标题： 探索自驾车实验室中kNN噪声特征检测与恢复的前沿 显示英文标题

标题： Exploring the Frontiers of kNN Noisy Feature Detection and Recovery for Self-Driving Labs

摘要： 自动驾驶实验室（SDLs）通过将机器学习与自动化实验平台相结合，显示出加速材料发现的潜力。 然而，输入参数捕获中的错误可能会破坏用于建模系统性能的特征，从而影响当前和未来的实验活动。 本研究开发了一个自动化工作流程，以系统地检测噪声特征，确定可以纠正的样本-特征配对，并最终恢复正确的特征值。 随后进行了一项系统研究，以考察数据集大小、噪声强度和特征值分布如何影响噪声特征的可检测性和可恢复性。 一般来说，高强度噪声和较大的训练数据集有利于噪声特征的检测和校正。 低强度噪声会降低检测和恢复效果，但可以通过更大的清洁训练数据集进行补偿。 具有连续和分散特征分布的特征与具有离散或狭窄分布的特征相比，显示出更高的可恢复性。 这项系统研究不仅展示了一个在存在噪声、数据有限和不同特征分布的情况下进行合理数据恢复的模型无关框架，还为材料数据集中的kNN插补提供了一个具体的基准。 最终，它旨在提高自动化材料发现中的数据质量和实验精度。 显示英文摘要

摘要： Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the capture of input parameters may corrupt the features used to model system performance, compromising current and future campaigns. This study develops an automated workflow to systematically detect noisy features, determine sample-feature pairings that can be corrected, and finally recover the correct feature values. A systematic study is then performed to examine how dataset size, noise intensity, and feature value distribution affect both the detectability and recoverability of noisy features. In general, high-intensity noise and large training datasets are conducive to the detection and correction of noisy features. Low-intensity noise reduces detection and recovery but can be compensated for by larger clean training data sets. Detection and correction results vary between features with continuous and dispersed feature distributions showing greater recoverability compared to features with discrete or narrow distributions. This systematic study not only demonstrates a model agnostic framework for rational data recovery in the presence of noise, limited data, and differing feature distributions but also provides a tangible benchmark of kNN imputation in materials data sets. Ultimately, it aims to enhance data quality and experimental precision in automated materials discovery.