1 Preface 2 Introduction 2.1 The Uniform Distribution and the Generation of Random Numbers 2.2 Polymerizations are Stochastic Processes 2.3 A Preliminary Monte Carlo Simulation Algorithm 2.4 Chain Length and Molecular Weight Averages 2.5 Final Thoughts 2.6 Extra Readings 3 Steady State Monte Carlo Models 3.1 Case Study 3.

1: Styrene Free-Radical Polymerization 3.2 Case Study 3.2: Free-Radical Copolymerization of Styrene and Methyl Methacrylate 3.3 Case Study 3.3: Coordination Polymerization with the Formation of Long Chain Branches 3.4 Final Thoughts 3.5 Extra Readings 4 Dynamic Monte Carlo Models 4.1 Case Study 4.

1 ? Part 1: Step-Growth Polymerization 4.2 Gillespie?s Method: A Stochastic Approach to Dynamic Simulation 4.3 Case Study 4.1 ? Part 2: Gillespie Algorithm for Step-Growth Polymerization 4.4 Case Study 4.1 ? Part 3: Non-Stoichiometric Step-Growth Polymerization 4.5 Reaction Networks and Competing Events 4.6 How to Select the Size of the Control Volume 4.

7 Case Study 4.2: Dynamic Styrene Free-Radical Polymerization with Gel Effect 4.8 Case Study 4.3: ATRP Copolymerization of Styrene and n-Butyl Acrylate 4.9 Case Study 4.4: Thermal Degradation of Polystyrene 4.10 Final Thoughts 4.11 Extra Readings 5 Optimization of Monte Carlo Methods 5.

1 Big O Notation and Computational Complexity 5.2 Compilation versus Interpretation 5.3 Homopolymerization Geometric Sampling 5.4 Copolymerization Geometric Sampling 5.5 Minimizing Random Number Generation Overhead 5.6 Balancing Accuracy and Speed 5.7 Disjoint Set Union for Gelation Tracking 5.8 GPU Parallel Computing 5.

9 Final Thoughts 5.10 Extra Readings 6 Epilogue: Creating Molecules to Understand Them 6.1 Lessons from Monte Carlo Simulation 6.2 Ten Rules for Writing Monte Carlo Algorithms 6.3 The Expanding Role of Monte Carlo Methods in Polymer Reaction Engineering 6.4 Final Thoughts Appendix A: Instantaneous Chain Length Distribution (CLD) of Polymers Made by Free Radical Polymerization.