Understanding how particles nucleate in a multi-component gas mixture has important implications not only for climate and weather, but also wide-ranging technological applications including gas separations, pollution control, and nanotechnology. The goals of this project are (i) to develop computational algorithms and analysis tools for efficient investigations of multi-component gas- to-particle nucleation processes, (ii) to elucidate atmospherically relevant nucleation processes and to validate the rate predictions through strategically selected laboratory experiments measuring cluster size and mass distributions at the sub-3 nm scale, and (iii) to deploy a freely-available cyber-tool that transforms data to knowledge by enabling large-scale modelers and experimental researchers to harvest predicted atmospheric nucleation rates and learn about mechanisms, by providing a general framework to visualize and analyze the abundance of digital data generated by particle-based simulations for any type of gas-to-particles nucleation process, and by being an aid for teaching about nucleation.
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