Hypothesis-Driven Autonomous Experimentation for Accelerated Research

The current materials research process is slow and expensive; taking decades from invention to commercialization. The Air Force Research Laboratory pioneered ARES™, the first autonomous experimentation system for materials development. A rapidly growing number of researchers are now exploiting advances in artificial intelligence (AI), autonomy & robotics, along with modeling and simulation to create research robots capable of making research progress orders of magnitude faster than today. <br/> <br/>We will focus on our recent campaign of hypothesis-drive search, where carbon nanotube growth is optimized based on free energy of reduction of metal catalysts. Our reduction hypothesis enabled us to collapse a huge search space of CVD synthesis, confirming our hypothesis.<br/> <br/>We will discuss concepts and advances in autonomous experimentation in general, and associated hardware, software and autonomous methods that are part of the Autonomous Materials Innovation Infrastructure (AMII). We consider the impact of autonomous experimentation on human scientists and the scientific enterprise: Changing roles for humans and robots, expectations. In the future, we expect autonomous experimentation to revolutionize the research process, and propose a “Moore’s Law for the Speed of Research,” where the rate of advancement increases exponentially, and the cost of research drops exponentially.