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Press Release

Johns Hopkins APL Employing AI to Discover Materials for National Security Needs

Bringing together material and data scientists, the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, is leveraging the power of artificial intelligence to rapidly discover materials that can withstand extreme environments.

“As the U.S. faces pressing national security challenges, there are increasing operations in austere environments — and those operations require revolutionary new materials,” said Morgan Trexler, who leads APL’s Science of Extreme and Multifunctional Materials program. “We cannot wait decades to discover materials that meet those needs. By infusing AI approaches throughout the discovery process, we can more quickly and intentionally identify materials for complex, specific applications.”

Because researching new materials is time-consuming and expensive, Trexler added, researchers tend to identify and augment shortcomings in existing materials, rather than explore new element combinations from scratch.

“The approach to building on existing materials will only ever yield limited improvements,” said Keith Caruso, chief scientist in APL’s Research and Exploratory Development Department. “To create groundbreaking materials, we need to make a fundamental leap.”

Turning Up the Heat

Caruso leads an APL project that is harnessing improvements in AI and machine learning to find materials that can withstand extreme heat as components of a hypersonic vehicle.

“There are a huge number of structure and element combinations for potential materials,” said Victor Leon, an AI research scientist on the project. “It would be impossible to effectively explore that massive space using a purely experimental and computational approach. But, by combining AI, high-fidelity density functional theory simulations and material science knowledge, we can narrow the search to a more palatable set of combinations.”

The team’s focus on applications for high-temperature capabilities drove the creation of a novel AI architecture for materials discovery that can target desired properties, structures and chemistries. Once the search is refined, APL researchers create prototypes and use high-throughput screening to rapidly measure material characteristics. The process is repeated over and over, each time feeding new information into the AI model to provide more predictive results.

“Materials discovery is really a three-part problem,” said Christopher Stiles, chief scientist of APL’s Electrical and Mechanical Engineering Group. “First, you need to discover the potential composition and structure. Then you need to discover how to synthesize the material. Finally, you need to measure relevant properties and, if properties meet desired values, understand what compositional, structural or processing aspect, or combination thereof, is responsible for that unique behavior you didn’t have yesterday.”

The project is one of several at APL targeting the discovery and development of materials with unprecedented properties that can withstand extreme environments.

Making Materials for the Moon, on the Moon

Six samples of materials for lunar missions
To discover materials that could enable missions on the Moon, 200 samples were created and tested, which provided over 400 measurements to inform the comprehensive knowledge base of lunar materials.

Credit: Johns Hopkins APL/Christopher Stiles

The need to utilize readily available resources for material creation extends beyond Earth. As the U.S. progresses with its strategy, AI-guided discovery for in situ materials could one day enable lunar and planetary missions.

Stiles leads an APL team collaborating with at the Johns Hopkins Krieger School of Arts and Sciences and at the Johns Hopkins Whiting School of Engineering to develop machine-learning tools that accelerate targeted materials discovery and manufacturing. The project could help researchers discover, design and fabricate new materials in austere environments such as the Moon.

Building on APL’s AI-enabled predict-make-measure approach, which was successfully employed in the targeted discovery of a novel superconductor, the team extended the approach to utilize large language models. These models were used to create a comprehensive knowledge base of elements found on the lunar surface. Because there are no comprehensive databases of lunar constituents or lunar manufacturing processes, the team is also developing these foundational capabilities.

The team leveraged generative AI to create millions of candidate materials and physics-based AI to down-select materials for their synthesizability. Then, neural networks are used to predict mechanical properties of interest, from which a subset is further down-selected. From that reduced set, the team created over 200 samples using directed energy deposition and characterized them to improve and retrain the AI models.

“While we account for restrictions to building structures on the Moon, there are factors that could benefit manufacturing on the lunar surface. The Moon’s microgravity environment surrounded by a vacuum could make processes that are difficult on Earth easier to accomplish,” said Milena Graziano, a senior materials scientist at APL. “There are a lot of different levers to pull that could be exploited for operating in an extreme environment.”

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