Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios
arXiv:2301.01231v11 citationsh-index: 11
Originality Incremental advance
AI Analysis
This work addresses a domain-specific challenge in polymer chemistry by providing a computational tool for reaction engineering.
The paper tackled the problem of predicting comonomers reactivity ratios in polymerization reactions using machine learning, achieving predictions based on molecular structures.
Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.