Liu Yu, Pratiush Utkarsh, Bemis Jason, Proksch Roger, Emery Reece, Rack Philip D, Liu Yu-Chen, Yang Jan-Chi, Udovenko Stanislav, Trolier-McKinstry Susan, Kalinin Sergei V
Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.
Oxford Instruments Asylum Research, Santa Barbara, California 93117, USA.
Rev Sci Instrum. 2024 Sep 1;95(9). doi: 10.1063/5.0219990.
The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.
计算能力和机器学习算法的快速发展为利用扫描探针显微镜（SPM）实现科学发现自动化铺平了道路。实现自动化SPM的关键要素包括能够从Python代码进行SPM控制的接口、高计算能力的可用性以及科学发现工作流程的开发。在这里，我们构建了一个Python接口库，它能够从本地计算机或远程高性能计算机控制SPM，满足自主工作流程中机器学习算法对高计算能力的需求。我们还引入了一个通用平台，将科学发现中SPM的操作抽象为固定策略或奖励驱动的工作流程。我们的工作提供了一个完整的基础设施，用于构建自动化SPM工作流程，以实现常规操作和利用机器学习进行自主科学发现。
