PNNL

The Materials, Characterization, Prediction, and Control (MCPC) internally funded investment builds on the successful collaboration and integration of Pacific Northwest National Laboratory's (PNNL's) capabilities in materials science and engineering, physics-based computational sciences, interfacial chemistry, metrology, and data analytics. Our team has the advantage of having access to state-of-the-art materials characterization tools needed to validate modeling methods and train machine learning algorithms against ground truth. Our unique facilities allow us to conduct experiments at pilot-scale—filling the niche between universities and full-scale production facilities.

The MCPC investment has four main objectives:

• Accelerate modeling-based prediction of material microstructure.
• Apply advanced diagnostics for real-time, in-line validation of product characteristics.
• Develop physics-informed data analytics tailored for material processing.
• Demonstrate an integrated framework to support process qualification by our partners.

Current efforts to modernize material processing for the nuclear stockpile and next-generation nuclear energy technologies are hindered by the lack of fundamental, science-based understanding of the material systems. Without this fundamental understanding, the development and qualification of new material systems generally rely on lengthy trial-and-error approaches. A comprehensive understanding of the relationships between production process, material microstructure, and performance properties is key to accelerating development timelines, achieving major cost savings, and opening opportunities for the adoption of game-changing, emerging material systems in national security and nuclear energy.

The MCPC investment is a key element in PNNL’s Laboratory Objective entitled Accelerating the Development and Characterization of Nuclear Material Processing.