Related papers: Machine Learning Accelerates Raman Computations fr…
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory…
Hydrocarbons are of great importance in carbon-bearing fluids in deep Earth and in ice giant planets at extreme pressure (P)-temperature (T) conditions. Raman spectroscopy is a powerful tool to study the chemical speciation of hydrocarbons;…
Raman microspectroscopy was used to characterize amorphous silica plastic behavior. Using a correlation between Raman spectrum and density, a map of the local residual indentation-induced densification is obtained. The existence of a…
Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively…
High-gain resonant nonlinear Raman scattering on trapped cold atoms within a high-fineness ring optical cavity is simply explained under a nonlinear opto-mechanical mechanism, and a proposal using it to detect frequency of micro-trap on…
The rapid and accurate detection of biochemical compositions in fish is a crucial real-world task that facilitates optimal utilization and extraction of high-value products in the seafood industry. Raman spectroscopy provides a promising…
Determining light shift in Raman-Ramsey interference is important for the development of atomic frequency standards based on a vapor cell. We have accurately calculated light shift in Raman-Ramsey interference using the density-matrix…
While many vibrational Raman spectroscopy studies of liquid water have investigated the temperature dependence of the high-frequency O-H stretching region, few have analyzed the changes in the Raman spectrum as a function of temperature…
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the…
The Raman effect -- inelastic scattering of light by lattice vibrations (phonons) -- produces an optical response closely tied to a material's crystal structure. Here we show that resonant optical excitation of IR and Raman phonons gives…
Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological…
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…
Recent applications of machine learning and statistical inference provide case studies demonstrating how such approaches can accelerate the discovery process in physical chemistry and related fields. Examples discussed in this review…
Raman spectroscopy is a label-free, chemically specific optical technique which provides detailed information about the chemical composition and structure of the excited analyte. Because of this, there is growing research interest in…
Magnetic Resonance Imaging (MRI) has become one of the most important tools to screen humans in medicine, virtually every modern hospital is equipped with an NMR tomograph. The potential of NMR in 3D imaging tasks is by far greater, but…
The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine…
: Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only…
Predictive modeling of the phonon/thermal transport properties of materials is vital to rational design for a diverse spectrum of engineering applications. Classical Molecular Dynamics (MD) simulations serve as a tool to simulate the time…
Raman scattering is a chemically selective probing mechanism with diverse applications in industry and clinical settings. Yet, most samples are optically opaque limiting the applicability of Raman probing at depth. Here, we demonstrate…
Single-beam spectrally controlled (SBSC) two-dimensional (2D) Raman spectroscopy is a unique 2D vibrational measurement technique utilizing trains of short pulses that are generated from a single broadband pulse by pulse shaping. This…