Molecular topological deep learning for polymer property prediction
<|reference_start|>Molecular topological deep learning for polymer property prediction: 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.<|reference_end|>
@article{shen2024molecular,
    title={Molecular topological deep learning for polymer property prediction},
    author={Cong Shen, Yipeng Zhang, Fei Han and Kelin Xia},
    journal={arXiv preprint arXiv:2410.04765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04765},
    primaryClass={cond-mat.mtrl-sci cs.AI cs.LG}
}