As new paradigms in advanced computing take shape, computational chemistry researchers are finding new ways to solve challenging chemistry problems.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.

In soil, microbes produce and consume methane. Using a technique called pool dilution, researchers can separate the rate of methane production and consumption from the net rate.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

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.

Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

In honor of National Intern Day, PNNL and intern Jonathan Mills were named among the Top 100 in the nation by early-career recruiting firm WayUp.

As wildfires grow increasingly severe, PNNL researchers are working to slow their spread and mitigate their harmful effects.

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

A new extrusion process eliminates pre-heating to provide significant energy savings during production of extruded aluminum alloys.

The first-ever simulation of aluminum conductivity offers a recipe for an inexpensive, lightweight alternative to copper.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Artificial intelligence-based decision tool enables grid operators to respond more quickly and effectively to grid emergencies.