A new generation of microelectronics research begins at PNNL.

Sergei Kalinin, a joint appointee at the University of Tennessee, Knoxville and PNNL, and Ji-Guang (Jason) Zhang, a PNNL Lab Fellow, are part of the 2024 class of National Academy of Inventors Fellows.

His expertise in data science and commitment to open science will drive dataset dissemination and advance collaborative global research efforts.

Federal environmental permitting process to be fast-tracked with AI-powered tools and cloud-supported data analysis.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

PNNL chief data scientist Lauren Charles participated in a science-focused panel linked to the 79th session of the United Nations General Assembly.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

Samrat Chatterjee will serve on the editorial board of Risk Analysis, the flagship journal of the Society for Risk Analysis.

PNNL researchers Marisa Decillis and Luke Richards contributed to a Center for Security and Emerging Technology report, “Securing Critical Infrastructure in the Age of AI.”

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

A team of researchers at PNNL is developing a new approach to explore the higher-dimensional shape of cyber systems to identify signatures of adversarial attacks.

Samrat (Sam) Chatterjee, a PNNL chief data scientist and team leader with the Data Sciences and Machine Intelligence group, was co-author of a CSET workshop report on agentic artificial intellilligence

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Joint Appointee Mitra Taheri spoke on the AI in Government episode of the EY Leading into Tomorrow podcast.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

PNNL computational and data scientist Mark Maupin was the team lead for a paper published in the journal Molecules.