Design of Thermosetting Polymers with High Thermal Stability and Enhanced Processability via ML-assisted Material Genome Approach

Phthalonitrile (PN), due to its good mechanical and thermal properties, stands out as an ideal high-performance resin. However, the conflict between heat resistance and processing property has always hindered the development and application of PN resins. We proposed a machine-learning-assisted materials genome approach to enable the practical design of PNs with excellent heat resistance and expansive processing windows. Combining domain knowledge, we constructed three property prediction models through machine learning. Subsequently, an extensive collection of candidate structures was created to screen optimal structures based on the machine learning predictions. Three PN structures were selected for proof-of-concept experiments. The measure properties of PNs agree well with the predictions, confirming the effectiveness of our strategy. The optimal PN surpasses existing performance constraints and extends processing boundaries without compromising heat resistance. In addition, the integrated gradients method was extended to analyze chemical “genes”, which deepens the understanding of the structure–property relationship of the PNs, and takes a step forward in implementing interpretable machine learning methods. The insights derived from gene analysis can assist in the design of heat-resistant polymers with excellent processing properties.