Our recent work on applying QM-MM method to estimate grain boundary segregation energy distributions is featured in Nature Computational Science! Read more about the work here.
Nanocrystalline metals are highly defected structures that accelerate sintering. We study the kinetic competition between low-temperature densification and organic removal and develop a grain size dependent map, which allows to predict the sintering behavior of a broad range of materials.
Grain boundaries contain a broad range of segregation sites. We have estimated distributions of grain boundary segregation entropy, and found a correlation between site-wise segregation energy and entropy. The spectra along with a spectral thermodynamic model shall provide an insight
Our new publication on shows how to overcome the limited availability of interatomic potentials by combining machine learning with first-principles calculations to study grain boundary solute segregation. Read more on Physical Review Letter here!
Mo-Cr system shows promising properties for high temperature applications. Check out how nanophase separation accelerates sintering in Mo-Cr system on Materialia!
Understanding solute segregation as a function of grain size is important to the design of nanocrystalline alloys. Check out our new paper on how grain size and triple junctions influence grain boundary solute segregation at the nanocrystalline limit on Acta