OPoly26 Dataset for Polymer Property Prediction
Analysis
This paper introduces a significant new dataset, OPoly26, containing a large number of DFT calculations on polymeric systems. This addresses a gap in existing datasets, which have largely excluded polymers due to computational challenges. The dataset's release is crucial for advancing machine learning models in polymer science, potentially leading to more efficient and accurate predictions of polymer properties and accelerating materials discovery.
Key Takeaways
- •OPoly26 is a large-scale dataset of DFT calculations for polymers.
- •The dataset addresses a gap in existing materials datasets.
- •Augmenting ML models with OPoly26 improves performance for polymer prediction tasks.
- •The dataset is publicly released to facilitate further research.
Reference
“The OPoly26 dataset contains more than 6.57 million density functional theory (DFT) calculations on up to 360 atom clusters derived from polymeric systems.”