AI 中文总结
研究针对机器学习模型及训练数据基于两个未经验证的假设的问题，引入结合多种分析和DFT判定的审计协议，以自旋霍尔电导率为例验证，揭示模型和数据问题，该协议成本低且适用特定情况。
AI 中文摘要
用于材料特性的机器学习模型基于两个标准验证从未检验过的假设：模型特征反映特性的物理性质而非训练分布的偶然情况，以及训练标签本身是正确的。我们引入了一种模型无关的审计协议，结合SHAP归因、反事实部分依赖分析和拉索蒙风格的跨模型验证，每项发现都由目标密度泛函理论（DFT）判定。在仅使用成分的随机森林对本征自旋霍尔电导率进行演示时，该模型无需松弛晶体结构，达到了与结构感知图网络竞争的精度，同时适用于尚未计算结构的更大成分空间。模型审计揭示平均p价描述符与铂含量在统计上纠缠在一起，这是学习表示的属性而非物理性质；DFT证实了结果，一种无铂化合物（HgOsPb₂）的真实自旋霍尔电导率几乎是预测值的四倍。数据审计暴露了HfC训练标签中三十倍的误差，在相同数据上训练的每个黑箱模型都无法检测到地继承了该误差。该协议以几次DFT计算的成本对模型及其训练数据进行审计，适用于一种元素主导高特性区域的情况。
英文摘要
Machine-learning models for materials properties rest on two assumptions that standard validation never tests: that a model's features reflect the physics of the property rather than accidents of the training distribution, and that the training labels are themselves correct. We introduce a model-agnostic audit protocol for both, combining SHAP attribution, counterfactual partial dependence analysis, and Rashomon-style cross-model verification, with every finding adjudicated by targeted density functional theory (DFT). Demonstrated on intrinsic spin Hall conductivity using a composition-only Random Forest, the model needs no relaxed crystal structure, reaching accuracy competitive with structure-aware graph networks while remaining applicable to the far larger space of compositions for which no structure has been computed. The model audit reveals that the average p-valence descriptor becomes statistically entangled with Pt content - a property of the learned representation rather than the physics; DFT confirms the consequence, a Pt-free compound (HgOsPb$_2$) whose true SHC is nearly four times the prediction. The data audit exposes a thirtyfold error in the HfC training label, inherited undetectably by every black-box model trained on the same data. The protocol audits a model and its training data for the cost of a few DFT calculations, wherever one element dominates the high-property regime.
