Catalysts that efficiently transfer hydrogen for storage in organic hydrogen carriers are key for more sustainable generation and use of hydrogen. New research identifies activity descriptors that can accelerate novel catalyst development.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

1-propanol converts from a monomer into a trimer at aluminum sites within zeolite pores.

Studying single crystal reactions provides molecular-level insight into catalytic processes.

Scientists have developed a better way to recognize denial-of-service internet attacks, improving detection by 90 percent.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

Researchers have knitted together three large databases of information to help cybersecurity experts detect and prevent attacks.

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

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

A new method to convert waste plastic to fuel and raw materials promises to help close the carbon cycle at mild temperature and with high yield.

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

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

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.

PNNL scientists carve a path to profit from carbon capture by creating a system that efficiently captures CO2 and converts it into one of the world’s most widely used chemicals: methanol.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.

Using electrocatalysis offers a different path for reactions involving polar groups.