PaddleMaterials is an end-to-end AI4Materials toolkit built on the PaddlePaddle deep learning framework. Designed as a data-mechanism dual-driven platform for developing and deploying foundation models in materials science, PPMat enables researchers to efficiently build AI models and accelerate material discovery using pretrained models.
| Task | Description | Typical Applications |
|---|---|---|
| Property Prediction (PP) | Predict material properties from structure | Formation energy, band gap, elastic moduli |
| Structure Generation (SG) | Generate novel crystal structures | High-throughput screening, inverse design |
| Interatomic Potential (IP) | Replace DFT with ML potentials | Molecular dynamics, large-scale simulations |
| Electronic Structure (ES) | Predict electronic properties | Band structure, density of states |
| Spectrum Elucidation (SE) | Reconstruct structures from spectra | NMR structure elucidation |
- Inorganic Crystals - Well-supported with multiple datasets (MP2018, MP2024, JARVIS) and pretrained models
- Organic Molecules - Support for small molecule datasets (QM9) and property prediction
- Polymers, catalysts, and amorphous materials are under development
- ✅ Rich Pretrained Models - 50+ pretrained models ready for inference
- ✅ Multi-Task Integration - Unified framework across PP, SG, MLIP, MLES, SE
- ✅ Domestic Hardware Support - Full support for MetaX GPUs and NVIDIA GPUs
- ✅ PaddlePaddle Ecosystem - Seamless integration with PaddlePaddle tools
- ✅ Production-Ready - Distributed training, mixed precision, checkpoint recovery
| Task | Description | Link |
|---|---|---|
| Property Prediction (PP) | Predict formation energy, band gap, elastic properties | README |
| Structure Generation (SG) | Generate new crystal structures with diffusion models | README |
| Interatomic Potential (IP) | DFT-accurate potentials for molecular dynamics | README |
| Electronic Structure (ES) | Predict electronic structure properties | README |
| Spectrum Elucidation (SE) | Reconstruct molecular structures from NMR spectra | README |
Please refer to the installation document for your hardware environment. See SupportedHardwareList for more multi-hardware adaptation information.
Predict material formation energy using a pretrained MEGNet model:
python property_prediction/predict.py \
--model_name='megnet_mp2018_train_60k_e_form' \
--weights_name='best.pdparams' \
--cif_file_path='./property_prediction/example_data/cifs/' \
--save_path='result.csv'Generate novel crystal structures:
python structure_generation/predict.py \
--model_name='mattergen_mp20' \
--num_structures=100 \
--save_path='generated_structures/'Run molecular dynamics with ML potentials:
python interatomic_potentials/run_md.py
--model_name='mattersim_1M'
--structure_path='input.cif'
--temperature=300For training and fine-tuning, refer to the documentation.
For developer, please refer to architecture.
| Task | Models | Dataset |
|---|---|---|
| Property Prediction | MEGNet, iComformer, DimeNet++ | MP2018, MP2024, JARVIS |
| Structure Generation | MatterGen, DiffCSP | MP20, ALEX |
| Interatomic Potentials | CHGNet, MatterSim | MPTRJ |
| Electronic Structure | InfGCN | Custom datasets |
Full model list: See MODEL_REGISTRY
Join the PaddleMaterials WeChat group to discuss with us!
PaddleMaterials is licensed under the Apache License 2.0.
@misc{paddlematerials2025,
title={PaddleMaterials, a deep learning toolkit based on PaddlePaddle for material science.},
author={PaddleMaterials Contributors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleMaterials}},
year={2025}
}This repository references code from the following projects:
PaddleScience | Matgl | CDVAE | DiffCSP | MatterGen | MatterSim | CHGNet | AIRS