Utilizing domain-specific languages as a tools for enabling AI-guided materials design

Abstract

While many exemplary libraries and packages for enabling development of actionable predictive models for polymer chemistry. Their effective utilization by subject-matter experts (SMEs) during the data generation and representation tasks can be challenging. Here, we explore how domain-specific languages (DSLs) can serve as an intermediate tool to facilitate effective translation and representation of experimental data for consumption within AI and informatics pipelines. Additionally, by leveraging language assistive tools present in modern integrated development environments (IDEs), we can significantly reduce the burden of learning and using a DSL in daily research workflows for knowledge capture. Ultimately, DSLs and their use within IDEs can serve as a solution to handle knowledge representation and translation tasks required to enable development or fine-tuning of effective AI models for materials design.