Automated quality control and data interpretation are the missing puzzle pieces towards prolonged walk-away-times in closed loop experimentation . Early demonstrations of closed loop experiments included mostly error resilient measurement and facile data analysis, but the step towards complex and interrelated experimentation necessitates more robust data quality assessment. The evolution of these automated experiments towards truly autonomous Material Acceleration Platforms (MAPs) require integration of experimental processes, data management, and strategic decision-making 2 self-driving laboratories (SDLs) . Laboratories such as BEAR , ARES , Clio , equipped with advanced frameworks are instrumental in boosting operational efficiency and research safety . These technological leaps not only streamline experimental processes, but also enable scientists to undertake deeper and more intricate inquiries, accelerating discovery in various scientific domains . Additionally, the integration of AI and machine learning (ML) allows for efficient exploration of complex chemical and material terrains . SDLs, in particular, show the potential to accelerate research output by up to 30 times . By automating and digitalizing processes, these systems increase experimental accuracy and walk away time .