PNNL researchers learn to control peptoid “handedness,” one key to precision drug delivery and diagnostics.

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

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

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Converting seawater into hydrogen fuel using electrochemistry offers a renewable energy future for Guam.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

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

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL scientists are creating new ways to learn more about the vast sea of chemicals that haven't been identified—yet.

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.

Researchers created the first open-source database of exotic materials eyed for use in quantum applications.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

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.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

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

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.