Multi-modal cascade feature transfer for polymer property prediction

TL;DR

This paper introduces a multi-modal cascade transfer learning model that combines diverse polymer data types to improve the accuracy of predicting polymer physical properties.

Contribution

It presents a novel multi-modal cascade model that integrates features from chemical structures, molecular descriptors, and additive information for enhanced polymer property prediction.

Findings

The proposed model outperforms traditional single-feature approaches.

Empirical evaluation shows high predictive accuracy across multiple datasets.

Combining multiple data modalities improves prediction robustness.

Abstract

In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. However, in conventional approaches, prediction models were often constructed using each type of data separately. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by graph convolutional neural networks (GCN) with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets. We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature.