The Human Factors Symposium took place at Discovery Hall at PNNL in May 2023. Fifty-seven attendees participated in the three-day event representing 15 different institutions.

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

Highly precise and controllable single-atom catalysts are affected by reaction conditions, which can alter the bonding around the atoms and the activity.

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

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

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Water affects the intermediates present on the surface of a metal oxide catalyst during ketonization.

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

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A new system can convert common plastics into valuable fuel molecules.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

Within zeolite pores, rates of alcohol dehydration increase with ionic strength until reaching a maximum and then decreasing.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

In a water-containing system, the open circuit potential stabilizes positively charged species and lowers the energy barrier of the reaction.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.