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.

Advancing the understanding and monitoring of nuclear material processing to accelerate development and qualification of new material systems for national security and nuclear energy.

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.

The Molecular Observation Network is a national open science network designed to produce a comprehensive database of molecular and microstructural information on soil, water, microbial communities, and biogenic emissions.

The Pacific Northwest Advanced Compound Identification Center (PNACIC) brings together innovations in integrated chemistry and advanced instrumentation to create a platform for comprehensive, unambiguous identification of metabolites.

The Pacific Northwest Center for Cryo-EM electron microscopy is a user facility operated jointly by PNNL and Oregon Health & Science University.

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.

PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.

PNNL computer scientists are presenting the latest research in HPC at SC20.

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

The user-friendly Project Schedule Visualizer software developed at PNNL helps users readily identify and understand the impacts of updates to the schedule, budget, and risks associated with large, complex projects that cross departments.

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 RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.

PNNL's River Corridor Hydrobiogeochemistry Scientific Focus Area works to transform understanding of spatial and temporal dynamics in river corridor hydrobiogeochemical functions from molecular reaction to watershed and basin scales.

Powered by few-shot learning, the Sharkzor AI-driven, scalable web application makes it possible to quickly characterize and sort electron microscopy images used to analyze radioactive materials.

Advancing our understanding of how soil microbial communities respond to—and affect—changing environmental conditions

PNNL’s Space Science, Technology, and Policy activities draw on diverse technical expertise and research capabilities from across the Laboratory.

A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.