We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

This paper presents the current state of the global gyrokinetic code Orb5 as an update of the previous reference [Jolliet et al., Comp. Phys. Commun. 177 409 (2007)]. The Orb5 code solves the electromagnetic Vlasov-Maxwell system of equations using a PIC scheme and also includes collisions and strong flows. The code assumes multiple gyrokinetic ion species at all wavelengths for the polarization density and drift-kinetic electrons. Variants of the physical model can be selected for electrons such as assuming an adiabatic response or a "hybrid" model in which passing electrons are assumed adiabatic and trapped electrons are drift-kinetic. A Fourier filter as well as various control variates and noise reduction techniques enable simulations with good signal-to-noise ratios at a limited numerical cost. They are completed with different momentum and zonal flow-conserving heat sources allowing for temperature-gradient and flux-driven simulations. The code, which runs on both CPUs and GPUs, is well benchmarked against other similar codes and analytical predictions, and shows good scalability up to thousands of nodes.

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The physical processes active during the crystallization of a low-pressure, low-gravity planetesimal core are poorly understood but have implications for asteroidal magnetic fields and large-scale asteroidal structure. We consider a core with only a thin silicate shell, which could be analogous to some M-type asteroids including Psyche, and use a parameterized thermal model to predict a solidification timeline and the resulting chemical profile upon complete solidification. We then explore the potential strength and longevity of a dynamo in the planetesimal's early history. We find that cumulate inner core solidification would be capable of sustaining a dynamo during solidification, but less power would be available for a dynamo in an inward dendritic solidification scenario. We also model and suggest limits on crystal settling and compaction of a possible cumulate inner core.Iron meteorites can also provide information on the cooling rate experienced after solidification. Yang et al. [2008] examined several irons in the IVA meteorite family and found a large range of cooling rates within Key Points: • The rate and structure of inward fractional crystallization are constrained • Crystal settling could form a cumulate inner core with significant trapped melt • Constraints are placed on dynamo strength and longevity during solidification