On the contrary, the data-driven development of interpretable physical models resisted automation for long . The possibility of building data-driven interpretable and generalizable models for complex and not well understood physical systems is important as these models share the similar structure to those based on first principles and can be transferred to analogous systems, whereas surrogate models cannot be easily generalized . This, in the longer term, can improve the time and resource efficiency for the product discovery and process optimization, especially for the manufacturability and the scale-up, for which a mechanistic understanding is often crucial .