Daniel Siukei Ng
1
, Malik Wagih1, Tianjiao Lei1, Christopher A. Schuh1
1. Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
Daniel Siukei Ng 1 , Malik Wagih1, Tianjiao Lei1, Christopher A. Schuh1
1. Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
A reduction in grain size is expected to convey several benefits to structural materials in nuclear fusion reactors, as grain boundaries can improve mechanical strength, as well as act as a sink for radiation-induced defects to avoid embrittlement. However,
nanocrystalline structures contain a strong driving force for grain growth and tend to be thermally unstable. Selecting solutes with a thermodynamic preference to segregate to grain boundaries can stabilize smaller grains up to reasonable operating
temperatures.
There are limited solute segregation data on candidate alloys for fusion applications such as vanadium, which is of interest for its low neutron activity and high thermal stress factor. A combined machine learning and molecular
mechanics framework developed to calculate segregation energies from ab initio methods was adapted to BCC metals, allowing for the prediction of solute segregation strength in vanadium-base binary alloys across all transition elements. Select alloys
were experimentally synthesized through powder metallurgy ball-milling, annealed, and characterized through transmission electron microscopy to validate the computational predictions of thermally stable solute segregation and nanocrystallinity. Mechanical
testing demonstrated significantly higher strength in sintered powder compacts of vanadium alloys compared to their cast coarse-grained counterparts. Microstructures before and after ion irradiation experiments at elevated temperatures were examined
to determine the extent of damage and radiation stability.