Cation Exchange Pathways for Constructing Complex Nanoparticle Libraries

Non-Technical Summary Advances in artificial intelligence (AI) are expected to accelerate the discovery of new materials by combining computer-aided predictions with robot-driven tools to make them. Millions of materials have been predicted, but almost all of them can currently not be make. In this project, which is supported by the Solid State and Materials Chemistry program in NSF’s Materials Research Section A, Professor Raymond Schaak and his group at Penn State University use simple chemical reactions to design and make new materials with nanometer-scale dimensions. These chemical reactions replace certain elements in nanoparticle materials with other elements without changing the way in which the elements are arranged overall. This approach allows the researchers to design new nanoparticles in ways that control some of the most important aspects of these materials. For example, by using materials that allow the elements being replaced to move very quickly through them, the researchers are making new classes of nanoparticles that may be useful for transporting charge and interacting with light. The researchers are also controlling how far apart the elements are in the nanoparticle materials, which is a useful tool for making materials, for example superstructures, that have been predicted but never made before. By combining multiple steps into a single chemical reaction, the research team is also discovering entirely new types of nanoparticle materials with complex features that are beyond what can currently be predicted. These chemical reactions are designed to be AI-friendly and therefore compatible with robot-driven tools for accelerating materials discovery. The principal investigator and his group are partnering with faculty and students at local colleges and also developing resources to teach students and researchers about topics and skills that are important for future careers in AI-assisted materials discovery. Technical summary Computational prediction capabilities coupled with AI-driven autonomous laboratories are poised to rapidly accelerate the discovery of new materials. Millions of potential materials have been studied computationally, including many metastable compounds and complex nanoparticles, but almost all are unsynthesizable based on current knowledge and capabilities, and their complexity complicates automation. This project, supported by the Solid State and Materials Chemistry program in NSF’s Materials Research Section A, uses generalizable nanoparticle cation exchange reactions to simplify and expand the rational synthesis of nanostructurally complex nanoparticles and superstructures. The researchers are increasing the scope of materials that can be made with cation exchange reactions by designing new nanoparticle precursors having high cation mobilities, leveraging coherent strain to stabilize metastable and superlattice structures, and dynamically restructuring simple nanoparticle precursors into complex products. This research is providing new knowledge for making metastable compounds to help bridge the gap between computationally predicted and synthesizable materials while also simplifying procedures for eventual alignment with AI-driven automated workflows. The principal investigator and his group are partnering with faculty and students at local colleges to pilot workshops on materials characterization, crystal structure visualization, and manuscript writing. This team is also developing educational resources for standardizing AI-driven automated nanoparticle synthesis protocols. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.