August 2019   |   Materials Science

Power play

Carnegie Mellon-led team generates atomic-scale simulations at Argonne in search of the best new solar cell materials.

Fusion Energy
June 2019

Fusion’s path to practicality

Company embraces supercomputing in quest for viable fusion energy.

Machine Learning, New Faces
May 2019

Lessons machine-learned

The current class of early-career honorees applies machine-learning tools to science problems.

April 2019

Biting back at the flu bug

Supercomputer power is providing influenza insights to help drug designers produce a more effective vaccine.

Science Highlights

May 2019

AI and deep learning for fusion

Princeton researchers apply deep learning to a new code, Fusion Recurrent Neural Network (FRNN), to forecast events that disrupt fusion reactions.

Bagel-shaped fusion reactors called tokamaks produce energy of the kind that powers the sun. But massive disruptions can halt fusion reactions and damage the reactors.  Princeton University researchers’ Fusion Recurrent Neural Network (FRNN) code harnesses an artificial-intelligence tool called deep-learning to predict disruptive events.  Researchers can also use the code to make predictions that could open avenues for active reactor control and optimization. The method, reported in Nature, holds promise for enabling steady-state operation of tokamaks.

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