Toward Diverse Polymer Property Prediction Using Transfer Learning

TL;DR

This paper demonstrates that transfer learning with neural networks can accurately predict multiple diverse properties of linear polymers using small datasets, improving polymer property prediction efficiency.

Contribution

The study introduces a transfer learning approach to predict multiple polymer properties, showing high accuracy with limited data and evaluating different loss functions for optimal performance.

Findings

High accuracy in predicting four polymer properties with small datasets.

Combined loss function outperforms individual loss functions.

Transfer learning enhances property prediction efficiency.

Abstract

The prediction of mechanical and thermal properties of polymers is a critical aspect for polymer development. Herein, we discuss the use of transfer learning approach to predict multiple properties of linear polymers. The neural network model is initially trained to predict the heat capacity in constant pressure (Cp) of linear polymers. Once, the pre-trained model is transferred to predict four additional properties of polymers: specific heat capacity (Cv), shear modulus, flexural stress strength at yield, and tensile creep compliance. They represent a diverse set of mechanical, thermal, and rheological properties. We demonstrate the effectiveness of the approach by achieving high accuracy in predicting the four additional properties using relatively small datasets of 13 to 18 samples. Also, the performance of the base model is examined using five different loss functions. Our results suggest that the combined loss function had better performance compared to the individual loss functions.