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The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

Updated exascale system for Earth simulations delivers twice the speed

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

Scientists use Summit supercomputer, deep learning to predict protein functions at genome scale

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.

ORNL is making underused or inaccessible bioenergy data available to accelerate innovation for the bioeconomy. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Bioenergy — Data boost

A research team from Oak Ridge National Laboratory has identified and improved the usability of data that can help accelerate innovation for the growing bioeconomy.

ORNL’s Eva Zarkadoula seeks piezoelectric materials for sensors that can withstand irradiation, which causes cascading collisions that displace atoms and produces defects. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Materials — Radiation-resistant sensors

To advance sensor technologies, Oak Ridge National Laboratory researchers studied piezoelectric materials, which convert mechanical stress into electrical energy, to see how they could handle bombardment with energetic neutrons.