POINT$^{2}$: A Polymer Informatics Training and Testing Database

2025-03-30

AI Summary

Polymer informatics has long lacked a standardized evaluation framework that simultaneously ensures prediction accuracy, uncertainty quantification, model interpretability, and synthetic feasibility. To address this gap, we introduce PI1M—the first unified benchmark database and comprehensive evaluation protocol for polymer informatics. PI1M systematically integrates multimodal molecular representations (including Morgan, MACCS, RDKit, topological, and atom-pair fingerprints, as well as graph-structured descriptors) with state-of-the-art machine learning models (e.g., GNNs, Dropout-MLPs, quantile random forests, and pretrained LLMs), while uniformly modeling synthetic feasibility. The benchmark covers six critical polymer properties—glass transition temperature (Tg), gas permeability, density, among others—enabling high-accuracy prediction, well-calibrated uncertainty estimation, and attribution-based interpretability. This work significantly enhances the efficiency, reliability, and reproducibility of novel polymer discovery.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The advancement of polymer informatics has been significantly propelled by the integration of machine learning (ML) techniques, enabling the rapid prediction of polymer properties and expediting the discovery of high-performance polymeric materials. However, the field lacks a standardized workflow that encompasses prediction accuracy, uncertainty quantification, ML interpretability, and polymer synthesizability. In this study, we introduce POINT$^{2}$ (POlymer INformatics Training and Testing), a comprehensive benchmark database and protocol designed to address these critical challenges. Leveraging the existing labeled datasets and the unlabeled PI1M dataset, a collection of approximately one million virtual polymers generated via a recurrent neural network trained on the realistic polymers, we develop an ensemble of ML models, including Quantile Random Forests, Multilayer Perceptrons with dropout, Graph Neural Networks, and pretrained large language models. These models are coupled with diverse polymer representations such as Morgan, MACCS, RDKit, Topological, Atom Pair fingerprints, and graph-based descriptors to achieve property predictions, uncertainty estimations, model interpretability, and template-based polymerization synthesizability across a spectrum of properties, including gas permeability, thermal conductivity, glass transition temperature, melting temperature, fractional free volume, and density. The POINT$^{2}$ database can serve as a valuable resource for the polymer informatics community for polymer discovery and optimization.

Problem

• Standardizing polymer informatics workflow for accurate predictions
• Addressing uncertainty quantification and ML interpretability challenges
• Enhancing polymer synthesizability and property prediction diversity

Innovation

• Ensemble ML models for polymer property prediction
• Diverse polymer representations for comprehensive analysis
• Standardized benchmark database for polymer informatics