Reproducibility in automated chemistry laboratories using computer science abstractions

While abstraction is critical for the transferability of automated laboratory science in (bio)chemical and materials sciences, its improper implementation is a technical debt taken against the reproducibility of experimental results. Over the decades, computer science has developed guidelines and strategies for how abstractions are captured in programming languages—particularly concerning the substitutability of implementations of abstracted ideas and the clear definition of the contexts in which abstractions are used. However, few programming languages developed for automated experiments fully leverage the wisdom learned in computer science. To achieve collaborative sharing of scientific knowledge via automated laboratories, the way that experimental protocols are codified and interpreted by machine agents must use abstractions responsibly and with reproducibility, rather than solely transferability, at its core. This Review discusses how computer science principles of abstraction can be translated to create more reproducible automation as an enabler for the acceleration of collaborative research with self-driving laboratories. Digital workflow representations in automated and autonomous chemistry laboratories can achieve transferability by using abstract concepts. However, such abstractions must abide by certain rules to ensure reproducibility. Lessons learned from computer science for responsible abstraction are translated into an automated chemistry laboratory context to guide digital workflow development towards reproducibility.