ترغب بنشر مسار تعليمي؟ اضغط هنا

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

103   0   0.0 ( 0 )
 نشر من قبل Maxim Ziatdinov
 تاريخ النشر 2021
والبحث باللغة English




اسأل ChatGPT حول البحث

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging. Here, we aim to analyze the major pathways towards AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment, and consider the latencies, biases, and knowledge priors of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning.



قيم البحث

اقرأ أيضاً

Magnetic microscopy that combines nanoscale spatial resolution with picosecond scale temporal resolution uniquely enables direct observation of the spatiotemporal magnetic phenomena that are relevant to future high-speed, high-density magnetic storag e and logic technologies. Magnetic microscopes that combine these metrics has been limited to facility-level instruments. To address this gap in lab-accessible spatiotemporal imaging, we develop a time-resolved near-field magnetic microscope based on magneto-thermal interactions. We demonstrate both magnetization and current density imaging modalities, each with spatial resolution that far surpasses the optical diffraction limit. In addition, we study the near-field and time-resolved characteristics of our signal and find that our instrument possesses a spatial resolution on the scale of 100 nm and a temporal resolution below 100 ps. Our results demonstrate an accessible and comparatively low-cost approach to nanoscale spatiotemporal magnetic microscopy in a table-top form to aid the science and technology of dynamic magnetic devices with complex spin textures.
Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy. A Bayesian Optimization framework for imaging is developed and its performance for a variety of acquisition and pathfindin g functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with metrics showing gains of ~3x in the sampling efficiency. This approach opens the pathway to perform more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability, tip wear, and/or stochastic sample damage that occurs at specific, a priori unknown spatial positions. Potential improvements to the framework to enable the incorporation of more prior information and improve efficiency further are discussed.
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 deep kernel learning workflow combining the correlative prediction of the t arget functional response and its uncertainty from the structure, and physics-based selection of acquisition function guiding 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 towards physical discovery. Here, this approach is illustrated for nanoplasmonic studies of the nanoparticles and experimentally implemented for bulk- and edge plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material. This approach is universal and is expected to be applicable to probe-based microscopic techniques including other STEM modalities and Scanning Probe Microscopies.
Electrical double layers play a key role in a variety of electrochemical systems. The mean free path of secondary electrons in aqueous solutions is on the order of a nanometer, making them suitable for probing of ultrathin electrical double layers at solid-liquid electrolyte interfaces. Employing graphene as an electron-transparent electrode in a two-electrode electrochemical system, we show that the secondary electron yield of the graphene-liquid interface depends on the ionic strength and concentration of electrolyte and applied bias at the remote counter electrode. These observations have been related to polarization-induced changes in the potential distribution within the electrical double layer and demonstrate the feasibility of using scanning electron microscopy to examine and map electrified liquid-solid interfaces
The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of relia ble data interpretation, i.e. conversion from detected signal to descriptors specific to tip-surface interactions and subsequently to materials properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation (BE) SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We note that in application of Bayesian methods, special care should be made for proper setting on the problem as model selection vs. establishing practical parameter equivalence. We further explore the non-linear mechanical behaviors at topological defects in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of Duffing resonance frequency and the nonlinearity of the sample surface, suggesting the presence of the hidden elements of domain structure. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls can be significantly broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا