We present a comprehensive study on the influence of
Ti doping
on K+ migration in the K1–x
Fe1–x
Ti
x
O2 solid electrolyte. A novel approach is proposed
which is based on modeling of configurational spaces (CSs) and full
sets of inequivalent migration pathways by means of density functional
theory (DFT) calculations and machine learning (ML) techniques. A
2 × 1 × 1 supercell (32 formula units) of a low-temperature
polymorph of the KFeO2 compound with space group symmetry Pbca was used. For the three lowest Ti contents (x = 0.03, 0.06, and 0.09), all symmetrically inequivalent
configurations of atomic arrangements (CSs) and K+ migration
pathways (total numbers: 128, 59520, and 8630400) were generated.
With the DFT-derived energetics of K+ migration at the
lowest doping level (x = 0.03), the ML models were
trained to predict ionic transport properties by using geometrical
descriptors for the pathway-dopant arrangement. The trained ML models
were then used to evaluate the K+ migration properties
for pathways at higher doping levels. The computational results obtained
are in good agreement with the results of a previous experimental
study of the title compound. This demonstrates the applicability of
the proposed approach for modeling and predicting effects of doping
in crystalline solids, such as solid electrolytes and intercalation
cathodes. Brief recommendations are given on the application of the
proposed combined approach.