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Cellular elastomeric metamaterials are interesting for various applications, e.g. soft robotics, as they may exhibit multiple microstructural pattern transformations, each with its characteristic mechanical behavior. Numerical literature…
Magnetic properties of electrical steel are usually measured on Single Sheet Testers, Epstein frames or ring cores. Due to the geometric dimensions and measurement principles of these standardized setups, the fundamental microstructural…
We suggest the new experimental method to explore the properties of slow strange mesons at normal nuclear matter density. We show that the $K^{+}$ and $K^{-}$ mesons with extremely small momenta relative to the surrounding medium rest frame…
Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data-driven methods for the discovery of patterns among multiple lengthscales and time-scales…
Micro and nanoscale materials have remarkable mechanical properties, such as enhanced strength and toughness, but usually display sample-to-sample fluctuations and non-trivial size effects, a nuisance for engineering applications and an…
This study is focused on the mechanical characterization of materials used in microelectronic and micro- electromechanical systems (MEMS) devices. In order to determine their mechanical parameters, a new deformation bench test with suitable…
Atom probe tomography requires needle-shaped specimens with a diameter typically below 100 nm, making them both very fragile and reactive, and defects (notches at grain boundaries or precipitates) are known to affect the yield and data…
Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale…
A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules. These features are evaluated by monitoring performance…
Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prior to training and evaluating a model's performance would be…
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and…
High-quality charged current quasielastic scattering data have recently been reported for both muon neutrinos and antineutrinos from several accelerator-based neutrino experiments. Measurements from MiniBooNE were the first to indicate that…
Magnetic nanoparticles have proven to be valuable for biomedical applications. A prerequisite for the efficiency of these applications are precisely characterized particles. Thermal Noise Magnetrometry (TNM), unlike any other magnetic…
One of the essential questions in the area of granular matter is, how to obtain macroscopic tensorial quantities like stress and strain from ``microscopic'' quantities like the contact forces in a granular assembly. Different averaging…
A simple model is used to explore issues related to self-similarity, intermittency and scaling behavior in nuclear collisions. Both scaled factorial moments and power moments are considered in the investigation of these features. The…
The possibility of measuring neutral-current coherent elastic neutrino-nucleus scattering (CENNS) at the TEXONO experiment has opened high expectations towards probing exotic neutrino properties. Focusing on low threshold Germanium-based…
The incredible thermo-mechanical properties of biological materials arise from the microscopic scale due to a complex hierarchical mechanism, regulated by microinstabilities at the molecular level. The description of such complex structures…
Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when…
We discuss recent experimental results on the modification of hadron properties in a nuclear medium. Particular emphasis is placed on an $\omega$ production experiment performed by the CBELSA/TAPS collaboration at the ELSA accelerator. The…
Solid state theory, density functional theory and its generalizations for correlated systems together with numerical simulations on supercomputers allow nowadays to model magnetic systems realistically and in detail and can be even used to…