A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.

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.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

A new perspective article discusses how integrating carbon dioxide capture and conversion in solvents can lead to cheaper and more efficient carbon management systems.

Providing a perspective on the importance of integrating experimental work with theory and simulations when designing new carbon capture solvents.

Ionic liquids bound to thin layers of graphene oxide produces more selective and efficient multi-layer membranes for water filtration.

Surface characterization allows researchers to explore the compatibility between different solvent-membrane combinations.