  Crystal structures connected by continuous phase transitions are linked
through mathematical relations between crystallographic groups and their
subgroups. In the present study, we introduce group-subgroup machine learning
(GS-ML) and show that including materials with small unit cells in the training
set decreases out-of-sample prediction errors for materials with large unit
cells. GS-ML incurs the least training cost to reach 2-3% target accuracy
compared to other ML approaches. Since available materials datasets are
heterogeneous providing insufficient examples for realizing the group-subgroup
structure, we present the "FriezeRMQ1D" dataset with 8393 Q1D organometallic
materials uniformly distributed across 7 frieze groups. Furthermore, by
comparing the performances of FCHL and 1-hot representations, we show GS-ML to
capture subgroup information efficiently when the descriptor encodes structural
information. The proposed approach is generic and extendable to symmetry
abstractions such as spin-, valency-, or charge order.
