用机器学习自动分析晶体生长数据，加速新材料合成。
Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
- 从反射电子衍射图中提取通用特征，实现小样本预测。
- 准确预测薄膜晶粒取向和掺杂浓度，节省80%合成时间。
- 适合材料合成与表征效率提升的研究者使用。
材料合成优化受限于依赖人工工具和直觉的串行反馈流程，且多种表征手段彼此孤立。本文利用机器学习自动化并泛化反射高能电子衍射（RHEED）数据的特征提取，在约10组专家标注的小样本数据上建立定量预测关系，显著减少后续样品生长的时间成本。该方法在典型材料体系 \\ce{W_{1-x}V_xSe2} / c-面蓝宝石 (0001) 上验证了两个目标：1）基于生长前基底数据预测薄膜晶粒对齐；2）利用原位RHEED作为外延方法（如X射线光电子能谱）的代理，估算钒掺杂浓度。两项任务均采用相同的材料无关特征，避免特定系统重新训练，可在100样本合成实验中实现潜在80%的时间节省。这些预测有助于规避无效实验、减少后续表征，并提升材料合成的控制精度。
原文摘要 · Abstract (English)
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.
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