Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language.

ABSTRACT: Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage their historical data in ML development. Here we show that a domain specific language, termed Chemical Markdown Language (CMDL), provides flexible, extensible, and consistent representation of disparate experiment types and polymer structures. CMDL enables seamless use of historical experimental data to fine-tune regression transformer (RT) models fo

SUBMITTER: Park NH 

PROVIDER: S-EPMC10284867 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature