Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

In the past couple of years, machine learning (ML) has been widely leveraged in discovering functional materials. However, several difficulties seriously impede the application of ML in the field of thermoset shape memory polymers (TSMPs), e.g., the intractable feature identification or fingerprinting, inadequate experimental data on recovery stress, programming stress, strain, and lack of multilength scale structural information. Hence there is currently a lack of studies towards ML-assisted discovery of TSMPs. In this study, we propose a series of methodologies to cope with the difficulties, i.e., adopting the most recently proposed linear notation BigSMILES in fingerprinting, supplementing existing dataset by reasonable approximation, leveraging a mixed dimension (1D and 2D) input model, and a type of dual-convolutional-model framework. By doing these, a new ML framework for predicting the recovery stresses of TSMPs is developed, which is validated by synthesizing and testing two new epoxy networks predicted by the ML model. By forging new TSMPs space with 4,459 samples, the ML model identified and screened 14 mostly unknown TSMPs with greater recovery stress than the known TSMPs. One of the 14 predicted polymers was validated by molecular dynamics (MD) simulation. This study demonstrates the capability of our methodologies for discovering new TSMPs with desired recovery stress by a small training dataset, and may be adopted for discovering new TSMPs with other desired properties.

Automated summary

This study proposes methodologies to address the difficulties in applying ML in the field of thermoset shape memory polymers (TSMPs) and develops a new ML framework for predicting the recovery stresses of TSMPs. By using these methods, 14 mostly unknown TSMPs with greater recovery stress than the known ones were identified and one of them was validated by molecular dynamics simulation.