To extract transferable insights from scanning transmission electron microscopy (STEM), one must deal with noise arising from electron scattering and of the investigated sample. This noise hinders a quantitative analysis of the observation, notably when the features of interest lie in the gradients of the raw data. Physics-informed neural networks have been proposed as a means to incorporate...
Since few years, in addition to classical (simulation-based) HPC usage, we observe a steadily growing need of our users for support of high-performance data-analytics (HPDA) and AI workflows.
In this presentation, we will give an overview of the HPC clusters available at MPCDF for HPDA and AI workflows, the available (HPC-optimized) software stack and we will present recently introduced...
Quantitatively understanding the link between anharmonicity and thermal conductivity, $\kappa$, is pivotal to the search for better thermal insulators. To help find this link we present new descriptors of $\kappa$ based on our new measure of anharmonicity, $\sigma^\mathrm{A}$. Using an updated sure-independence screening and sparsifying operator (SISSO) method, we find analytical expressions...
Normalizing flows are a popular class of models for approximating probability distributions. However, in many tasks such as image generation benchmarks they are still outperformed by autoregressive models and generative adversarial networks. This is in part due to their invertible nature limiting their ability to model target distributions with a complex topological structure. Several...
