Molecular Beam Epitaxy Automation and Machine Learning to Accelerate the Design of Materials

Innovative Advanced Materials (IAM), Inc., founded by Dr. W. Alan Doolittle, proposes to demonstrate machine learning methods for MBE growth that will lead to the development of an autonomous control system. IAM will use its Ubiquitous Control™ software, built based on MATLAB technologies, for eventual control of the MBE process with the primary in-situ sensor being RHEED image analysis. Both 2D diffraction data and 2D time variable data will be presented to machine learning algorithms in an effort to build a model of the inherent relationships with MBE process conditions. While the machine learning algorithm does not “know the underlying physics� such 2D diffraction data is already known to contain physical information about lattice spacing and thus, composition, surface roughness, faceting (connected to surface energy), relative degrees of crystallinity (i.e., amorphous, polycrystalline, crystalline), ratios of chemical species (e.g., 2x4 and 4x2 reconstructions), and relative temperature ranges via observed RHEED patterns and growth mode (Frank–van der Merwe or FM, Volmer–Weber or VW, or Stranski–Krastanov or SK modes). The focus of this program is to demonstrate a new approach that uses machine learning to predict the material properties of MBE grown films.