WeChemSynOntology: Semantic Modeling of Wet Chemical Syntheses in a Self-Driving Lab for Nano- and Advanced Materials

Abstract

Representing experimental procedures in an unambiguous way that can be understood and reproduced by other scientists is at the heart of scientific progress. For centuries, these descriptions were made by humans and for humans, often assuming implicit or tacit knowledge. However, when Materials Acceleration Platforms (MAPs) and Self-Driving Labs (SDLs) are used for the autonomous discovery and optimization of materials, sharing knowledge and workflows that were designed and executed by machines becomes increasingly important. These machines require an explicit, precise and accurate description and modeling of all process parameters and steps that need to be executed. To address these needs, especially in the domain of materials science and nano and advanced materials synthesis, we developed the Wet Chemical Synthesis Ontology, which is based on the Project Material Digital and Basic Formal Ontology. The ontology contains recurring concepts from millions of wet chemical synthesis procedures in the scientific literature. We discuss the design considerations, concepts, and architecture of our ontology in detail, and demonstrate how it can be applied to the construction and querying of semantically annotated knowledge graphs from wet chemical nano and advanced materials synthesis workflows that were previously described and performed on an SDL. Using such formal representations and semantic annotations for describing synthesis procedures and workflows facilitates the reproducibility, sharing, and execution of synthesis procedures across different labs around the world that use different orchestrators for their robotic hardware.