PNNL’s building science research and technology deployment initiatives were highlighted at the HUD Innovative Housing Showcase.

Faced with limited data, researchers at PNNL studied different techniques to improve disease prediction in resource-limited settings.

The PNNL-managed Building America Solution Center translates research into actionable considerations for homeowners and builders to provide two solutions in one: increasing energy efficiency while also enhancing disaster resistance.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

Joint appointments at PNNL and UTEP are teaming together to strengthen and protect sensitive data.

A PNNL team’s analysis of new-housing data concludes that single-family homes in lower-income counties are less energy-code-compliant than in higher-income counties, a finding that could shape strategies for enhanced code adoption.

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

Staff at PNNL recently completed a report highlighting commercial products enabled through projects funded by the Department of Energy’s Building Technologies Office.