An interdisciplinary team at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, has made significant strides in materials science by applying artificial intelligence to automate microcapsule experimentation. This innovative approach has reduced the time required for individual researchers on each experiment by more than 80%, thereby allowing scientists to conduct a greater number of experiments.
Previously, researchers have utilized AI to enhance the discovery of novel superconductors and advanced robotics controls. The current project, titled Microcapsule AI-driven Testing, Learning, and Accelerated Synthesis (ATLAS), further explores the integration of AI in materials research. Leveraging generative AI as a “co-investigator,” ATLAS accelerates the synthesis and testing pipeline for microcapsules, cutting the hands-on time per experiment from nine hours to under 90 minutes. This efficiency enables researchers to allocate more time to additional experiments and analyses.
Microcapsules are tiny particles designed to carry an active substance, or payload, inside a protective shell that regulates the timing of the payload’s release. These particles have diverse applications across various sectors, including military, agriculture, health, and materials science. For instance, microcapsules can protect sensitive payloads from harsh environments, modulate the delivery of pain-relief drugs, or control the activation timing of self-healing paints.
“What makes microcapsules so powerful is their versatility; the same core technology can enhance the performance and durability of critical systems or serve as a foundation for adaptive materials,” said Leslie Hamilton, program manager for Science of Extreme and Multifunctional Materials in APL’s Research and Exploratory Development Mission Area. “However, that versatility relies on precisely controlled chemistry. Engineering a capsule that releases the right payload at the right time and under the right conditions requires molecular-scale precision, which is why speeding up their design and fabrication is transformative.”
Led by materials chemist Allison Moyer and AI researcher Jenelle Millison, the ATLAS team selected microcapsules as an ideal candidate for AI-driven research. Traditional development methods for microcapsules are slow and labor-intensive, characterized by multiple physical and chemical variables with complex, nonlinear relationships. The lack of existing literature on the use of an agentic co-investigator in microcapsule research presents a unique opportunity for APL to advance in this domain.
“Microcapsules pose a significant challenge due to the multitude of variables involved and their intricate interactions,” Moyer noted. “This has direct implications for various fields, whether it’s imparting self-healing functionalities to smart coatings or enabling underwater adhesives for maritime applications.”
ATLAS draws upon APL’s expertise in modeling and simulation, robotics and autonomy, generative AI, and materials science to streamline microcapsule development and experimentation. The team has created a reaction model that describes the kinetics of chemical reactions during microcapsule formation, allowing researchers to simulate these reactions step-by-step. These simulations enable the automation of selecting conditions for future experiments, which are then validated in a laboratory setting. So far, the automation system encompasses stirring, heating, pH adjustment, and reagent addition. A specialized automation server has been developed to manage commands and distribute them to various pieces of equipment, facilitating an integrated approach to conducting the physical components of experiments within a modular lab environment.
In addition to the automation features, the team has developed a literature agent that automatically identifies relevant publications to guide research efforts throughout the experimental process. “APL is unique in that we have roboticists, modeling experts, AI researchers, and chemists all working in the same facility,” Millison remarked. “This collaboration enables us to redefine the scientific process to create next-generation materials for our sponsors.”
The successful integration of AI into microcapsule research not only accelerates development but also has the potential to revolutionize applications across various fields, enhancing capabilities in ways previously thought to be unattainable. As the team continues to refine and expand its methodologies, the implications for advanced materials science remain significant.
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