Scanning probe microscopy (SPM) has revolutionized the fields of materials, nano-science, chemistry, and biology, by enabling mapping of surface properties and surface manipulation with atomic precision. However, these achievements require constant human supervision; fully automated SPM has not been accomplished yet. Here we demonstrate an artificial intelligence framework based on machine learning for autonomous SPM operation (DeepSPM). DeepSPM includes an algorithmic search of good sample regions, a convolutional neural network to assess the quality of acquired images, and a deep reinforcement learning agent to reliably condition the state of the probe. DeepSPM is able to acquire and classify data continuously in multi-day scanning tunneling microscopy experiments, managing the probe quality in response to varying experimental conditions. Our approach paves the way for advanced methods hardly feasible by human operation (e.g., large dataset acquisition and SPM-based nanolithography). DeepSPM can be generalized to most SPM techniques, with the source code publicly available.

扫描探针显微镜（SPM）通过实现表面特性的映射和原子精度的表面处理，彻底改变了材料，纳米科学，化学和生物学领域。但是，这些成就需要不断的人工监督。全自动SPM尚未完成。在这里，我们演示了一个基于机器学习的人工智能框架，用于自主SPM操作（DeepSPM）。DeepSPM包括对好的样本区域进行算法搜索，用于评估所采集图像质量的卷积神经网络，以及用于可靠地调节探头状态的深度强化学习剂。DeepSPM能够在多天的扫描隧道显微镜实验中连续获取和分类数据，响应变化的实验条件来管理探头质量。我们的方法为人为操作难以实现的高级方法铺平了道路（例如，大数据集获取和基于SPM的纳米光刻）。DeepSPM可以推广到大多数SPM技术，并且源代码可以公开获得。