Autonomous convergence of STM control parameters using Bayesian optimization
Scanning tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and their surface energetics. However, the optimization of the imaging conditions is a tedious process due to the extremely sensitive tip–surface interaction, thus limiting the throughput efficiency. In this paper, we deploy a machine learning (ML)-based framework to achieve optimal atomically resolved imaging conditions in real time. The experimental workflow leverages the Bayesian optimization (BO) method to rapidly improve the image quality, defined by the peak intensity in the Fourier space. The outcome of the BO prediction is incorporated into the microscope controls, i.e., the current setpoint and the tip bias, to dynamically improve the STM scan conditions. We present strategies to either selectively explore or exploit across the parameter space. As a result, suitable policies are developed for autonomous convergence of the control parameters. The ML-based framework serves as a general workflow methodology across a wide range of materials.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 2564795
- Journal Information:
- APL Machine Learning, Journal Name: APL Machine Learning Journal Issue: 1 Vol. 2; ISSN 2770-9019
- Publisher:
- American Institute of PhysicsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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