About Sam Mao
I am a materials data scientist developing data visualization and deep learning tools on micrographs and videos for characterization at multi-length/time scales. My experimental projects are majorly funded by the National Science Foundation (NSF), Department of Energy (DOE) and General Electrics (GE) including access to user facilities such as ORNL Leadership Class Computing Facility (OLCF), Center for Nanophase Materials Sciences (CNMS), High Flux Isotope Reactor (HFIR) and Nuclear Science User Facility (NSUF). My specific expertise focused on microstructure characterization and small-scale mechanical testing on superconduting and soft materials including in-situ SEM/TEM on alloys, ceramics and composites etc. under extreme conditions.
Intro Content
Communications Materials
Probing the nanostructure of fission products in oxide fuels using machine learning
Ceramic oxides in extreme environments degragate with complicated microstructural and microchemical changes. This work provides a machine-learning approach that fastens and expands the processing of large analytical microscopy data volumes approaching the atomistic-scale precision.
