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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Author(s) Christopher Benjamin Kuenneth, Rampi Ramprasad
On-Site Speaker (Planned) Christopher Benjamin Kuenneth
Abstract Scope Polymer informatics tools have been recently gaining ground to design and discover polymers that meet specific application needs. A critical component of such tools is the conversion of polymers to machine readable representations (so-called fingerprints). The fingerprinting process has so far been based on handcrafted approaches that capture key chemical and structural features. Recently, within the domain of natural language processing, transformer-based ML models have demonstrated a new, fully ML based path to obtain fingerprints of language. Here, we view SMILES strings as a language representation of polymers, and use them to train a transformer based ML model using more than 100 million SMILES strings. The performance of the so-derived fingerprints are compared with traditional fingerprints using a large polymer property data set. Our new approach has a similar prediction performance compared to the existing state-of-the-art methods, but is faster, more flexible, and allows us to create fully-autonomous ML pipelines.


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