Omics-scale polymer computational database transferable to real-world artificial intelligence applications

TL;DR

This paper introduces PolyOmics, a large-scale computational polymer database generated via automated simulations, enabling improved AI models for polymer property prediction and bridging the gap between computational and experimental materials science.

Contribution

The creation of PolyOmics, an extensive, collaborative computational database for polymers, and demonstrating its effectiveness in enhancing machine learning models for real-world applications.

Findings

Database size improves model generalization power-law scaling

Pretrained models can be fine-tuned with limited experimental data

Ultralarge-scale data reveals unexplored polymer regions

Abstract

Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science.