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New Conservation Laws in Turbulent Magnetized Flows
University of Rochester (UofR) researchers used a novel coarse-graining framework for disentangling multiscale interactions to find the existence of two separate conservation laws over an entire range of length scales in turbulent magnetized flows. The work relied on a suite of massively parallel simulations run on NERSC using the DiNuSUR code developed at UofR. Read More »
Petawatt Laser Guiding and Electron Beam Acceleration to 8 GeV in a Laser-Heated Capillary Discharge Waveguide
Researchers at Berkeley Lab’s BELLA Center set a new world record in laser-driven plasma-based electron acceleration by obtaining beams with an energy of up to 7.8 GeV in a 20 cm-long plasma using the high-power BELLA laser. The maximum achieved energy nearly doubled their previous record set in 2014. Read More »
Machine-Learned Impurity Level Prediction in Semiconductors
Argonne National Laboratory (ANL) researchers ran high-throughout atomistic simulations on NERSC supercomputers and generated comprehensive computational datasets of impurity properties in two classes of semiconductors: lead-based hybrid perovskites and Cd-based chalcogenides. These datasets led to machine learned models which enable accelerated prediction and design for the entire chemical space of materials and impurities in these semiconductor class. Read More »
Mapping Neutral Hydrogen in the Early Universe
Researchers at Berkeley Center for Cosmological Physics (BCCP) developed a model that produces maps of the 21cm emission signal from neutral hydrogen in the early universe. Thanks to NERSC supercomputers, the team was able to run simulations with enough dynamic range and fidelity to theoretically explore this uncharted territory which contains 80% of the feasibly observable universe by volume and holds the potential to revolutionize cosmology. Read More »
Enabling Thermochemistry Estimation using Deep Learning
MIT researchers developed an automated system to continually perform quantum chemistry calculations and use the results to continually retrain a deep learning model for predicting the thermochemistry, i.e., enthalpy of formation, entropy, and heat capacities, of complex polycyclic molecules. A novel approach for estimating uncertainties in these predictions was used to identify which new molecules should be refined with quantum chemistry and added to the training data set. NERSC resources enabled performing many quantum chemistry calculations automatically and in parallel. Read More »
