The Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge (AT SCALE) Initiative will transform materials synthesis through closed-loop autonomous experimentation.

PNNL is leading the nation with research addressing urgent needs for reimagining U.S. critical infrastructure against the realities of software-speed attacks and hazards.

The Center for AI @PNNL is driving a research agenda that explores the foundations and emerging frontiers of AI, combining capability development and application to mission areas in science, security and energy resilience.

Pacific Northwest National Laboratory is partnering with the U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency to develop and operate the Control Environment Laboratory Resource

The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.

Usable and explainable models for global-scale, cross-lingual proliferation expertise identification and forecasting.

Pacific Northwest National Laboratory’s (PNNL) Generative AI (GenAI) investment aims to harness this transformative technology to drive innovation across the science, energy, and security research domains.

PNNL is heavily engaged in the development and use of mass spectrometry technology across its science, energy, and security missions, from fundamental research through mature operational capabilities.

The Mathematics for Artificial Reasoning in Science (MARS) initiative seeks to complement machine learning and deep learning to advance science.

Mega AI seeks to develop massive-scale, self-supervised, multimodal foundation models of scientific knowledge capable of general-purpose inferences to enable reasoning with existing knowledge and discovery of new knowledge.

Pacific Northwest National Laboratory supports innovations in data analytics, instrumentation, and experimental techniques for the Northwest (NW) Biopreparedness Research Virtual Environment (BRaVE) Initiative.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.

Using a reverse genome strategy to target the molecular basis of function.

PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

The Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems (SEA-CROGS) Center is a collaborative effort that advances scalable and efficient physics-informed machine intelligence.