A Method for Inferring Polymers Based on Linear Regression and Integer Programming
This work addresses polymer design for chemical engineering, but it is incremental as it builds on a prior framework with modifications like new descriptors and linear regression.
The paper tackles the problem of designing polymer molecular structures with desired chemical properties by proposing a method that uses linear regression and integer programming within an existing framework, achieving the ability to infer polymers with up to 50 non-hydrogen atoms in monomer form.
A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.