大模型比传统方法更擅长预测膜材料的断裂伸长率。
Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance
- 用大模型和偏最小二乘法对比预测膜性能，基于孔径等结构参数。
- 大模型将伸长率预测误差降低40%以上，且结果更稳定。
- 适合小数据、非线性关系的材料性能预测场景。
由于实验研究常面临极端数据稀缺问题，从结构描述符预测聚砜（PSF）膜的力学性能仍具挑战。本研究对比了四种大语言模型（DeepSeek-V3、DeepSeek-R1、ChatGPT-4o、GPT-5）与偏最小二乘（PLS）回归在预测杨氏模量（E）、抗拉强度（TS）和断裂伸长率（EL）上的表现，输入参数包括孔径（PD）、接触角（CA）、厚度（T）和孔隙率（P）。结果显示，对于EL，LLMs显著优于基线，其中DeepSeek-R1和GPT-5分别实现40.5%和40.3%的均方根误差降幅，平均绝对误差由$11.63\±5.34$%降至$5.18\u00b10.17$%。LLMs的运行间变异性压缩至≤3%，而PLS高达47%。对E和TS的预测，两类方法无显著差异（q≥0.05），表明线性方法在强结构-性能关联下依然有效。误差拓扑分析显示，系统性回归均值偏差主要受数据区间效应影响，而非模型家族局限。研究证实：在数据不稳定、非线性约束敏感场景下，大模型优势明显；而需可解释潜变量分解时，PLS仍具竞争力。二者互补提示：将大模型知识嵌入可解释框架的混合架构，或可优化小数据材料发现。
原文摘要 · Abstract (English)
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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