Machine Learning for Optical Scanning Probe Nanoscopy

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Accelerated Nano-Optical Imaging through Sparse Sampling

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

Ultrafast anisotropic dynamics of hyperbolic nanolight pulse propagation

Polariton pulses-transient light-matter hybrid excitations-traveling through anisotropic media can lead to unusual optical phenomena in space and time. However, studying these pulses presents challenges with their anisotropic, ultrafast, and nanoscale field variations. Here, we demonstrate the creation, observation, and control of polariton pulses, with in-plane hyperbolic dispersion, on anisotropic crystal surfaces by using a time-resolved nanoimaging technique and our developed high-dimensional data processing. We capture and analyze movies of distinctive pulse spatiotemporal dynamics, including curved ultraslow energy flow trajectories, anisotropic dissipation, and dynamical misalignment between phase and group velocities. Our approach enables analysis of polariton pulses in the wave vector time domain, demonstrating a time-domain polaritonic topological transition from lenticular to hyperbolic dispersion contours and the ability to study the polariton-induced time-varying optical forces. Our findings promise to facilitate the study of diverse space-time phenomena at extreme scales and drive advances in ultrafast nanoimaging.

Machine learning the microscopic form of nematic order in twisted double-bilayer graphene

Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.

Interrogating Quantum Nonlocal Effects in Nanoplasmonics through Electron-Beam Spectroscopy

A rigorous account of quantum nonlocal effects is paramount for understanding the optical response of metal nanostructures and for designing plasmonic devices at the nanoscale. Here, we present a scheme for retrieving the quantum surface response of metals, encapsulated in the Feibelman d-parameters, from electron energy-loss spectroscopy (EELS) and cathodoluminescence (CL) measurements. We theoretically demonstrate that quantum nonlocal effects have a dramatic impact on EELS and CL spectra, in the guise of spectral shifts and nonlocal damping, when either the system size or the inverse wave vector in extended structures approaches the nanometer scale. Our concept capitalizes on the unparalleled ability of free electrons to supply deeply subwavelength near-fields and, thus, probe the optical response of metals at length scales in which quantum-mechanical effects are apparent. These results pave the way for a widespread use of the d-parameter formalism, thereby facilitating a rigorous yet practical inclusion of nonclassical effects in nanoplasmonics.

Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning

The development of a data-driven science paradigm is greatly revolutionizing the process of materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the birefringent phase-matching ability to deep-ultraviolet (UV) region is of vital significance for the field of laser technologies. Herein, a target-driven materials design framework combining high-throughput calculations (HTC), crystal structure prediction, and interpretable machine learning (ML) is proposed to accelerate the discovery of deep-UV NLO materials. Using a dataset generated from HTC, an ML regression model for predicting birefringence is developed for the first time, which exhibits a possibility of achieving fast and accurate prediction. Essentially, crystal structures are adopted as the only known input of this model to establish a close structure-property relationship mapping birefringence. Utilizing the ML-predicted birefringence which can affect the shortest phase-matching wavelength, a full list of potential chemical compositions based on an efficient screening strategy is identified. Further, eight structures with good stability are discovered to show potential applications in the deep-UV region, owing to their promising NLO-related properties. This study provides a new insight into the discovery of NLO materials and this design framework can identify desired materials with high performances in the broad chemical space at a low computational cost.