PNNL researchers established an Internet of Things Common Operating Environment (IoTCOE) laboratory to explore the risks associated with IoT connectivity to the internet, the energy grid and other critical infrastructures.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.

PNNL quantum computing pioneer Nathan Wiebe contributed to a Google-led quantum computing landmark study published in the journal Science.

The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.

Making sure there’s enough electricity at the lowest price is a critical endeavor undertaken daily by electricity market operators. Now, there’s an approach that provides more timely and accurate information to make day-ahead decisions.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.

Ang Li and Antonino Tumeo will present hardware to balance machine learning workloads at a top computer architecture conference.

PNNL data scientist Carlos M. Ortiz Marrero has co-edited a volume that highlights work at the intersection of quantum computation and mathematics.

PNNL’s Patrick Balducci delivered an information-packed tutorial on grid energy storage valuation at the Naval Postgraduate School in Monterey, California.

PNNL quantum algorithm theorist and developer Nathan Wiebe is applying ideas from data science and gaming hacks to quantum computing

PNNL employs deep learning strategies to advance Model Predictive Control (MPC), a highly promising solution for intelligent building operations.

The race toward the first practical quantum computer is in full stride. Scientists at PNNL are bridging the gap between today’s fastest computers and tomorrow’s even faster quantum computers.

First-of-its-kind network analysis on a supercomputer can speed real-time applications for cybersecurity, transportation, and infectious disease tracking

Sonja Glavaski and Kevin Schneider, both electrical engineers at PNNL, have been named as IEEE fellows. IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

A student computing security research project guided by PCSD computer scientists Ang Li and Kevin Barker placed third among dozens of entries in the student research poster session at SC19, a premier annual conference for high-performance c

At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.

PNNL’s Intelligent Load Control technology manages and adjusts electricity use in buildings when there’s peak demand on the power grid.

Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.

With support from DOE’s Office of Electricity and National Grid, PNNL led a groundbreaking study to accurately assess the full value of grid energy storage investments across a wide variety of use cases.

Energy storage is slowly shifting utility planning practices from the current paradigm, which ensures grid reliability by building reserve generation resources, to ensuring grid reliability by optimizing grid services.