Domain-specific languages as a tool for knowledge representation in polymer chemistry

Abstract

Domain-specific languages (DSLs) are used for specific domain areas for where their custom syntax and narrowed scope allow for concise, interpretable expression of the programming tasks. Despite the extensive use of DSLs for a variety of domains, they are relatively underexplored for knowledge representation and translation tasks within experimental science. Here, we will discuss how the flexibility and expressiveness inherent to DSLs can enable effective representation of experimental data with a specific focus on experimental polymer data using a DSL termed Chemical Markdown Language (CMDL). We will discuss how the inherent extensibility of using a DSL such as CMDL enables straightforward support and use of a variety of polymer structural representation systems as well as accommodate a multitude of experimental data types. Experimental data represented using CMDL may be seamlessly utilized to develop of ML-models for materials and catalyst design, which in turn have been validated experimentally. The interoperability of CMDL enabled platforms and data representations with broader open-source data initiatives within polymer chemistry will also be discussed.