Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry

Progress in the application of machine
learning (ML) methods to
materials design is hindered by the lack of understanding of the reliability
of ML predictions, in particular, for the application of ML to small
data sets often found in materials science. Using ML prediction for
transparent conductor oxide formation energy and band gap, dilute
solute diffusion, and perovskite formation energy, band gap, and lattice
parameter as examples, we demonstrate that (1) construction of a convex
hull in feature space that encloses accurately predicted systems can
be used to identify regions in feature space for which ML predictions
are highly reliable; (2) analysis of the systems enclosed by the convex
hull can be used to extract physical understanding; and (3) materials
that satisfy all well-known chemical and physical principles that
make a material physically reasonable are likely to be similar and
show strong relationships between the properties of interest and the
standard features used in ML. We also show that similar to the composition–structure–property
relationships, inclusion in the ML training data set of materials
from classes with different chemical properties will not be beneficial
for the accuracy of ML prediction and that reliable results likely
will be obtained by ML model for narrow classes of similar materials
even in the case where the ML model will show large errors on the
data set consisting of several classes of materials.