February 29, 2019
Congratulations to Gayatri (now at Iowa State) and team for developing a new data-driven tool for single crystal structure solution! doi: 10.1021/acs.inorgchem.9b00344
The code is available on GitHub:
Advanced materials characterization reveals the relationship between the host crystal structure's band gap and the position of trap states. This work is going to help design the next generation of "glow-in-the-dark" materials!
October 22, 2018
Machine learning to identify high-efficiency phosphors!
Highlighted by the University of Houston, Science Daily, Phys.org and others!
July 16, 2018
Great collaboration developing machine learning to identify new materials.
Highlighted by ChemistryWorld
July 11, 2018
Now we have the recipe to make materials glow-in-the-dark longer!
April 5, 2018
Using machine learning to predict the band gap of solids; >75,000 band gaps predicted in the supporting information!
March 29, 2018
Another new high-efficiency blue phosphor, borates are awesome!