Active learning for the design of polymerization reactions
Project Description
Polymer is an important class of materials that are used in almost every aspect of our daily lives. The property of polymers has great tunability due to the infinite possibility of polymer microstructures, including the variation of monomer types, polymer chain length, composition and sequence distributions. On the other hand, this also creates a large design space for polymers and it is becoming increasingly important to develop computational approaches to simulate polymerization systems to design the reaction recipe.

It is therefore necessary to navigate the design space effectively. Active machine learning algorithms have been developed for this purpose, where a machine learning is iteratively trained, with a strategy to maximize the information gain with new data collected. In this project, you will use kinetic Monte Carlo (KMC) simulations for generating polymerization reaction data, and an active learning framework to maximize the efficiency of (simulated) data collection.
GAO Hanyu
Course type
Applicant's Roles
In this project, you will develop KMC and continuum simulation models for radical polymerizations with different mechanisms, including conventional free radical polymerization and controlled radical polymerization (e.g. ATRP, RAFT), with or without chain transfer, branching and crosslinking. This work will provide foundations for further algorithm and software development for designing and controlling polymerization processes.
Applicant's Learning Objectives
Through working on the project, you are expected to gain the following experiences/expertise:
1. Mathematical programming used in chemical process simulation;
2. Data structure knowledge and practice for recording polymer chain architecture;
3. Polymer science and polymer reaction engineering
Complexity of the project