PaddlePaddle/PaddleMaterials

PaddlePaddle/PaddleMaterials

Description: PaddleMaterials is a data-mechanism dual-driven, foundation model development and deployment, end to end toolkit based on PaddlePaddle deep learning framework for materials science.

Language: Python

License: Apache-2.0

Stars: 117

Forks: 40

Open issues: 42

Created: 2024-08-07T12:22:33Z

Pushed: 2026-06-03T11:04:00Z

Default branch: develop

Fork: no

Archived: no

README:

PaddleMaterials

🚀 Introduction

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.

Core Capabilities

| 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 |

Supported Materials

• 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*

Why PaddleMaterials?

• ✅ 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

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📣 News

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📑 Tasks

| Task | Description | Link | |------|-------------|------| | Property Prediction (PP) | Predict formation energy, band gap, elastic properties | [README](property_prediction/README.md) | | Structure Generation (SG) | Generate new crystal structures with diffusion models | [README](structure_generation/README.md) | | Interatomic Potential (IP) | DFT-accurate potentials for molecular dynamics | [README](interatomic_potentials/README.md) | | Electronic Structure (ES) | Predict electronic structure properties | [README](electronic_structure/README.md) | | Spectrum Elucidation (SE) | Reconstruct molecular structures from NMR spectra | [README](spectrum_elucidation/README.md) |

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🔧 Installation

Please refer to the installation [document](Install.md) for your hardware environment. See [SupportedHardwareList](./docs/multi_device.md) for more multi-hardware adaptation information.

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⚡ Get Started

Property Prediction

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'

Structure Generation

Generate novel crystal structures:

python structure_generation/predict.py \
--model_name='mattergen_mp20' \
--num_structures=100 \
--save_path='generated_structures/'

Interatomic Potentials

Run molecular dynamics with ML potentials:

python interatomic_potentials/run_md.py
--model_name='mattersim_1M'
--structure_path='input.cif'
--temperature=300

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Train Your Own Model

For training and fine-tuning, refer to the [documentation](get_started.md).

Contribute to PaddleMaterials

For developer, please refer to [architecture](docs/ARCHITECTURE_ch.md).

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🎯 Available Pretrained Models

| 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](ppmat/models/__init__.py)

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⭐️ Star History


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👩‍👩‍👧‍👦 Cooperation

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👩‍👩‍👧‍👦 Community

Join the PaddleMaterials WeChat group to discuss with us!

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📜 License

PaddleMaterials is licensed under the [Apache License 2.0](LICENSE).

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🎓 Citation

@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}
}

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Acknowledgements

This repository references code from the following projects:

PaddleScience | Matgl | CDVAE | DiffCSP | MatterGen | MatterSim | CHGNet | AIRS