Collaborative Research: Data Science-Enabled Learning, Prediction and Simulation of Nanoparticle Population Behavior for Advanced Nanomanufacturing

About This Grant

This project develops a combined numerical-experimental approach to understand and model the synthesis of large populations of advanced materials that are a hundred thousand times smaller than human hair. A data science-based framework is developed to enable learning, predicting, and simulating hard-to-model nanoscale fabrication processes, which underpin a variety of emerging applications in electronics, energy storage, and biomedicine. The project resonates with the global quest towards realizing the potential of artificial intelligence and machine learning in boosting American competitiveness in advanced manufacturing. The scientific community benefits from this research by extending the approach to a broader set of nanoscale material systems including different oxide-supported metal nanoparticles. The team studies the evolution of alumina-supported iron nanoparticles which serve as nanocatalysts for the chemical vapor deposition (CVD) growth of vertically aligned carbon nanotubes (VACNTs) for next generation thermal interfaces and electrical interconnects. The educational impact includes upskilling STEM students and junior scientists on timely topics at the nexus of data and manufacturing sciences. Moreover, the project generates jargon-free outreach materials explaining topics in machine learning and advanced nanomanufacturing to the general audience. The collective behavior and interactions among substrate-bound nanoparticles during the coupled physicochemical processes of oxidation/reduction, dewetting, coarsening, and catalysis are not well understood. This severely constrains the ability to reliably manufacture dense populations (hundreds of billions per square centimeter) of functional nanoparticles or active nanocatalysts. This project combines probabilistic data science methods with in-situ environmental transmission electron microscopy (E-TEM) to elucidate the dynamics of spatial proximity effects among ensembles of adjacent nanoparticles. The research is to leverage spatio-temporal point process theory, a branch of probabilistic machine learning, for quantifying, predicting, and simulating the time evolution of location and size distributions and spatial dependencies during the formation and evolution of metal nanoparticles from thin films. In pursuit of this research, the following tasks are performed: (1) In-situ E-TEM measurements of population behavior of metal oxide reduction, nanoparticle formation by dewetting, coarsening by Ostwald ripening, and catalytic activation; (2) Automated image segmentation of in-situ E-TEM videos to extract salient information about the time evolution of locations, sizes, areal densities, shapes and activation of nanoparticles; (3) Learning from experimental observations: Spatio-temporal statistical modeling of segmentation data using point process theory to characterize, predict, and simulate the evolution of interaction potentials; (4) Learning beyond experimental constraints: Elucidating the physicochemical dynamics of metal/support interfacial phenomena for larger spatial domains, finer temporal resolutions, and unsampled conditions. 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.