  Establishing a data-driven pipeline for the discovery of novel materials
requires the engineering of material features that can be feasibly calculated
and can be applied to predict a material's target properties. Here we propose a
new class of descriptors for describing crystal structures, which we term
Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and
is shown to accurately predict a range of material properties. These simple and
intuitive class of descriptors are generated from the energetics of a material
at a low level of theory using an incomplete ab initio calculation. We
demonstrate how the incorporation of ROSA descriptors in ML-based property
prediction leads to accurate predictions over a wide range of crystals,
amorphized crystals, metal-organic frameworks and molecules. We believe that
the low computational cost and ease of use of these descriptors will
significantly improve ML-based predictions.
