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

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

Predictability of localized plasmonic responses in nanoparticle assemblies

Design of nanoscale structures with desired nanophotonic properties are key tasks for nanooptics and nanophotonics. Here, the correlative relationship between local nanoparticle geometries and their plasmonic responses is established using encoder-decoder neural networks. In the im2spec network, the correlative relationship between local particle geometries and local spectra is established via encoding the observed geometries to a small number of latent variables and subsequently decoding into plasmonic spectra; in the spec2im network, the relationship is reversed. Surprisingly, these reduced descriptions allow high-veracity predictions of the local responses based on geometries for fixed compositions and chemical states of the surface. The analysis of the latent space distributions and the corresponding decoded and closest (in latent space) encoded images yields insight into the generative mechanisms of plasmonic interactions in the nanoparticle arrays. Ultimately, this approach creates a path toward determining configurations that can yield the spectrum closest to the desired one, paving the way for stochastic design of nanoplasmonic structures

Sculpting the plasmonic responses of nanoparticles by directed electron beam irradiation

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into the associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry and materials, and even then, limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, we use the atomic-sized probe of the scanning transmission electron microscope (STEM) to perform precise sculpting and design of nanoparticle configurations. Furthermore, using low-loss (EELS), we provide dynamic analyses of evolution of the plasmonic response during the sculpting process. We show that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or arbitrarily patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy domains. This process significantly increases the scope for design possibilities and presents opportunities for arbitrary structure development, which are ultimately key for nanophotonic design. Nanosculpting introduces yet another capability to the electron microscope.This effort is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.) S.H.C and D.J.M. acknowledge (NSF CHE-1905263, and CDCM, an NSF MRSEC DMR-1720595), the Welch Foundation (F-1848), and the Fulbright Program (IIE-15151071). Electron microscopy was performed using instrumentation within ORNL’s Materials Characterization Core provided by UT-Battelle, LLC, under Contract No. DE-AC05- 00OR22725 with the DOE and sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy.Center for Dynamics and Control of Material

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy

We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). Our method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which we use to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. We present empirical analyses that demonstrate the efficacy and generality of our approach

Electron-beam induced emergence of mesoscopic ordering in layered MnPS3_{3}

Ordered mesoscale structures in 2D materials induced by small misorientations have opened pathways for a wide variety of novel electronic, ferroelectric, and quantum phenomena. Until now, the only mechanism to induce this periodic ordering was via mechanical rotations between the layers, with the periodicity of the resulting moir\'e pattern being directly related to twist angle. Here we report a fundamentally new mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorous trichalcogenide, MnPS$_{3}$, induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulphur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and opens a pathway towards device applications by enabling controlled periodic potentials on the atomic scale.Comment: Electron microscopy data and analysis codes are freely available here: https://github.com/kevinroccapriore/MnPS

Mechanism for etching of exfoliated graphene on substrates by low-energy electron irradiation from helium plasma electron sources

Article investigating the mechanism for etching of exfoliated graphene multilayers on SiO₂ by low-energy (50 eV) electron irradiation using He plasma systems for electron sources