Using machine learning interatomic potentials for finding CoNiTi ternaries

Superalloys are materials with excellent mechanical properties at extreme temperatures. Superalloys including Co or Ni atoms [1] prompt for a thorough search to improve industry-demanding properties, which requires new computational methods to sweep the huge amounts of possibilities. We search through 200,000 CoNiTi crystal structures to find superalloy phases using machine learning based on interatomic moment tensor potentials (MTP) [2, 3]. We have not only reproduced results reported in the AFLOW database but also predicted stable binary and ternary phases that are not present in the literature. The MTP approach shortens the computational analysis of CoNiTi systems by a factor of 100 compared to a pure DFT methodology. Further analysis will include searching for stable structures at higher temperatures for possible industrial applications.
References
[1] C. Nyshadham et al. Acta Mater. 122, 438 (2017).
[2] A. Shapeev. Multiscale Model. Simul., 14, 1153 (2016).
[3] K. Gubaev et al. ArXiv:1806.10567 [cond-mat.mtrl-sci].