Related papers: Machine Learning Accelerates Raman Computations fr…
Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection.…
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological…
MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
We present a computational method to accurately calculate Raman spectra from first principles with an at least one order of magnitude higher efficiency. This scheme thus allows to routinely calculate finite-temperature Raman spectra…
Molecular dynamics (MD) is a powerful and popular tool for understanding the dynamical evolution of materials at the nano and mesoscopic scales. There are various flavors of MD ranging from the high fidelity albeit computationally expensive…
Theoretical frameworks used to qualitatively and quantitatively describe nuclear dynamics in solids are often based on the harmonic approximation. However, this approximation is known to become inaccurate or to break down completely in many…
We present and evaluate an efficient method for simulating Raman spectra from molecular dynamics (MD) calculations {\it without} defining normal modes. We apply the method to high pressure hydrogen in the high-temperature "Phase IV": a…
Due to their sub picosecond temporal resolution, coherent Raman spectroscopies have been proposed as a viable extension of Spontaneous Raman (SR) thermometry, to determine dynamics of mode specific vibrational energy content during out of…
In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular…
The conventional explanation of plasmon-enhanced Raman scattering attributes the enhancement to the antenna effect focusing the electromagnetic field into sub-wavelength volumes. Here we introduce a new model that additionally accounts for…
In what has been described as the fourth age of Quantum Chemistry, variational nuclear motion programs are now routinely being used to obtain the vibration-rotation levels and corresponding wavefunctions of small molecules to the sort of…
Raman spectroscopy has attracted interest as a non-invasive optical technique to study the composition and structure of a wide range of materials at the microscopic level. The intrinsic fluorescence background can be orders of magnitude…
Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based…
Several methods are available to compute the anharmonicity in semi-rigid molecules. However, such methods are not routinely employed yet because of their large computational cost, especially for large molecules. The potential energy surface…
Coherent Raman scattering provides highly sensitive vibrational analysis through nonlinear light-matter interactions. However, its application to metal interfaces has remained challenging because the intrinsically large non-resonant…
Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they…
Strain in a material induces shifts in vibrational frequencies, which is a probe of the nature of the vibrations and interatomic potentials, and can be used to map local stress/strain distributions via Raman microscopy. This method is…
Materials characterization remains a significant, time-consuming undertaking. Generally speaking, spectroscopic techniques are used in conjunction with empirical and ab-initio calculations in order to elucidate structure. These experimental…
Over the past decade, reflection matrix microscopy (RMM) and advanced image reconstruction algorithms have emerged to address the fundamental imaging depth limitations of optical microscopy in thick biological tissues and complex media. In…