 4.2.4 Further Improvements for Pure Component Approaches 4.3 Activity Coefficient GC Methods 141 4.3.1 The ASOG Method 142 4.3.2 The UNIFAC Method 144 4.3.2.1 Limitations of the Original UNIFAC Approach 148 4.3.2.2 Modifications of the Method 148 4.4 GC Methods in Equations of State 150

4.4.1 EoS-gE Methods 151 4.4.2 GC Methods Directly Implemented in Equations of State 4.5 The Statistical Associating Fluid Theory (SAFT) 153 4.5.1 Homonuclear Approaches 154 4.5.1.1 GC-SAFT 155 4.5.1.2 GC Approaches in PC-SAFT 156

135 135

140

152

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4.5.2 Tangential Heteronudear Models in SAFT 157 4.5.3 Fused GC Heteronudear Models: SAFT-y and GC-SAFT-VR 157 4.6 Other Predictive Methods 163 4.7 Concluding Remarks 164

5 Optimization-Based Approaches to Computational Molecular

Design 173

J.C. Eslick, S.M. Shulda, P. Spencer and K.V. Camarda 173 5.1 Introduction and Motivation 173 5.1.1 Motivation for Computer-Aided Molecular Design 173 5.1.2 CAMD Methodology 174 5.1.3 Chapter Overview 175

5.2 Quantitative Structure-Property Relationships 276 5.2.1 Group Contribution Approaches 176 5.2.2 Topological Descriptors 177 5.2.3 Regression Methods for Generation of QSPR Models 177 5.2.4 Use of Molecular Simulation for QSPR Development 178 5.3 Problem Formulations for CAMD 179 5.3.1 General Overview 179 5.3.2 Problem Formulations for Use with Enumerative Algorithms 179 5.3.3 Problem Formulations for Use with Deterministic Algorithms 180 5.3.4 Problem Formulations for Use with Stochastic Algorithms 180 5.4 Mathematical Techniques for the Solution of CAMD Optimization

Problems 181 5.4.1 Enumerative Approaches 181 5.4.2 Global Optimization Algorithms 181 5.4.3 Stochastic Algorithms 182 5.5 The Tabu Search Algorithm 182 5.5.1 Overview and Background 182 5.5.2 Application to CAMD 183 5.6 Case Study 184 5.6.1 Overview 184 5.6.2 Generation of QSPR Models 184 5.6.3 Problem Formulation 187 5.6.4 Results 189 5.6.5 Case Study Conclusions 190 5.7 Conclusions and Future Directions 191

6 Molecular Modeling of Formulated Consumer Products 195

B.P. Murch, K.L. Anderson, P. Verstraete, W. Laidig, D.T. Stanton, D.M. Eike and P. Koenig 195

6.1 Introduction 195 6.2 Performance Properties of Complex Liquid Formulations 196 6.3 Stability Assessment of Multiphase Formulations 200

6.4 Process Factors: Metastable States of Multiphase Mixtures 204 6.5 Summary 206

7 Recent Advances in De Novo Protein Design 207

M.L. Bellows, H.K. Fung and C.A. Floudas 207 7.1 Introduction 207 7.2 De Novo Approach with Fold Specificity 209 7.2.1 Stage One: In Silico Sequence Selection 210 7.2.1.1 Sequence Selection for a Single Template Structure 210 7.2.1.2 Sequence Selection for Multiple Template Structures 211 7.2.2 Stage Two: Approximate Fold Specificity Calculation 213 7.3 De Novo Approach with Approximate Binding Affinity 215 7.3.1 Stage 1: In Silico Sequence Selection 215 7.3.2 Stage 2: Ranking Metric Based on Approximate Binding Affinity

