Machine Learning for Optical Scanning Probe Nanoscopy

The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. The scattering-type scanning near-field optical microscopy (s-SNOM) technique has recently spread to many research fields and enabled notable discoveries. In this brief perspective, we show that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial intelligence (AI) and machine learning (ML) algorithms. We show that, with the help of AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent

Electronic interactions in Dirac fluids visualized by nano-terahertz spacetime mapping

Ultraclean graphene at charge neutrality hosts a quantum critical Dirac fluid of interacting electrons and holes. Interactions profoundly affect the charge dynamics of graphene, which is encoded in the properties of its collective modes: surface plasmon polaritons (SPPs). The group velocity and lifetime of SPPs have a direct correspondence with the reactive and dissipative parts of the tera-Hertz (THz) conductivity of the Dirac fluid. We succeeded in tracking the propagation of SPPs over sub-micron distances at femto-second (fs) time scales. Our experiments uncovered prominent departures from the predictions of the conventional Fermi-liquid theory. The deviations are particularly strong when the densities of electrons and holes are approximately equal. Our imaging methodology can be used to probe the electromagnetics of quantum materials other than graphene in order to provide fs-scale diagnostics under near-equilibrium conditions

Temporal and spatial changes in hydrological wet extremes of the largest river basin on the Tibetan Plateau

Global warming accelerates the rate of inter-regional hydrological cycles, leading to a significant increase in the frequency and intensity of hydrological wet extremes. The Tibetan Plateau (TP) has been experiencing a rapid warming and wetting trend for decades. This trend is especially strong for the upper Brahmaputra basin (UBB) in the southern TP. The UBB is the largest river on the TP, and these changes are likely to impact the water security of local and downstream inhabitants. This study explores the spatial-temporal variability of wet extremes in the UBB from 1981–2019 using a water- and energy-budget distributed hydrological model (WEB-DHM) to simulate river discharge. The simulated results were validated against observed discharge from the Ministry of Water Resources at a mid-stream location and our observations downstream. The major findings are as follows: (1) the WEB-DHM model adequately describes land-atmosphere interactions (slight underestimation of −0.26 K in simulated annual mean land surface temperature) and can accurately reproduce daily and monthly discharge (Nash-Sutcliffe efficiency is 0.662 and 0.796 respectively for Nuxia station); (2) although extreme discharge generally occurs in July and is concentrated in the southeastern TP, extreme wet events in the UBB are becoming increasingly frequent (after 1998, the number of extreme days per year increased by 13% compared to before) and intense (maximum daily discharge increased with a significant trend of 444 (m ^3 s ^−1 ) yr ^−1 ), and are occurring across a wider region; (3) Precipitation is more likely to affect the intensity and spatial distribution of wet extremes, while the air temperature is more correlated with the frequency. Our wet extreme analysis in the UBB provides valuable insight into strategies to manage regional water resources and prevent hydrological disasters

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general

Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general

Infrared nano-imaging of Dirac magnetoexcitons in graphene

Magnetic fields can have profound effects on the motion of electrons in quantum materials. Two-dimensional electron systems subject to strong magnetic fields are expected to exhibit quantized Hall conductivity, chiral edge currents and distinctive collective modes referred to as magnetoplasmons and magnetoexcitons. Generating these propagating collective modes in charge-neutral samples and imaging them at their native nanometre length scales have thus far been experimentally elusive. Here we visualize propagating magnetoexciton polaritons at their native length scales and report their magnetic-field-tunable dispersion in near-charge-neutral graphene. Imaging these collective modes and their associated nano-electro-optical responses allows us to identify polariton-modulated optical and photo-thermal electric effects at the sample edges, which are the most pronounced near charge neutrality. Our work is enabled by innovations in cryogenic near-field optical microscopy techniques that allow for the nano-imaging of the near-field responses of two-dimensional materials under magnetic fields up to 7 T. This nano-magneto-optics approach allows us to explore and manipulate magnetopolaritons in specimens with low carrier doping via harnessing high magnetic fields