A small-data-driven model for predicting adsorption properties in polymeric thin films

Abstract

Artificial intelligence allowing data-driven prediction of physicochemical properties of polymers is rapidly emerging as a powerful tool for advancing material science. Here, we developed a methodology to use polymer adsorption data as predictable data by analyzing causal relationships between polymer properties and experimental results instead of using big polymer data.