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

Developing a new scheme to more efficiently solve quantum chemistry 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 Program Development Office Director Karl Mueller explains how the TEC4 project seeks to make advanced chemical computations accessible to all.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

Electronic structure codes in a cloud environment can enable efficient computational chemistry simulations.

CACTUS provides AI-powered cheminformatics for autonomous science.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

Researchers discussed this topic at the 2024 Analog Computing for Science Workshop co-led by Antonino Tumeo.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.

Researchers will explore climate, pathogens and energy-efficient microelectronics using 3 million node hours on the nation’s supercomputers.

Complex metal alloys enter a new era of predictive design for aerospace and other high-temperature applications.

A new study demonstrates a hybrid model that can simulate part of a system at the molecular scale and other parts at larger scales in a computationally efficient manner, providing greater simulation flexibility.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.