PaddleMaterial is a data-mechanism dual-driven and deep learning toolkit based on PaddlePaddle for material science, designed to help researchers more efficiently explore, discover, and develop new materials. It has supported inorganic materials and part of organic molecules, and will support more types of materials including polymers, organic molecules, catalysts, and so on. It has supported some representative models including the equivalent graph networks-based model, diffusion model, multi-modal model, and will support more kinds of deep learing models and agents works related to AI4Material fields in the feature.
Inorganic materials, characterized by their symmetrical and periodic structures, exhibit a wide range of properties and are widely applied in various fields, from electronic devices to energy applications. Traditional experimental and computational methods for discovering crystalline materials are often time-consuming and expensive. Data-driven approaches to material discovery have the power to model the highly complex atomic systems within crystalline materials, paving the way for rapid and accurate material discovery.
Organic materials, distinguished by covalently linked, directionally bonded networks, mainly defined as a carbon–hydrogen or carbon–carbon bond chemical compound. These traits support core applications including flexible displays, organic photovoltaics, high-energy-density battery electrodes, advanced separation membranes, catalyts. The vast compositional and conformational space of organic molecules makes trial-and-error synthesis and ab-initio simulations slow and costly. Data-driven methods that fuse high-throughput datasets, graph-based representations, and deep generative models rapidly learn structure–property links, enabling fast virtual screening and rational design for more agile, sustainable advances in organic materials.
🔥 2025.07.01: The Suzhou Laboratory has established a novel framework based on PaddleMaterial, combining an active learning workflow with conditional-diffusion-based structure generation, thereby achieving unprecedented expansion of two-dimensional material databases. For more information, please refer to ML2DDB.
- MLIP-Machine Learning Interatomic Potential
- IOMPP-Inorganic Material Property Prediction
- IOMSG-Inorganic Material Structure Generation
Please refer to the installation document for environment configuration.
PaddleMaterial offers multiple built-in models that can be directly used for inference. Taking the megnet_mp2018_train_60k_e_form model as an example (a MEGNet model trained on the MP2018 dataset for material formation energy prediction), use the following command for inference:
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'| Parameter | Description |
|---|---|
| --model_name | Name of the built-in model |
| --weights_name | Weights file name |
| --cif_file_path | Path to CIF files for prediction |
| --save_path | Path to save prediction results |
For more information on how to use PaddleMaterial to train and fine tune a model, please refer to the documentation.
We sincerely invite you to spare a moment from your busy schedule to share your feedback.
PaddleMaterial is licensed under the Apache License 2.0.
@misc{paddlematerial2025,
title={PaddleMaterial, a deep learning toolkit based on PaddlePaddle for material science.},
author={PaddleMaterial Contributors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleMaterial}},
year={2025}
}
This repository references the code from the following repositories: PaddleScience, Matgl, CDVAE, DiffCSP, MatterGen, MatterSim, CHGNet, AIRS, etc.