A baker who never tastes the dough and only looks at the color of the crust is unlikely to bake perfect bread. Materials science has long been similar: automated setups could churn through hundreds of variants but didn't understand why one recipe was better than another. Now engineers have created a system that doesn't just search: it formulates rules, like an experienced technologist who can explain every step to an apprentice.

The subject was a film of gallium oxide, a material for power electronics that can operate at high voltages. The problem is that defects arise during deposition, spoiling the properties. To assess purity, the authors used the Urbach energy: the lower it is, the less disorder in the structure. The robot sputterer varied four parameters (substrate temperature, plasma power, and two gas flows) while an optical sensor measured quality on the fly. Within a few dozen iterations, the system found a regime with an Urbach energy of 182 meV — a record low for such films.

But the main point is not the record: it's that the system could explain its findings. A special algorithm analyzed how each parameter affects the outcome. It turned out that substrate temperature is the main lever: it changes quality the most. Power and gas flows act almost independently, while temperature and oxygen have a bit of a "disagreement"—their combined effect defines the narrow window where the ideal film is born. These rules were successfully transferred to another type of substrate without additional tuning, and a single crystal grew there as well.

Thus, the laboratory transforms from a black box into a mentor: it not only hands over a ready recipe but also teaches the human which ingredients matter and how they interact. The baker finally tastes the dough.