polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language.
The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature.
The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome. org.
Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs.
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.
The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives.
Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology.
Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available.