Machine learning is used to study growth of a metal-organic framework (MOF) in a high-dimensional synthetic space. Neural networks for image processing also provide tools for automatically measuring thickness and lateral size of MOF nanoplates to provide quantitative data for further analysis. Relationships among different quantities in these synthetic endeavors were searched and evaluated with state-of-the-art mathematical tools. This works highlights new opportunities in using machine learning to expedite materials development and provides insight into their synthesis process.

No abstract

Broadband near infrared (NIR) emission materials are of interest for various applications including non-destructive biomedical imaging. In this work, ytterbium ion (Yb3+) were successfully doped into Cs2AgInCl6: Cr3+ (i.e. CAIC:...

Excited state energies on a two-dimensional light-harvesting metal–organic layer (MOL) are efficiently transported to Re- and Ir-based reaction centers for converting CO2 to CO or HCOOH.