ChemFlow：一种用于化学混合物多尺度表示学习的分层神经网络
ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures
AI总结：
ChemFlow通过分层神经网络整合原子、功能团和分子级特征，有效预测化学混合物的物理化学性质，优于现有模型。
摘要：
准确预测分子混合物的物理化学性质仍是一个重大挑战，因为这需要同时嵌入分子内相互作用，同时考虑混合物组成（即浓度和比例）。现有方法无法模拟现实的混合环境，其中紧密耦合的相互作用在不同层级间传播——从原子和功能团到整个分子，并且跨层级的信息交换持续受到组成的影响。为了弥合孤立分子与现实化学环境之间的差距，我们提出了ChemFlow，一种新的分层框架，整合了原子、功能团和分子级的特征，促进这些层级间的信息流动以预测复杂的化学混合物行为。ChemFlow采用原子级特征融合模块Chem-embed，生成受混合状态和原子特性影响的上下文感知原子表示。接下来，双向的团到分子和分子到团注意力机制使ChemFlow能够捕捉混合物中功能团内的相互作用以及跨分子的相互作用。通过动态调整表示以适应浓度和组成，ChemFlow在预测浓度依赖性质方面表现出色，并在浓度敏感和浓度不敏感的系统中显著优于最先进的模型。广泛的实验表明，ChemFlow在建模复杂化学混合物方面具有优越的准确性和效率。
英文摘要：
Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
