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Vibrational spectroscopy is key for probing the interplay between the structure and dynamics of aqueous systems. In order to map different regions of experimental spectra to the microscopic structure of a system, it is important to combine…
Mid-infrared (mid-IR) spectroscopy is a crucial workhorse for a plethora of analytical applications and is suitable for diverse materials, including gases, polymers or biological tissue. However, this technologically significant wavelength…
Infrared (IR) spectroscopy can be used as an important and effective tool for probing periodic networks of quantum wires or nanotubes (quantum crossbars, QCB) at finite frequencies far from the Luttinger liquid fixed point. Plasmon…
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which…
Spectral imaging enables spatially-resolved identification of materials in remote sensing, biomedicine, and astronomy. However, acquisition times require balancing spectral and spatial resolution with signal-to-noise. Hyperspectral imaging…
Accurate and reliable Magnetic Resonance Imaging (MRI) analysis is particularly important for adaptive radiotherapy, a recent medical advance capable of improving cancer diagnosis and treatment. Recent studies have shown that IVIM-NET, a…
Vibrational spectra of condensed and gas-phase systems containing light nuclei are influenced by their quantum-mechanical behaviour. The quantum dynamics of light nuclei can be approximated by the imaginary time path integral (PI)…
Implicit neural representation (INR) has emerged as a promising solution for encoding volumetric data, offering continuous representations and seamless compatibility with the volume rendering pipeline. However, optimizing an INR network…
A theoretical framework is presented for the computation of rovibrational polaritonic states of a molecule in a lossless infrared (IR) microcavity. In the proposed approach the quantum treatment of the rotational and vibrational motion of…
Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software) - an automated computational…
Gravitational wave detection has opened up new avenues for exploring and understanding some of the fundamental principles of the universe. The optimal method for detecting modelled gravitational-wave events involves template-based matched…
A combined approach that uses the vibrational self-consistent field (VSCF) and vibrational configuration interaction (VCI) method together with the semiclassical instanton theory was developed to study vibrational tunneling spectra of…
Millimeter-wave (mmWave) radar has emerged as a compact and powerful sensing modality for advanced perception tasks that leverage machine learning. It is particularly effective in scenarios where vision-based sensors fail to capture…
Radiation symmetry evaluation is critical to the laser driven Inertial Confinement Fusion (ICF), which is usually done by solving a view-factor equation model. The model is nonlinear, and the number of equations can be very large when the…
We propose a fast and non-destructive spectroscopic method for single molecular ions that implements quantum logic schemes between an atomic ion and the molecular ion of interest. Our proposal relies on a hybrid coherent manipulation of the…
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…
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics…
Time-frequency analysis is an important and challenging task in many applications. Fourier and wavelet analysis are two classic methods that have achieved remarkable success in many fields. However, they also exhibit limitations when…
Spectral methods are widely used in geometry processing of 3D models. They rely on the projection of the mesh geometry on the basis defined by the eigenvectors of the graph Laplacian operator, becoming computationally prohibitive as the…
Model compression is critical for deploying deep learning models on resource-constrained devices. We introduce a novel method enhancing knowledge distillation with integrated gradients (IG) as a data augmentation strategy. Our approach…