Related papers: Predicting solution scattering patterns with expli…
Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose.…
Simulations of turbulent fluid flow around long cylindrical structures are computationally expensive because of the vast range of length scales, requiring simplifications such as dimensional reduction. Current dimensionality reduction…
Molecular dynamics simulations are powerful tools to extract the microscopic mechanisms characterizing the properties of soft materials. We recently introduced machine learning surrogates for molecular dynamics simulations of soft materials…
Performing reliability analysis on complex systems is often computationally expensive. In particular, when dealing with systems having high input dimensionality, reliability estimation becomes a daunting task. A popular approach to overcome…
Neutrons and x-rays are coherent probes, and their coherent properties are used in scattering experiments. Only coherent scattering probes can elucidate collective molecular motions. While phonons in crystals were studied for half a century…
Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and…
Recently, a new neutron spectroscopy for the dynamics in complex (bio-) systems has been proposed [A. Benedetto, and G. J. Kearley, Sci. Rep. 6, 34266, (2016)]. This spectroscopy is ideal where only the overall relaxation time in a…
Contrast variation small-angle neutron scattering (CV-SANS) is a powerful tool to evaluate the structure of multi-component systems by decomposing scattering intensities $I$ measured with different scattering contrasts into partial…
The sol-gel silica glasses doped with copper selenide nanoparticles were studied through small-angle neutron scattering (SANS) and transmission electron microscopy. SANS intensities demonstrate the complicated dependence on the scattering…
Hypothesis Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external…
Neutron scattering is a powerful probe of strongly correlated systems. It can directly detect common phenomena such as magnetic order, and can be used to determine the coupling between magnetic moments through measurements of the spin-wave…
Predicting volume swelling of structural materials in nuclear reactors under high-dose neutron irradiations based on existing low-dose experiments or irradiation data with high-dose-rate energetic particles has been a long-standing…
The cross section for coherent elastic neutrino-nucleus scattering (CE$\nu$NS) depends on the response of the target nucleus to the external current, in the Standard Model (SM) mediated by the exchange of a $Z$ boson. This is typically…
Solvation is a notoriously difficult and nagging problem for the rigorous theoretical description of chemistry in the liquid phase. Successes and failures of various approaches ranging from implicit solvation modeling through dielectric…
Building the European Spallation Source (ESS), the most powerful neutron source in the world, requires significant technological advances at most fronts of instrument component design. Detectors are not an exception. The existing…
Resonant soft X-ray scattering (RSXS) is a powerful technique for probing both spatial and electronic structures within solid-state systems. We present a newly developed RSXS capability at beamline 13-3 of the Stanford Synchrotron Radiation…
We develop the techniques of \cite{KS1} and \cite{ES1} in order to derive dispersive estimates for a matrix Hamiltonian equation defined by linearizing about a minimal mass soliton solution of a saturated, focussing nonlinear Schr\"odinger…
This work presents an illustrative application of the second-order adjoint sensitivity analysis procedure (SO-ASAP) to a paradigm neutron diffusion problem, which is sufficiently simple to admit an exact solution, thereby making transparent…
Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed as the \emph{entropy} computed by the transformed…
Witnessing subsystems for probabilistic reachability thresholds in discrete Markovian models are an important concept both as diagnostic information on why a property holds, and as input to refinement algorithms. We present SWITSS, a tool…