Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy

Abstract

Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement a deep kernel learning workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk- and edge plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material. This approach is universal, can be directly used as-is with any specimen, and is expected to be applicable to any probe-based microscopic techniques including other STEM modalities, Scanning Probe Microscopies, chemical, and optical imaging.

References in corpus (7)

• Topological Surface States Protected From Backscattering by Chiral Spin Texture
• Scalable Bayesian Optimization Using Deep Neural Networks
• Artificial Intelligence and Statistics
• Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
• Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy
• Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
• Sculpting the plasmonic responses of nanoparticles by directed electron beam irradiation

Cited by in corpus (11)

• Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
• Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
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• Post-Experiment Forensics and Human-in-the-Loop Interventions in Explainable Autonomous Scanning Probe Microscopy
• Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
• Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
• Autonomous convergence of STM control parameters using Bayesian Optimization
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
• Electron-beam induced emergence of mesoscopic ordering in layered MnPS${}_3$
• Autonomous Electron Tomography Reconstruction with Machine Learning