Scientific Highlights from Research Division "Scientific Computing, Theory and Data" (SCD)
Deep learning to avoid weather disappointments
Saharan dust storms played havoc with weather predictions. Invertible neural networks to retrieve aerosol properties from light scattering data may help.
Superconducting qubit first success at Quantum Computing Hub
Andreas Wallraff talks about moving in, refrigerators and measuring the first superconducting qubit at the ETHZ-PSI Quantum Computing hub.
How immune cells are activated
A research consortium has deciphered the mechanism of CCR5 receptor activation, providing insights for the development of CCR5 drug antagonists for AIDS, cancer, and inflammatory diseases.
Two scenarios for superconductivity in CeRh2As2
CeRh2As2, a nonsymmorphic heavy fermion material, was recently reported to host a remarkable temperature versus z-axis magnetic-field phase diagram with two superconducting phases. In this material, the two inequivalent Ce sites per unit cell, related by inversion symmetry, introduce a sublattice structure corresponding to an extra internal degree of freedom. In this work, we propose a classification of the possible superconducting states in CeRh2As2 from the two Ce-sites' perspective.
Watching receptor proteins changing shape
In our bodies, G protein-coupled receptors mediate countless processes. PSI researcher Ramon Guixà talks about how he brings those receptor molecules to life on the computer screen.
New blueprint for more stable quantum computers
PSI researchers have shown how faster and better defined quantum bits can be created. The central elements are magnetic atoms from the class of so-called rare-earth metals, selectively implanted into the crystal lattice of a material.
Fellow Award for Dr. Mantzaras
The prestigious Fellow Award “Fellow of The Combustion Institute” was allotted to Dr. Mantzaras for “Pioneering Experimental and Modeling Research in Hetero-/Homogeneous and Catalytic Combustion”. The combustion activities at LSM emphasize on non-intrusive laser-based measurements in a high-pressure optically accessible catalytic reactor, while the modeling activities encompass advanced multidimensional numerical simulations and theoretical work based on activation energy asymptotics.
Benefit of random testing
With the imminent relaxation of socio-economic restrictions, it becomes vital to assess its effect on the prevalence of acute infections within the population, as rapidly as possible. Currently available monitoring instruments for the COVID-19 pandemic have an inherent time delay of about 14 days, as they rely on confirmed infections, hospitalizations, and death numbers. These methods give Reff(t) (the number of infections caused by a single infected person), but their delay is a significant disadvantage when restrictions are released. If after relaxation, Reff(t) rises above 1, one will not be able to react adequately before two weeks have passed during which time the prevalence could significantly rise. Here, we propose the use of random testing to shorten this reaction time, by obtaining direct and modeling dependent information on Reff(t). Through random testing of between 2500 and 20000 people per day, we find that over periods significantly shorter than two weeks, it becomes possible to detect a dangerous increase in Reff with reasonable confidence.
Simulation: The third pillar of science
PSI researchers simulate and model large research facilities as well as experiments, for example, in the materials and biological sciences. Andreas Adelmann, head of PSI's Laboratory for Scientific Computing and Modelling, explains how they do it.