Mystery of how important aluminum minerals form and dissolve solved with materials science and advanced imaging studies.

The Genesis Mission will mobilize the Department of Energy’s 17 National Laboratories, industry, and academia to build an integrated discovery platform.

AI for Good event commemorates 60 years of community engagement, features AI applications, and offers practical AI tools to empower people.

Ice crystals are surprisingly tolerant of defects in their structure. The findings come from the first-ever molecular-resolution observations of nanoscale samples of ice frozen from liquid water.

Micron and PNNL co-design the Crete computer system, delivering a new memory architecture for data-intensive scientific computing and AI applications.

PNNL showcased its expertise in AI, machine learning, and related domains at the International Conference on Machine Learning.

Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

A new time-dependent wavefunction formulation for the cumulant Green’s functions can produce more accurate simulations.

Brown University professor and PNNL joint appointee was honored for his exceptional work in applied mathematics.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

Developing a new scheme to more efficiently solve quantum chemistry problems.

John VerWey, an advisor in the Mission Alignment group at PNNL, has recently been selected to lead a panel discussion at the inaugural Special Competitive Studies Project (SCSP) AI+ Compute & Connectivity Summit.

Formal mathematics both reveals what’s inside the black box of AI algorithms and pushes the boundary of what’s possible within math as a discipline.

Researchers can now quantify uncertainty in trained AI models aimed at solving difficult chemistry, biology and materials science problems.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

Nine national laboratories, including PNNL, bring together over 1,000 scientists for a day to use AI to accelerate scientific discovery.

PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.

PNNL Program Development Office Director Karl Mueller explains how the TEC4 project seeks to make advanced chemical computations accessible to all.