Multimodal Modeling for Polymer Property Prediction and Decoupling of Structure-Property Relationship.

ABSTRACT: The key challenge in the development of a target property polymer stems from the unclear structure-property relationship (SPR). A multimodal graph neural network (GNN) framework, GeoALBEF, is proposed to address this problem. GeoALBEF introduces a training process based on an "Align before Fuse" architecture that optimizes a three-stage loss, which deeply fuses polymer graph information and text information. Benchmark tests constructed based on 5,000 polymer samples showed that GeoALBEF reduced the mean relative RMSE by 8.6% compared to the suboptimal model in the prediction task of 24 key properties in 6 categories. It is especially worth pointing out that the model achieves functional group level interpretability through the attention mechanism and the functional group averaging strategy

SUBMITTER: Lv R 

PROVIDER: S-EPMC12746000 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature