Month: March 2025

Our new paper on the prediction of cation stoichiometry via machine learning analysis of RHEED patterns is out in Nano Letters. This work was co-led by Sumner Harris at Oak Ridge National Lab and our own Patrick Gemperline as part of his Ph.D. thesis at Auburn. Credit also goes to…

Our work on RHEED analytics and machine learning continues with a collaborative paper on segmentation of videos to detect changes in growth modes. Led by Tiffany Kaspar and the AT-SCALE team at Pacific Northwest National Lab, the work shows how machine learning can be employed to provide real-time feedback to…

We’ve been working in earnest for several years on machine learning and data analytics for maximizing the information we glean from reflection high-energy electron diffraction (RHEED). In the MBE and PLD world, RHEED is used to monitor the growth of epitaxial films in real time, generating information on growth rates…