Apply Monte Carlo methods to predict polymer microstructure and properties

Understanding polymer microstructure is critical for engineering functional polymers with targeted macroscopic properties. Monte Carlo Methods in Polymer Reaction Engineering: From Mechanisms to Molecular Architecture provides the first practical treatment of Monte Carlo modeling techniques applied specifically to polymer reaction engineering, equipping readers to program their own simulation algorithms.

The book progresses from simple principles to complex modeling scenarios through practical case studies and worked examples. Readers develop the ability to build Monte Carlo algorithms for solving specific polymerization problems. Coverage spans different Monte Carlo methods in a unified framework, connecting reaction mechanisms directly to molecular architecture predictions for real-world polymer systems.

Readers will also find:

• A didactic structure that builds from foundational Monte Carlo principles to advanced polymer reaction engineering applications with increasing complexity
• Practical case studies demonstrating how Monte Carlo methods predict and control polymer microstructure in academic and industrial settings
• Unified coverage of multiple Monte Carlo methods presented within a single coherent framework tailored to polymer reaction engineering problems
• Worked examples showing how reaction mechanisms connect to molecular architecture predictions for functional polymer design

Designed for polymer chemists, chemical engineers, materials scientists, and plastics technologists in both academia and industry, this book provides the practical tools needed to apply Monte Carlo simulation to polymerization systems. PhD students in materials science and engineering sciences will find it a direct path to developing their own modeling capabilities.

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