Related papers: Machine Learning on Neutron and X-Ray Scattering
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the…
The present chapter reviews current neutron and x-ray scattering techniques employed to elucidate the magnetic structures and spin dynamics of magnetic materials. Both techniques provide measurements as a function of the energy and the…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a…
The structural investigations of nanomaterials motivated by their large variety and diverse set of applications have attracted considerable attention. In particular, the ever-improving machinery, both in laboratory and at large scale…
Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds,…
Lattice dynamics play a crucial role in understanding the physical mechanisms of cutting-edge energy materials. Many excellent energy materials have complex multiple-sublattice structures, with intricate lattice dynamics, and the underlying…
We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is…
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…
Inelastic neutron and X-ray scattering experiments on surfaces and interfaces are a challenging topic in modern physics. Particular interest arises regarding surfaces and interfaces of soft matter and biological systems. We review both…
Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing…
Neutron scattering techniques offer a unique combination of structural and the dynamic information of atomic and molecular systems over a wide range of distances and times. The increasing complexity in science investigations driven by…
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…
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…
Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid…
Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applications. However, accurately determining their structure from…
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…
The spin of the neutron allows neutron scattering to reveal the magnetic structure and dynamics of materials over nanometre length scales and picosecond timescales. Neutron scattering is particularly in demand in order to understand…
Scattering often limits the controlled delivery of light in applications such as biomedical imaging, optogenetics, optical trapping, and fiber-optic communication or imaging. Such scattering can be controlled by appropriately shaping the…
Understanding and predicting the emergence of novel materials is a fundamental challenge in condensed matter physics, materials science and technology. With the rapid growth of materials databases in both size and reliability, the challenge…