Molecular topological deep learning for polymer property prediction

• URL: http://arxiv.org/abs/2410.04765v1
• Date: Mon, 7 Oct 2024 05:44:02 GMT
• Title: Molecular topological deep learning for polymer property prediction
• Authors: Cong Shen, Yipeng Zhang, Fei Han, Kelin Xia,
• Abstract summary: We develop molecular topological deep learning (Mol-TDL) for polymer property analysis. Mol-TDL incorporates both high-order interactions and multiscale properties into topological deep learning architecture.
• Score: 18.602659324026934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and efficient prediction of polymer properties is of key importance for polymer design. Traditional experimental tools and density function theory (DFT)-based simulations for polymer property evaluation, are both expensive and time-consuming. Recently, a gigantic amount of graph-based molecular models have emerged and demonstrated huge potential in molecular data analysis. Even with the great progresses, these models tend to ignore the high-order and mutliscale information within the data. In this paper, we develop molecular topological deep learning (Mol-TDL) for polymer property analysis. Our Mol-TDL incorporates both high-order interactions and multiscale properties into topological deep learning architecture. The key idea is to represent polymer molecules as a series of simplicial complices at different scales and build up simplical neural networks accordingly. The aggregated information from different scales provides a more accurate prediction of polymer molecular properties.

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