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Summary
We are developing automated methods for synthesizing and characterizing metal-organic frameworks to better understand synthesizability and properties. These studies will be accelerated using machine learning methods.
Description
Schematic image of automated MOF synthesis platform under construction.
Metal-organic frameworks (MOFs) show great promise for a variety of applications, from industrial gas separation, to catalysis, to PFAS absorption. One challenge to the adoption of these technologies is determining and optimizing the synthesis of these materials. There are large computational databases that describe potential MOFs but the synthesis of these MOFs is unknown. For experimentally synthesized MOFs, there is generally only one synthetic approach described, but the range and optimum of synthetic parameters may be unknown. Our effort is focused on developing an automated platform for gathering this synthesis data in order to learn to optimize synthesis and determine synthesizability. To achieve this we are building a robotic platform to synthesize MOFs along with several adjacent semi-automated platforms for screening including powder x-ray diffraction, Fourier transform infrared spectroscopy, and x-ray pair distribution measurements.