AI 中文总结
本研究提出基于预训练分子编码器的混合物感知三维分子表示学习策略，可精准预测有机混合物的粘度与密度，性能优于传统方法，还能捕捉非单调粘度变化，为功能性流体配制提供工具。
AI 中文摘要
有机混合物的粘度和密度是设计润滑剂、溶剂及传热流体的关键性质。在工程实践中，配制功能性流体需明确这些性质随组成和温度的变化规律，但由于可能的物质及组合数量庞大，对全参数空间开展 exhaustive 实验表征并不现实。本文提出一种基于预训练分子编码器的混合物感知三维分子表示学习策略，该策略联合编码组分结构、摩尔分数和温度，以实现对有机混合物的精准预测。在涵盖多种二元有机混合物的公开数据集上进行微调后，模型在测试集上的动态粘度R²值达0.973，密度R²值达0.996，显著优于传统机器学习基线。除整体精度外，该模型可捕捉混合时粘度的非单调变化，超越简单线性或对数混合规则。其架构可扩展至三元及多组分混合物，初步实验已验证这一点。利用该模型，本文定量分析了分子结构（支化度、环烷烃、芳香环）对粘度-温度行为的影响，这有助于设计具备优异粘温性能的润滑剂。综上，本研究为混合物性质预测提供了实用的数据驱动工具，可加速化学工程中功能性流体的理性配制。
英文摘要
The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of possible species and combinations. Here we introduce a mixture-aware 3D molecular representation learning strategy, built upon a pre-trained molecular encoder, that jointly encodes component structures, mole fractions, and temperature to achieve accurate predictions for organic mixtures. Fine-tuning on publicly available datasets covering a wide range of binary organic mixtures yields test-set R2 values of 0.973 for dynamic viscosity and 0.996 for density, significantly outperforming traditional machine learning baselines. Beyond this overall accuracy, the model captures non-monotonic viscosity changes upon mixing, surpassing simple linear or logarithmic mixing rules. The architecture is extendable to ternary and multicomponent mixtures, as verified via preliminary experiments. Using this model, we quantitatively analyze how molecular structure-branching, cycloalkane, and aromatic rings-affects viscosity-temperature behavior, which benefits the design of lubricants with superior viscosity-temperature performance. Altogether, this work provides a practical, data-driven tool for mixture property prediction, accelerating the rational formulation of functional fluids in chemical engineering.
