Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration.

翻译：自主实验室通过自动化和机器学习加速实验流程，在新材料发现领域具有巨大潜力。然而在薄膜科学领域，自主实验室主要局限于溶液基合成方法——这类方法虽易于自动化，却难以覆盖无机材料的广阔化学空间。本研究提出了一种基于磁控共溅射的自主实验室系统。我们采用组合材料方法，在多元素成分梯度薄膜上获得精确的成分分布图。传统方法通常需要耗时且易产生系统误差的非原位分析。本文提出了一种快速、无需校准的原位机器学习驱动方法，可为任意靶材组合和溅射条件生成成分分布图。我们开发了一种预测多元素组合薄膜成分分布的方法，该方法利用溅射腔内石英晶体微天平传感器的原位测量数据。针对特定靶材，通过基于高斯过程的主动学习，将传感器读数建模为溅射气压和磁控功率的函数。最终的高斯过程模型与腔内沉积通量分布的几何模型相结合，能够插值计算出样品任意位置处各靶材的沉积速率。我们研究了机器学习流程中的多种采集函数，其中完全贝叶斯高斯过程方法——BALM（贝叶斯主动学习MacKay算法）——表现出最佳性能，仅需10次实验即可学习单靶材的沉积速率。通过实验验证了共溅射成分分布预测的准确性。本框架通过避免大量表征或校准步骤，显著提升了实验通量，从而证明了机器学习引导的自主实验室在加速材料探索方面的潜力。