First-principles determination of phonon lifetimes, mean free paths, and thermal conductivities in crystalline materials:Pure silicon and germanium

The thermal properties of insulating, crystalline materials are essentially determined by their phonon dispersions, the finite-temperature excitations of their phonon populations-treated as a Bose-Einstein gas of harmonic oscillators-and the lifetimes of these excitations. The conceptual foundations of this picture are now a well-established cornerstone in the theory of solids. However, only in recent years our theoretical and algorithmic capabilities have reached the point where we can now determine all these quantities from first-principles, i.e. from a quantum-mechanical description of the system at hand without any empirical input. Such advances have been largely due to the development of density-functional perturbation theory that allows to calculate second-and third-order perturbations of a system of interacting electrons with a cost that is independent of the wavelength of the perturbation. Here we present an extensive case study for the phonon dispersions, phonon lifetimes, phonon mean free paths, and thermal conductivities for isotopically pure silicon and germanium, showing excellent agreement with experimental results, where available, and providing much needed microscopic insight in the fundamental atomistic processes giving rise to thermal conductivity in crystals

Network Aggregation to Enhance Results Derived from Multiple Analytics

Part 2: Clustering/Unsupervised Learning/AnalyticsInternational audienceThe more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architectures