לוח שנה משולב

abstract
Warm dense matter (WDM) is state of matter characterized by temperatures of the order of 10,000 K and nuclear densities of magnitude significantly higher than those found in ordinary condensed matter. WDM is found in many fields of physics, chemistry, planetary sciences, and even industry: stellar and planetary science, laser-induced chemical processes in solids and on surfaces as well as plasma physics.
Nowadays, using intense lasers, WDM properties can be investigated in the laboratory, thus requiring attention to theoretical research for interpretation and understanding of the results. The theoretical description of the regime is complex, being the intermediate between condensed matter physics (i.e., quantum description) and plasma physics (classical thermodynamics). WDM is often described theoretically using finite-temperature Kohn-Sham (KS) density functional theory (DFT) calculations, with reasonably good agreement to experiments. These calculations in finite (non-zero) temperatures are costly due to the large number of fractionally occupied KS orbitals that are involved. The computational cost exhibits cubic scaling with temperature.
Stochastic density functional theory (sDFT), developed recently [1,2,3] appears to be a viable approach to WDM since it is “orbital-free” and yet fully KS. The sDFT approach uses the Fermi Dirac occupation operator to calculate the energy, and finds the electronic density and other expectation values by executing the trace in a stochastic way using random orbitals; in doing so, it skips over the step of calculating the KS orbitals. We further use the stochastic trace formula to calculate the conductivity based on Kubo-Greenwood formula. In the talk, convergence of the free energy will be discussed, as well as calculations of equations of states and statistical convergence of the conductivity when calculated based on stochastic thermal DFT. Transition to metallization in he-h systems was seen in temperature of ~60kK using the stochastic approach.
[1] R. Baer, D. Neuhauser, E. Rabani, Phys. Rev. Lett. 111, 106402 (2013)
[2] Y. Cytter, E. Rabani, D. Neuhauser, and R. Baer Phys. Rev. B 97, 115207 (2018)
[3] Yael Cytter, Eran Rabani, Daniel Neuhauser, Martin Preising, Ronald Redmer, and Roi Baer Phys. Rev. B 100, 195101 (2019)

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The presentation will review two prospective analytic epidemiologic cohort studies of aging in which all participants are organ donors. It will summarize associations of risk factors for common chronic neurologic conditions of aging with an emphasis on Alzheimer’s dementia. It will then summarize the relation of different pathologies and resilience markers assessed at autopsy. Together this will highlight the complexity of neurodegenerative diseases. Next, it will illustrate how multi-level brain omic data can be mined to identify novel therapeutic targets. Finally, it will summarize a strategy to move these targets to a high throughput personalized medicine pipeline for compound screening.