David Senor has been selected for the Department of Energy’s Fusion Energy Sciences Advisory Committee.

Two PNNL teams have been recognized for their work on safe, secure radioactive material shipping.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

A PNNL team is leading the design, fabrication, and regulatory testing, and delivery of new packaging units that will be used to ship radioactive materials safely and securely.

Vanda Glezakou and Laboratory Fellow Roger Rousseau collaborated with other experts to write an American Chemical Society book chapter.

A hybrid computing approach for solving quantum chemical problems offers a practical bridge between classical and quantum computing.

The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.