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We describe a new experimental setup for the detection of magnetic circular dichroism with fast electrons (EMCD). As compared to earlier findings the signal is an order of magnitude higher, while the probed area could be significantly…
This paper study provides a novel contribution to the field of signal processing and DL for ECG signal analysis by introducing a new feature representation method for ECG signals. The proposed method is based on transforming time frequency…
Molecular Circular dichroism (CD) spectroscopy faces significant limitations due to the inherent weakness of chiroptical light-matter interactions. In this view, resonant optical antennas constitute a promising solution to this problem…
Photoelectron circular dichroism (PECD) is a method where randomly oriented chiral molecules are photoionized due to irradiation by circularly-polarized lasers, yielding large chiral signals in the photoelectron momentum distribution.…
The circular dichroism (ICD) induced from interaction of chiral molecules with achiral plasmonic metal nanostructures has improved sensitivity of molecular chirality detection. Although experimental results have presented several orders…
A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic…
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction.…
Chiral molecules are widespread in nature, playing a fundamental role in bio-chemical processes and in the origin of life itself. The observation of dynamics in chiral molecules is crucial for the understanding and control of the chiral…
Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density…
The chiral state of a molecule plays a crucial role in molecular recognition and biochemical reactions. Because of this and owing to the fact that most modern drugs are chiral, the sensitive and reliable detection of the chirality of…
High-fidelity electron microscopy simulations required for quantitative crystal structure refinements face a fundamental challenge: while physical interactions are well-described theoretically, real-world experimental effects are…
Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, and elasticity tensors, are computationally expensive to…
Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR),…
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural…
The ability to differentiate chiral molecules of different handedness is of great importance for chemical and life sciences. Since most of the relevant chiral molecules have their chiral transitions in the UV region, detecting their…
Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is…
Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying "hit" molecules from a large…
Circular dichroism spectroscopy is an essential technique for understanding molecular structure and magnetic materials, but spatial resolution is limited by the wavelength of light, and sensitivity sufficient for single-molecule…
Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free…
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of crystal structures has been widely used for structure…