Multi-modal cascade feature transfer for polymer property prediction
This work addresses the problem of accurate polymer property prediction for materials science, though it is incremental as it builds on existing transfer learning and multi-modal techniques.
The paper tackles polymer property prediction by proposing a multi-modal cascade model that combines features from chemical structures, molecular descriptors, and additive information, achieving higher predictive performance compared to baseline single-feature methods.
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.