Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

专为自主实验而设计的材料合成平台能够收集多模式诊断数据，这些数据可用于反馈以优化材料性能。脉冲激光沉积 (PLD) 正在成为一种可行的自主合成工具，因此需要开发能够从原位诊断中提取信息的机器学习 (ML) 技术。在这里，我们证明 PLD 过程中产生的等离子体羽流的增强 CCD 图像序列可用于异常检测和薄膜生长动力学预测。我们开发了多输出 (2 + 1)D 卷积神经网络回归模型，从羽流动力学中提取深层特征，这些特征不仅与测量的室压力和入射激光能量相关，更重要的是，预测自催化薄膜生长的参数模型源自原位激光反射率实验。我们的结果证明了如何利用 ML 和 PLD 中的原位羽流诊断数据来将沉积条件维持在最佳状态。此外，羽流动力学对沉积前薄膜生长动力学或其他薄膜特性的预测能力为非专家提供了一种快速预筛选生长条件的方法，这有望加速 PLD 的材料优化。