用拓扑神经网络提升聚合物性能预测精度
Molecular topological deep learning for polymer property prediction
- 将聚合物分子建模为多尺度单纯形复形，构建拓扑神经网络
- 在多个数据集上实现比现有模型更高的预测准确率
- 适合材料设计、高通量筛选等需要快速评估性能的场景
准确高效地预测聚合物性能对聚合物设计至关重要。传统实验方法和基于密度泛函理论（DFT）的模拟均成本高昂且耗时。近年来，大量基于图结构的分子模型涌现，在分子数据分析中展现出巨大潜力。然而，这些模型往往忽略数据中的高阶与多尺度信息。本文提出分子拓扑深度学习（Mol-TDL），将聚合物分子表示为不同尺度的单纯形复形，并构建相应的单纯形神经网络。通过融合多尺度信息，显著提升了聚合物分子性能的预测准确性。
原文摘要 · Abstract (English)
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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