Physical computing for materials acceleration platforms

A "technology lottery"describes a research idea or technology succeeding over others because it is suited to available software/hardware and not necessarily because it is superior. The nascent field of self-driving laboratories, particularly materials acceleration platforms (MAPs), is at risk: while it is logical and opportunistic to inject existing lab equipment and workflows with artificial intelligence (AI) and automation, such MAPs can constrain research by proliferating existing biases in science, mechatronics, and general-purpose computing. Rather than conformity, MAPs present opportunity to pursue new vectors of engineering physics with advances in cyberphysical learning and closed-loop, self-optimizing systems. We outline a simulation-based MAP program to design computers that use physics to solve optimization problems: the physical computing (PC)-MAP can mitigate hardware-software-substrate-user information losses present in all other MAP classes and eliminate lotteries by perfecting alignment between computing problems and media. We describe early PC advances and research pursuits toward optimal design of new materials and computing media.