A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

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.

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.

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

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

CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

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.

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

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.

New research informs the development of foundational models for computational chemistry through hardware-software co-design.