  Pauli spin blockade (PSB) can be employed as a great resource for spin qubit
initialisation and readout even at elevated temperatures but it can be
difficult to identify. We present a machine learning algorithm capable of
automatically identifying PSB using charge transport measurements. The scarcity
of PSB data is circumvented by training the algorithm with simulated data and
by using cross-device validation. We demonstrate our approach on a silicon
field-effect transistor device and report an accuracy of 96% on different test
devices, giving evidence that the approach is robust to device variability. The
approach is expected to be employable across all types of quantum dot devices.
