## ๐ 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? - โ **Rich Pretrained Models** - 50+ pretrained models ready for inference - โ **Multi-Task Integration** - Unified framework across PP, SG, MLIP, MLES, SE - โ **Domestic Hardware Support** - Full support for MetaX GPUs and NVIDIA GPUs - โ **PaddlePaddle Ecosystem** - Seamless integration with PaddlePaddle tools - โ **Production-Ready** - Distributed training, mixed precision, checkpoint recovery --- ## ๐ฃ News --- ## ๐ 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) | --- ## ๐ง 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. --- ## โก Get Started ### Property Prediction Predict material formation energy using a pretrained MEGNet model: ```bash 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: ```bash python structure_generation/predict.py \ --model_name='mattergen_mp20' \ --num_structures=100 \ --save_path='generated_structures/' ``` ### Interatomic Potentials Run molecular dynamics with ML potentials: ```bash python interatomic_potentials/run_md.py --model_name='mattersim_1M' --structure_path='input.cif' --temperature=300 ``` --- ### 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). --- ## ๐ฏ 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) --- ## โญ๏ธ Star History [](https://www.star-history.com/#PaddlePaddle/PaddleMaterilas&type=date&legend=top-left) --- ## ๐ฉโ๐ฉโ๐งโ๐ฆ Cooperation
--- ## ๐ฉโ๐ฉโ๐งโ๐ฆ Community Join the PaddleMaterials WeChat group to discuss with us!
--- ## ๐ License PaddleMaterials is licensed under the [Apache License 2.0](LICENSE). --- ## ๐ Citation ```bibtex @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} } ``` --- ## Acknowledgements This repository references code from the following projects: [PaddleScience](https://github.com/PaddlePaddle/PaddleScience) | [Matgl](https://github.com/materialsvirtuallab/matgl) | [CDVAE](https://github.com/txie-93/cdvae) | [DiffCSP](https://github.com/jiaor17/DiffCSP) | [MatterGen](https://github.com/microsoft/mattergen) | [MatterSim](https://github.com/microsoft/mattersim) | [CHGNet](https://github.com/CederGroupHub/chgnet) | [AIRS](https://github.com/divelab/AIRS)