Glass Transition Prediction of Binary Copolymers Across Large Chemical Spaces Using Machine Learning and Physics-Based Modeling.

Manav Bhati¹, Mohammad Atif Faiz Afzal¹, Alex K Chew², Andrea R Browning¹, Mathew D Halls³

¹Schrödinger Inc., Portland, OR 97204, USA.

²Schrödinger Inc., New York, NY 10036, USA.

³Schrödinger Inc., San Diego, CA 92121, USA.

PubMed PMC DOI PDF

Abstract

The glass transition temperature (Tg) is a pivotal design parameter for polymer performance across diverse applications, yet its rapid prediction within expansive chemical spaces remains a challenge. We present a machine learning (ML) framework for the high-throughput prediction of Tg in binary copolymers, trained on experimental datasets encompassing both homopolymers and copolymers. We evaluate…

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