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

Nathaniel H Park¹, Matteo Manica², Jannis Born^2,3, James L Hedrick⁴, Tim Erdmann⁴, Dmitry Yu Zubarev⁴, Nil Adell-Mill^2,5, Pedro L Arrechea⁴

¹IBM Research-Almaden, 650 Harry Rd., San Jose, CA, 95120, USA. npark@us.ibm.com.

²IBM Research-Zurich, Säumerstrasse 4, Rüschlikon, 8803, Switzerland.

³Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058, Basel, Switzerland.

⁴IBM Research-Almaden, 650 Harry Rd., San Jose, CA, 95120, USA.

⁵Arctoris, 120E Olympic Avenue, Abingdon, OX14 4SA, Oxfordshire, UK.

PubMed PMC DOI PDF

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…

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