Work

Multi-modal cascade feature transfer for polymer property prediction

Year: 2025

Type: article

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 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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae023e

Authors: Kiichi Obuchi, Yuta Yahagi, Kiyohiko Toyama, Shukichi Tanaka, Kota Matsui

Institutions: NEC (Japan), National Institute of Advanced Industrial Science and Technology, Kyoto University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funder: Japan Society for the Promotion of Science