Materials design typically targets an application that requires the synthesis of a material which is characterised by measurable and reliable properties and functions that are maintained during its use. Inexpensive and abundant raw materials, reproducibility, and scalability are decisive factors for success. The relationships between the structure and the function of a material are usually complex and intricate and they prevent a strictly in-silico design for realistic conditions. Thus, the experimental input is crucial. Artificial-intelligence (AI) methods have the potential to reduce the significant efforts related to the synthesis and characterization of materials, accelerating materials discovery. However, rigorously conducted experiments that provide consistent training data for AI are indispensable. They directly determine the reliability of generated insights.