智能材料建模：大语言模型与偏最小二乘回归在预测聚砜膜机械性能中的比较
Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance
AI总结：
本文比较了大语言模型与偏最小二乘回归在预测聚砜膜机械性能中的表现，发现大语言模型在非线性、约束敏感属性上表现更优，而偏最小二乘回归在需要可解释性分解的线性关系中仍具竞争力。
摘要：
从结构描述符预测聚砜（PSF）膜的机械性能仍极具挑战性，因为实验研究通常数据稀缺。为此，本研究将知识驱动推理通过四个大语言模型（LLMs）（DeepSeek-V3、DeepSeek-R1、ChatGPT-4o 和 GPT-5）与偏最小二乘（PLS）回归进行比较，以预测杨氏模量（E）、拉伸强度（TS）和断裂伸长率（EL）基于孔径（PD）、接触角（CA）、厚度（T）和孔隙率（P）的测量。这些知识驱动方法在特定属性上优于化学计量学基线。对于EL，LLMs实现了统计显著的改进，DeepSeek-R1和GPT-5分别实现了40.5%和40.3%的均方误差减少，将平均绝对误差从11.63±5.34%降至5.18±0.17%。LLMs的运行到运行变异性显著压缩（≤3%）与PLS（最高47%）相比。E和TS的预测在统计上与方法相当（q≥0.05），表明线性方法对于具有强结构-性能相关性的属性表现足够。误差拓扑分析揭示了受数据-制度效应主导的系统回归到平均行为，而非模型家族限制。这些发现表明，LLMs在bootstrap不稳定性下对于非线性、约束敏感属性表现更优，而PLS在需要可解释性分解的线性关系中仍具竞争力。所展示的互补性表明，混合架构在可解释框架中利用LLM编码的知识可能优化小数据材料发现。
英文摘要：
Predicting the mechanical properties of polysulfone (PSF) membranes from structural descriptors remains challenging due to extreme data scarcity typical of experimental studies. To investigate this issue, this study benchmarked knowledge-driven inference using four large language models (LLMs) (DeepSeek-V3, DeepSeek-R1, ChatGPT-4o, and GPT-5) against partial least squares (PLS) regression for predicting Young's modulus (E), tensile strength (TS), and elongation at break (EL) based on pore diameter (PD), contact angle (CA), thickness (T), and porosity (P) measurements. These knowledge-driven approaches demonstrated property-specific advantages over the chemometric baseline. For EL, LLMs achieved statistically significant improvements, with DeepSeek-R1 and GPT-5 delivering 40.5% and 40.3% of Root Mean Square Error reductions, respectively, reducing mean absolute errors from $11.63\pm5.34$% to $5.18\pm0.17$%. Run-to-run variability was markedly compressed for LLMs ($\leq$3%) compared to PLS (up to 47%). E and TS predictions showed statistical parity between approaches ($q\geq0.05$), indicating sufficient performance of linear methods for properties with strong structure-property correlations. Error topology analysis revealed systematic regression-to-the-mean behavior dominated by data-regime effects rather than model-family limitations. These findings establish that LLMs excel for non-linear, constraint-sensitive properties under bootstrap instability, while PLS remains competitive for linear relationships requiring interpretable latent-variable decompositions. The demonstrated complementarity suggests hybrid architectures leveraging LLM-encoded knowledge within interpretable frameworks may optimise small-data materials discovery.
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