Welcome to PolyMon’s documentation!

PolyMon is a unified framework for polymer property prediction. It combines traditional machine learning methods (Random Forest, XGBoost, LightGBM, CatBoost, TabPFN) with state-of-the-art deep learning models (Graph Neural Networks including GATv2, GIN, PNA, DimeNet++, and KAN-based architectures).

Key Features:

  • Multiple model types: Tabular ML and Graph Neural Networks

  • Flexible training strategies: Cross-validation, hyperparameter optimization, ensemble learning

  • Advanced techniques: Multi-fidelity learning, delta-learning, active learning

  • Comprehensive descriptors: RDKit, Mordred, ECFP fingerprints, graph-based representations

  • Support for 5 key polymer properties: Tg, FFV, Rg, Density, Tc

Getting Started

Package Reference

CLI

Quick Start

Installation:

# Install PyTorch first
conda install -y pytorch==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install torch_geometric

# Install PolyMon
pip install polymon

Train a model:

# Tabular model
polymon train --labels Tg --model rf --feature-names rdkit2d --n-fold 5

# Graph neural network
polymon train --labels Rg --model gatv2 --n-fold 5 --num-epochs 2500

Make predictions:

polymon predict --model-path model.pt --csv-path data.csv --smiles-column SMILES

For detailed examples, see the Usage Examples page.

Indices and tables