Data-driven exploration of dissolution and precipitation of high entropy metal oxide nanoparticles

About This Grant

NON-TECHNICAL SUMMARY This award supports fundamental research aimed at improving our understanding and predictive capabilities regarding the formation and degradation of high-entropy metal oxides (HEMOs) in aqueous environments. HEMOs typically consist of four or more metal elements in near-equimolar ratios and are at the forefront of innovation in energy technologies, including batteries, fuel cells, and catalysts. However, their synthesis and aqueous stability remains poorly understood. To address these challenges, the research team will utilize high-throughput computational modeling and machine learning to identify HEMO compositions that are both readily synthesizable and highly resistant to aqueous corrosion. The supported research will be tightly integrated with educational efforts through engagement of a broad audience (including high school and undergraduate students) through augmented virtual reality learning tools, online videos, and hands-on research experiences. The project will also provide open-access databases and open-source software tools to the broader materials science community. TECHNICAL SUMMARY The supported research will address a critical knowledge gap in understanding the aqueous-phase synthesis and corrosion behavior of high entropy metal oxides (HEMOs), a class of multicomponent materials with broad potential for energy storage and conversion applications. The immense compositional design space of HEMOs, combined with limited experimental and theoretical understanding of their precipitation and dissolution processes, presents a significant barrier to their scalable development. The research team will aim to: (i) Develop a universal machine learning model to accurately estimate the free energies of HEMOs at aqueous conditions; (ii) Quantify nucleation and dissolution rates of HEMOs in aqueous solution combining machine learning with nucleation theory; and (iii) Implement a kinetic Monte Carlo (KMC) simulation framework to model real-time precipitation and corrosion dynamics. These models established will also be calibrated and validated through collaborative experimental efforts as well as text mining of the experimental literature. The project will produce an open-source database, predictive tools, and simulation codes for high-entropy materials. Broader impacts will include efforts to engage students in diverse fields including data science, solid-state chemistry, energy storage and conversion, and nanoscience. To reach a broad range of participants, the project will integrate augmented and virtual reality applications into university curricula, engage K-12 students by collaborating with Young Scholars Program at FSU, and expand access to research content via a YouTube-based educational channel. These efforts aim to foster interest in materials chemistry and provide educational opportunities across a wide range of learning communities. STATEMENT OF MERIT REVIEW 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.