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

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.

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

CACTUS provides AI-powered cheminformatics for autonomous science.

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

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

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

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.

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

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.

Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.

A new approach validates quantum algorithms for low-energy nuclear physics applications

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