利用机器学习特征化预测和加速纳米材料合成
Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
AI总结：
本文提出用机器学习自动提取RHEED特征，在少量专家标注数据上建立材料无关的预测模型，实现晶粒取向和掺杂浓度的定量预测，可在100个样品的合成中节省80%时间。
摘要：
材料合成优化受限于依赖手动工具和直觉、跨越多个孤立表征模式的串行反馈过程。我们利用机器学习自动化并泛化了反射高能电子衍射（RHEED）数据的特征提取，在少量（约10个）专家标注数据上建立定量预测关系，从而为后续生长的样品节省大量时间。这些预测关系在一个代表性材料体系（c面蓝宝石(0001)上的W_{1-x}V_xSe2）中进行了评估，目标有两个：1）利用生长前衬底数据预测沉积薄膜的晶粒取向；2）利用原位RHEED作为非原位方法（如X射线光电子能谱）的替代手段来估计钒掺杂浓度。两项任务均使用相同的材料无关特征完成，避免了针对特定体系的重新训练，并有望在100个样品的合成活动中节省80%的时间。这些预测为避开注定失败的试验、减少后续表征以及提高材料合成的控制分辨率提供了指导。
英文摘要：
Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
