Related papers: Deep peak property learning for efficient chiral m…
We report on a joint experimental and theoretical study of photoelectron circular dichroism (PECD) in methyloxirane. By detecting O 1s-photoelectrons in coincidence with fragment ions, we deduce the molecule's orientation and photoelectron…
Chiral crystals show promise for spintronic technologies on account of their high spin selectivity, which has led to significant recent interest in quantitative characterization and first-principles prediction of their spin-optoelectronics…
Recently, there is a great interest in studying ultrasensitive detection and characterization of biomolecules using plasmonic particles, because they are of considerable importance in biomedical science and pharmaceutics. So far, all the…
Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory.…
In the close vicinity of a chiral nanostructure, the circular dichroism of a biomolecule could be greatly enhanced, due to the interaction with the local superchiral fields. Modest enhancement of optical activity using a planar…
Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface…
Circular Dichroism (CD) can distinguish the handedness of chiral molecules. However, it is typically very weak due to vanishing absorption at low molecular concentrations. Here, we suggest Thermal Circular Dichroism (TCD) for chiral…
Embedding molecular symmetries into machine-learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a…
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have employed strings, fingerprints, global…
Increasing the sensitivity of chiral spectroscopic techniques such as circular dichroism (CD) spectroscopy is a current aspiration in the research field of nanophotonics. Enhancing CD spectroscopy depends upon of two complementary…
Spectroscopic properties of molecules holds great importance for the description of the molecular response under the effect of an UV/Vis electromagnetic radiation. Computationally expensive ab initio (e.g. MultiConfigurational SCF, Coupled…
Electron magnetic chiral dichroism (EMCD) is an emerging tool for quantitative measurements of magnetic properties using the transmission electron microscope (TEM), with the possibility of nanometer resolution. The geometrical conditions,…
Chirality is a fundamental molecular property that governs stereospecific behavior in chemistry and biology. Capturing chirality in machine learning models remains challenging due to the geometric complexity of stereochemical relationships…
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. Here, we propose using the Euler Characteristic Transform…
Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule. Although mass spectrometry is applied in many areas, the vast majority of small molecules lack experimental reference spectra. For…
Circular dichroism (CD) spectroscopy is a useful technique for characterizing chiral molecules. It is more sensitive than total absorption to molecule conformation, and it is routinely used to identify enantiomers. We present here a first…
Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging…
Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational…
Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in…
Descriptor generation methods using latent representations of encoder$-$decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the…