  We have developed a method that can analyze large random grain boundary (GB)
models with the accuracy of density functional theory (DFT) calculations using
active learning. It is assumed that the atomic energy is represented by the
linear regression of the atomic structural descriptor. The atomic energy is
obtained through DFT calculations using a small cell extracted from a huge GB
model, called replica DFT atomic energy. The uncertainty reduction (UR)
approach in active learning is used to efficiently collect the training data
for the atomic energy. In this approach, atomic energy is not required to
search for candidate points; therefore, sequential DFT calculations are not
required. This approach is suitable for massively parallel computers that can
execute a large number of jobs simultaneously. In this study, we demonstrate
the prediction of the atomic energy of a Fe random GB model containing one
million atoms using the UR approach and show that the prediction error
decreases more rapidly compared with random sampling. We conclude that the UR
approach with replica DFT atomic energy is useful for modeling huge GBs and
will be essential for modeling other structural defects.
