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Robotic Screening Approach to Data-Driven Discovery of Novel Perovskite Nanocrystals
10: Chemical Engineering
Quantum-confined lead halide perovskite nanocrystals have recently emerged as a highly promising class of semiconductor materials for next-generation optoelectronics. Depending on the synthetic methods and conditions, it has been reported that colloidal perovskite nanocrystals of various geometries ranging from 0D quantum dots to 2D nanoplatelets could be synthesized. However, key factors that determine material properties and geometry have not been well-identified. An important advantage of perovskite nanocrystals over other conventional semiconductor nanocrystals is that they can be easily synthesized at room temperature using a technique called ligand-assisted reprecipitation. This technique enables facile synthesis of the materials at room temperature and rapid screening of the vast synthetic parameter space, which make them an ideal class of materials for the study using a robotic high-throughput synthesis/screening system. We are planning to explore the synthetic parameter space and generate large amount of material property data using automated robotic synthesis system. And in order to understand the key factors that determine nanocrystal geometry and properties, we envision that advanced statistical methods and machine-learning techniques will be employed. UROP student will mostly be working on writing python program and controlling the robotic system for the automated nanocrystal synthesis/screening, as well as monitoring its operation. We are envisioning this as a relatively long-term project (probably throughout 2020) and UROP student will eventually get an opportunity to design experiments, work/analyze independently, and contribute to the publication of scientific articles. Contact: Seung Kyun (SK) Ha email@example.com
1. Long-term commitment is preferred. 2. Previous experiences on Python is preferred. 3. Basic knowledge of semiconductor nanocrystals is appreciated but not required.