A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.
ChatGrid is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.
The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.
PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.
PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.
To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.