As for any physical modeling, one does not expect a model to be predictive outside its physical scope. Yet, in the traditional development of physical theories (sometimes referred to as "model-based", as opposed to "data-centric", approach) describing the limit of validity of a theory is an essential part of it. Such limits of validity are typically expressed as inequalities as function of key parameters governing the physical property or process. We identify the data-centric identification of the limits of validity of an ML model as, arguably, the biggest challenge in AI applied to materials science.