�Ӱ���Ƶ

Unprecedented Material Properties

An APL team is developing a framework, named Autonomous Researcher for Materials Discovery (ARMD), to directly embed mission requirements as part of the materials discovery process. Currently, the team is working on accelerating the development structural refractories for high-temperature applications.

“Testing properties of new materials is typically a slow process that creates a bottleneck between discovery and application, particularly for extreme environments like elevated temperature,” said Sal Nimer, a senior materials and test engineer and the principal investigator on ARMD.

Nimer’s team is creating samples using an additive manufacturing process known as blown powder directed energy deposition. This approach enables fabrication of hundreds of unique samples, with simultaneous variations in composition and processing conditions, on a single additive manufacturing build plate in custom-designed sample geometries tailored for mechanical testing.

The build plate containing hundreds of unique samples is then autonomously tested using an instrumented robotic arm. To understand properties under extreme conditions, a laser is attached at the end of the arm to heat the samples during testing. Depending on the details of the fabrication and test geometry, this specimen and method design enables up to three times less material volume, three times faster fabrication and five times faster testing than standard approaches.

Test data is then analyzed through a Bayesian optimization model, which suggests promising candidates and optimized parameters to meet the needs of each specific application. Overall, the ability to sample large, complex parameter spaces simultaneously and autonomously enables at least a 20-times throughput increase, significantly accelerating discovery and understanding.

In a separate project, materials scientists Eddie Gienger and Lisa Pogue created dozens of complex microstructures for multi-principal element alloys, which are stronger and better suited for materials operating in extreme environments. Gienger and Pogue developed a pathway that links alloy phases — distinct materials formed when an alloy is heated or cooled — to their mechanical properties. The capability provides a way to quickly create and analyze thousands of local data within bulk samples to intelligently inform the design of new materials and enable rapid material discovery.

Researchers across APL have too, across a range of loading modes and properties, including X-ray diffraction and X-ray computed tomography, to quickly assess tensile strength, porosity-induced stress fields, fatigue resistance and corrosion performance. These methods provide valuable material information that is otherwise time-consuming and arduous to obtain. By advancing materials characterization, data can be brought into the discovery process earlier, more rapidly and in larger quantities, to accelerate the path to materials discovery, demonstration and final implementation.

Finding Alternatives

Another national security challenge is the supply chain and limited availability of critical materials. Rare earth elements in particular have a high processing cost and have been identified as a . Currently, magnetic rare earth elements are vital to future green energy and electric technologies, including electric vehicles, so alternative solutions are needed.

“As electric vehicle use soars, there is a significant need for lower-cost, more abundant magnetic materials that don’t require rare earth elements,” said Nam Le, a computational physicist at APL. “It’s easy to tell if a new candidate material is free from rare earth elements, but it’s difficult to know if it would make a good magnet, so that’s the part we’re focusing on screening.”

Le leads De-Risking Key Materials in Energy Technologies (DRK MATER), a project to develop models to discover materials that can satisfy multiple requirements, such as strong magnetic properties and lower logistical and environmental impact.

For permanent magnets, there are two important properties: saturation magnetization and coercivity, which is a measure of the resistance of material to becoming demagnetized. However, AI models that can predict saturation magnetization don’t work as well to predict coercivity.

“When you start asking an AI model about a material it wasn’t trained on, the results can be very wrong,” said Le. “Initially, we got great results for one property but terrible, inaccurate results for the other.”

To improve the models, Le added, APL is working on methods that can learn from different sources simultaneously: augmenting small numbers of experimental data points with large numbers of data points simulated using a physics-based method called density functional theory, along with constraints related to supply risk.