The Polymer Society of Korea

Abstract

Recent advances in molecular simulation and machine learning technologies are fundamentally reshaping the framework of the discovery and optimization of polymeric materials, quickly replacing the traditional concept of iterative experimentation guided by chemical intuition. Here, we showcase an advanced digital platform technology that combines physics-based molecular simulations with machine learning algorithms to develop novel polymer materials, effectively navigating the vast macromolecular design spaces. Case studies will include, but are not limited to, designing copolymers for semiconductor packaging, optimizing thermochemistry of acrylate-based coating, and assessment of thermal oxidation in thermoset resins. The work demonstrates a major shift of paradigm in the usage of information technology in materials research with broad implications in product lifecycle management, positioning digital chemistry as a cornerstone of the next-generation polymer industry.