相关论文: Learning and Controlling Silicon Dopant Transition…
We explore theoretically the electroluminescence of single molecules. We adopt a local-electrode framework that is appropriate for scanning tunneling microscopy (STM) experiments where electroluminescence originates from individual…
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into…
Neural networks possess incredible capabilities for extracting abstract features from data. Electromagnetic computing harnesses wave propagation to execute computational operations. Metasurfaces, composed of subwavelength meta-atoms, are…
The Scanning Tunneling Microscope (STM) is a powerful instrument to study electronic density of states at surfaces down to atomic scale. Many interesting samples require studying variations as a function of the magnetic field, which is most…
The light absorption of [001] grown single-crystalline silicon wafers can be enhanced by chemical etching with potassium hydroxide resulting in a pyramid-like surface texture. Alongside this advantageous property in the context of solar…
Thermal transport in nanostructures plays a critical role in modern technologies. As devices shrink, techniques that can measure thermal properties at nanometer and nanosecond scales are increasingly needed to capture transient,…
A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire…
We report theoretical results predicting the atomic manipulation of a silver atom on a Si(001) surface by a scanning probe tip, and providing insight into the manipulation phenomena. A molecular mechanics technique has been used, the system…
The scanning transmission electron microscope, a workhorse instrument in materials characterization, is being transformed into an atomic-scale material manipulation platform. With an eye on the trajectory of recent developments and the…
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior…
In this paper we provide a novel approach to the analysis of kinetic models for label switching, which are used for particle systems that can randomly switch between gradient flows in different energy landscapes. Besides problems in biology…
We present annular dark field scanning transmission electron microscopy (ADF-STEM) as an efficient, fast, and non-destructive nanoscale tool for monitoring solid-state phase transition. Using metal-insulator transition in vanadium dioxide…
Transmission electron microscopy (TEM) and scanning TEM (STEM) are indispensable tools for materials characterization. However, during a typical (S)TEM experiment, the sample is subject to a number of effects that can change its atomic…
We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We…
Momentum-resolved scanning transmission electron microscopy (MRSTEM) is a powerful phase-contrast technique that can map lateral magnetic and electric fields ranging from the micrometer to the subatomic scale. Resolving fields ranging from…
Four-dimensional scanning transmission electron microscopy (4D-STEM) is a powerful tool that allows for the simultaneous acquisition of spatial and diffraction information, driven by recent advancements in direct electron detector…
sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across…
The scaling of active photonic devices to deep-submicron length-scales has been hampered by the fundamental diffraction limit and the absence of materials with sufficiently strong electro-optic effects. Here, we demonstrate a solid state…
The measurement of shifts in the energy of X-ray emission lines is important for understanding the electronic structure and physical properties of materials. In this study, we demonstrate a method using a synchrotron source to introduce…
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the…