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The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational…
The record-breaking thermoelectric performance of tin selenide (SnSe) has motivated the investigation of analogue compounds with the same structure. A promising candidate that emerged recently is germanium selenide (GeSe). Here, using…
In contemporary self-supervised contrastive algorithms like SimCLR, MoCo, etc., the task of balancing attraction between two semantically similar samples and repulsion between two samples of different classes is primarily affected by the…
Doppler and Sisyphus cooling of $^{174}$YbOH are achieved and studied. This polyatomic molecule has high sensitivity to physics beyond the Standard Model and represents a new class of species for future high-precision probes of new…
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron microscope point-scan systems with minimal information loss. Building on a history of successful deep learning applications in compressed…
Using the \emph{ab initio} evolutionary algorithm (implemented in USPEX) and electronic structure calculations we investigate the properties of a new thermoelectric material FeSbAs, which is a material analog of the enigmatic thermoelectric…
Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and…
As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for…
Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming, and there exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. To…
Purpose: Accurate electronic stopping power data is crucial for calculating radiation-induced effects in various applications, from dosimetry and radiotherapy to particle physics. In this study, Stacking Ensemble Machine Learning (EML)…
Predicting nanoscale thermal transport in dielectrics requires models, such as the Boltzmann transport equation (BTE), that account for phonon boundary scattering in structures with complex geometries. Although the BTE has been validated…
The accuracy of the information that can be extracted from electron diffraction patterns is often limited by the presence of optical distortions. Existing distortion characterization techniques typically require knowledge of the reciprocal…
The Dark MAtter Particle Explorer (DAMPE) instrument is a space-borne cosmic-ray detector, capable of measuring ion fluxes up to $\sim$500 TeV/n. This energy scale is made accessible through its calorimeter, which is the deepest currently…
Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with…
Phase change materials based on Ge-Sb-Te alloys are widely explored for their potential in both memory devices and thermoelectric applications. In this study, films of Ge2Sb2Te5 (GST) doped with varying concentrations of Pb were prepared…
The semiconductor industry faces a computational crisis in extreme ultraviolet (EUV) lithography optimization, where traditional methods consume billions of CPU hours while failing to achieve sub-nanometer precision. We present a…
Recent measurements on extremely-underdoped YBa2Cu3O6+y [Phys. Rev. Lett. 99, 237003 (2007)] have allowed the critical temperature (T_c), superfluid density [rho_0 (T << T_c)] and dc conductivity [sigma_dc (T ~ T_c)] to be determined for a…
The MgB2 superconductor was doped with electroluminescent Y2O3:Eu, to synthesise a superconducting metamaterial. The temperature dependence of the resistivity of the superconductor indicates that the critical temperature (TC) of samples…
In this work, the development and implementation of the effective stochastic potential (ESP) method is presented to perform efficient conformational sampling of molecules. The overarching goal of this work is to alleviate the computational…
Cryo-electron tomography (Cryo-ET) is a powerful tool in structural biology for 3D visualization of cells and biological systems at resolutions sufficient to identify individual proteins in situ. The measurements are collected by tilting…