For the first time, researchers at Berkeley Lab have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more.
Predicting Metallic Defects with Machine Learning
Predicting Defects with Machine Learning
Invisible Chaos of Superluminous Supernovae
Berkeley Lab to Lead AMR Co-Design Center
CCSI Toolset Wins 2016 R&D100 Award
To better understand the physical conditions that create superluminious supernova, astrophysicists are running 2D simulations of these events using supercomputers at NERSC and CRD developed CASTRO code.
A paper published December 15 during the American Geophysical Union (AGU) fall meeting in San Francisco points to new evidence of human influence on extreme weather events.
When the latest version of the Graph 500 list was released Nov. 16 at the SC16 conference, there were two new entries in the top 10, both contributed by Khaled Ibrahim of Berkeley Lab’s Computational Research Division.