# PaddleMaterial **Repository Path**: mirrors_PaddlePaddle/PaddleMaterial ## Basic Information - **Project Name**: PaddleMaterial - **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 and engineering. - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: develop - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-07 - **Last Updated**: 2026-07-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # PaddleMaterials

## πŸš€ 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 | Forward design or predict formation energy, band gap, elastic moduli etc. | | **Structure Generation (SG)** | Generate novel crystal structures | Inverse design or structure generation | | **Machine Learning Interatomic Potential (MLIP)** | Surrogate Model for DFT as ML potentials | Molecular dynamics simulations | | **Electronic Structure (ES)** | Surrogate Model for DFT to predict physical field | Predict electronic density | | **Spectrum Elucidation (SE)** | Reconstruct structures from spectra | NMR structure elucidation | | **Spectrum Enhancement (SPEN)** | Enhance microscopy and spectrum signals | STEM image enhancement, denoising | ### 🧱 Supported Materials - **Inorganic Crystals** - Well-supported with multiple datasets and pretrained models - **Organic Molecules** - Support for small molecule datasets and property prediction - *Polymers, catalysts, and amorphous materials are under development* ### ✨ Why PaddleMaterials? - βœ… **Rich Pretrained Models & AI-ready Datasets** - 50+ pretrained models ready for inference and Multiple curated datasets for training - βœ… **Multi-Task Integration** - Unified framework across tasks of PP, SG, MLIP, ES, SE, SPEN etc. - βœ… **Multi-Hardware Support** - Full support for NVIDIA GPUs and MetaX GPUs and Intel CPUs - βœ… **Production-Ready** - Easy to use with standandlize design & distributed training, mixed precision, checkpoint recovery ### πŸ“‘ Support 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) | | **Machine Learning Interatomic Potential (MLIP)** | 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) | | **Spectrum Enhancement (SPEN)** | Enhance microscopy and spectral signals | [README](spectrum_enhancement/README.md) | ### πŸ€– Available Pretrained Models | Task | Models | Dataset | |------|--------|---------| | **Property Prediction** | MEGNet, iComformer, DimeNet++ | MP2018, MP2024, JARVIS | | **Structure Generation** | MatterGen, DiffCSP | MP20, ALEX | | **Machine Learning Interatomic Potential** | CHGNet, MatterSim | MPTRJ | | **Electronic Structure** | InfGCN | QM9_ES, MP_ES, OMol25_MC_ES | | **Spectrum Elucidation** | DiffNMR | MSD_NMR | | **Spectrum Enhancement** | SFIN | SFIN-HAADF/BF | Full model list: See [MODEL_REGISTRY](ppmat/models/__init__.py#L75) --- ## πŸš€ Get Started ### πŸ”§ 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. --- ### ⚑ Easy Inference #### 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 ``` #### Electronic Structure Run prediction of elcutorninc density: ```bash python interatomic_potentials/run_md.py --model_name='mattersim_1M' --structure_path='input.cif' --temperature=300 ``` #### Spectrum Elucidation Run NMR spectrum elucidate: ```bash python spectrum_elucidation/sample.py --config_path='spectrum_elucidation/configs/diffnmr/DiffNMR.yaml' --weights_name='DiffNMR_nless15_best.pdparams' --save_path='result_diffnmr_nless15/' --checkpoint_path="pretrained" ``` #### Spectrum Enhancement Run prediction of elcutorninc density: ```bash python spectrum_enhancement/predict.py --model_name sfin_haadf_enhance --split val ``` --- ### πŸ‹οΈ Start Training For training and fine-tuning, refer to the [documentation](get_started.md). --- ## 🀝 Contributors & Cooperation & Community [![Star History Chart](https://api.star-history.com/svg?repos=PaddlePaddle/PaddleMaterials&type=date&legend=top-left)](https://www.star-history.com/#PaddlePaddle/PaddleMaterilas&type=date&legend=top-left) Thanks to all contributors who have helped build PaddleMaterials！ Thanks for the following organiziton for cooprative support!

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## πŸ› οΈ Contribute to PaddleMaterials For developer, please refer to [architecture](docs/ARCHITECTURE_ch.md). --- ## πŸ“œ License PaddleMaterials is licensed under the [Apache License 2.0](LICENSE). --- ## πŸŽ“ 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} } ``` --- ## πŸ™ 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)