Calculation 215 7.3.2.1 Structure Prediction 215 7.3.3 Docking Prediction 216 7.3.3.1 Approximate Binding Affinity Calculation 217 7.4 Applications and Representative Results 219 7.4.1 HLA-DR1 in Complex with an Influenza Virus Peptide 219 7.4.2 Complement Component C3c in Complex with Compstatin 220 7.4.3 Inhibitors for HIV-1 gpl20 and HIV-1 gp41 225 7.5 Summary 226

8 Principles and Methodologies for the Controlled Formation of

Self-Assembled Nanoscale Structures with Desired Geometries 233

E.O.P. Soils, P.I. Barton and G. Stephanopoulos 233 8.1 Overview of the Controlled Nanostructure Formation Approach 234 8.1.1 Formulation of the Problem 235 8.1.2 Design Principles for the Proposed Methodology 236 8.2 Statistical Mechanics and Ergodicity 249 8.3 Methodological Procedures for the Controlled Formation of

Desired Nanostructures 252 8.3.1 Shaping the Energy Landscape for Local Stability of the Desired

Nanostructure 252 8.3.1.1 Shaping the Energy Landscape for ID Structures 252 8.3.1.2 Shaping the Energy Landscape for 2D Structures 255 8.3.2 Shaping the Energy Landscapes for Robustness of the Desired

Nanostructure: A Combinatorially Constrained Optimization Problem 256

8.3.2.1 Defining the Ergodic Component a 257 8.3.3 Dynamic Evolution: Multiscale Formation of Local Ergodic

Subsystems 260 8.4 Summary 263

X Contents

9 Computer-Aided Methodologies for the Design of Reaction Solvents 267 H. Strübing, S. Konstantinidis, P.G. Karamertzanis, E.N. Pistikopoulos, A. Galindo and C.S. Adjiman 267

9.1 Introduction 267 9.1.1 A Brief Overview of Computer-Aided Molecular Design 268 9.1.1.1 The "Generate-and-Test" Approach 269 9.1.1.2 The Optimization-Based Approach 269 9.1.2 Solvent Design for Reactions 270 9.2 Solvent Effects on Reactions and the Transition-State Theory 271 9.3 Capturing Solvent Effects with an Empirical Approach 275 9.3.1 The Solvatochromic Equation: General Background 276 9.3.2 The Solvatochromic Scales 278 9.3.2.1 Solvent Solvatochromic Scales 278 9.3.2.2 Solute Solvatochromic Scales 279 9.3.2.3 Use of Solute Scales in the Modeling of Solvent Effects on

Reactions 279 9.3.3 Colinearity in the Solvatochromic Equation 280 9.4 Solvent Design for an SN2 Reaction with an Empirical Model 281 9.4.1 Introduction 281 9.4.2 The Menschutkin Reaction 282 9.4.3 The Solvatochromic Equation for the Menschutkin Reaction 283 9.4.4 Problem Formulation 286 9.4.4.1 Design Variables 287 9.4.5 Structure-Property Constraints, hi = 0 and gi ^ 0 287 9.4.5.1 Abraham's Hydrogen-Bond Acidity (A) 287 9.4.5.2 Abraham's Hydrogen-Bond Basicity (B) 289 9.4.5.3 Dipolarity/Polarizability Parameter (S) 291 9.4.5.4 Polarizability Correction Parameter (8) 292 9.4.5.5 Cohesive Energy Density {&2

H) 293 9.4.5.6 Melting Point 293 9.4.6 Chemical Feasibility Constraints, hi = 0 and g2 < 0 294 9.4.6.1 Types of Molecules 294 9.4.6.2 Octet Rule 294 9.4.6.3 Aromatic Molecules 295 9.4.6.4 Forcing и, to be an Integer 295 9.4.6.5 Modified Bonding Rule 295 9.4.6.6 Chemical Complexity Constraints 296 9.4.6.7 Side Chains 297 9.4.7 Design Constraints d < 0 299 9.4.8 Integer Cuts 299 9.4.9 Results 299 9.5 Concluding Remarks 300

Index 307