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-## 基于GNN的二维材料稳定性预测 +## 🚀 Introduction -### 整体流程 +**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. -通过预测输入晶体的形成能（分解能等）等能量，实现晶体的稳定性预测，能量越低，稳定性越高。整体流程如下，网络模型输入为晶体的属性包括：原子类型、原子坐标、晶格常数等，网络模型输出为预测的晶体的形成能。涉及到： -1. 晶体数据的图结构表示； -2. GNN网络结构； -3. 预测稳定性； +
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+ +Join the PaddleMaterials WeChat group to discuss with us! +
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+ +## 🛠️ Contribute to PaddleMaterials + +For developer, please refer to [architecture](docs/ARCHITECTURE_ch.md). + +--- + +## 📜 License + +PaddleMaterials is licensed under the [Apache License 2.0](LICENSE). + +--- + +## 🎓 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) diff --git a/about_configs.md b/about_configs.md new file mode 100644 index 00000000..f1ed2823 --- /dev/null +++ b/about_configs.md @@ -0,0 +1,454 @@ +# About Configs 🧩 + +PaddleMaterials implements full lifecycle management for model training, covering core stages like training, fine-tuning, and prediction. It includes standardized datasets and build-in pre-trained model libraries, supporting one-click prediction. Training workflows are parameterized through structured configuration files, allowing end-to-end model training with simple parameter adjustments. + +
| Field Name | +Description | +
|---|---|
| Global | +System-level parameters for centralized management of public configurations and cross-module shared settings. | +
| Trainer | +Defines core training parameters including epoch count, checkpoint saving policies, and distributed training configurations. | +
| Model | +Neural network architecture definition module with initialization parameters and loss function configurations. | +
| Dataset | +Standardized data loading with integrated preprocessing, batching, and multi-process reading mechanisms. | +
| Metric | +Evaluation metric functions for performance assessment during training and testing. | +
| Optimizer | +Optimizer configuration interface supporting learning rate scheduling, weight decay, and gradient clipping parameters. | +
| Predict | +Configuration parameters for prediction workflows. | +
| Field Name | +Type | +Description | +
|---|---|---|
| label_names | +List[str] | +Defines model training targets (must match dataset column names exactly). This example enables only formation energy prediction. | +
| do_train | +Bool | +Enables/disables training loop execution. | +
| do_eval | +Bool | +Enables/disables standalone evaluation process (independent of periodic validation during training). | +
| do_test | +Bool | +Enables/disables inference testing (disabled by default). | +
| graph_converter | +Class Config | +Material structure to graph conversion configuration for data loading and prediction stages. | +
| Field Name | +Type | +Description | +
|---|---|---|
| max_epochs | +int | +Maximum training epochs. | +
| seed | +int | +Random seed for reproducibility (controls numpy/paddle/random libraries). | +
| output_dir | +str | +Output directory for model weights and logs. | +
| save_freq | +int | +Checkpoint saving interval (epochs). Set to 0 for final epoch-only saving. | +
| log_freq | +int | +Training log interval (steps). | +
| start_eval_epoch | +int | +Epoch to begin evaluation (avoids early-stage fluctuations). | +
| eval_freq | +int | +Evaluation interval (epochs). Set to 0 to disable periodic validation. | +
| pretrained_model_path | +str/None | +Pre-trained model path (None = no pre-training). | +
| pretrained_weight_name | +str/None | +When using the built-in model, specify the exact weight file name (e.g., latest.pdparams). | +
| resume_from_checkpoint | +str/None | +Checkpoint path for training resumption (requires optimizer state and training metadata). | +
| use_amp | +bool | +Enables automatic mixed precision training. | +
| amp_level | +str | +Mixed precision mode ('O1'=partial FP32, 'O2'=FP16 optimization). | +
| eval_with_no_grad | +bool | +Disables gradient computation during evaluation (set to False for models with higher-order derivatives). | +
| gradient_accumulation_steps | +int | +Gradient accumulation steps for large batch simulation. | +
| best_metric_indicator | +str | +Metric for best model selection (train/eval loss/metric). | +
| name_for_best_metric | +str | +Specific metric name (must match Metric configuration). | +
| greater_is_better | +bool | +Metric optimization direction (False = lower is better). | +
| compute_metric_during_train | +bool | +Enables training set metric computation. | +
| metric_strategy_during_eval | +str | +Evaluation strategy (an "epoch" refers to calculations performed after completing a full pass through the entire dataset, whereas a "step" denotes incremental calculations processed with each individual batch.). | +
| use_visualdl/wandb/tensorboard | +bool | +Enables specific training logging tools. | +
| Field Name | +Type | +Description | +
|---|---|---|
| __class_name__ | +str | +Model class name. | +
| __init_params__ | +dict | +Initialization parameters (e.g., node embedding dimension). | +
| Field Name | +Type | +Description | +
|---|---|---|
| __class_name__ | +str | +Metric class name (supports PaddlePaddle APIs). | +
| __init_params__ | +dict | +Initialization parameters (empty dict if none). | +
| Field Name | +Type | +Description | +
|---|---|---|
| __class_name__ | +str | +Optimizer class name (e.g., Adam). | +
| __init_params__ | +dict | +Optimizer parameters (e.g., beta1/beta2 for Adam). | +
| lr.__class_name__ | +str | +Learning rate scheduler class name (e.g., Cosine). | +
| lr.__init_params__ | +dict | +Scheduler parameters (e.g., initial/min learning rates). | +
| Field Name | +Type | +Description | +
|---|---|---|
| train.dataset.__class_name__ | +str | +Dataset class name (e.g., MP2018Dataset). | +
| train.dataset.__init_params__.path | +str | +Data file path. | +
| train.dataset.__init_params__.property_names | +str | +Target properties (references Global labels). | +
| train.dataset.__init_params__.build_structure_cfg | +dict | +Material structure construction parameters. | +
| train.sampler.__init_params__.batch_size | +int | +Training batch size (per GPU). | +
| Model Name | +Dataset | +Normalized MAE of Density | +GPUs | +Training time | +Config | +Checkpoint | Log | +
|---|---|---|---|---|---|---|
| infgcn_md17_benzene | +MD17_EC_Benzene | +21.2614% | +1 | +59min | +infgcn_md17_benzene | +checkpoint | log | +
| infgcn_md17_ethane | +MD17_EC_Ethane | +6.9443% | +1 | +1hour17min | +infgcn_md17_ethane | +checkpoint | log | +
| infgcn_md17_ethanol | +MD17_EC_Ethanol | +64.5951% | +1 | +7min | +infgcn_md17_ethanol | +checkpoint | log | +
| infgcn_md17_malonaldehyde | +MD17_EC_Malonaldehyde | +17.7947% | +1 | +1hour29min | +infgcn_md17_malonaldehyde | +checkpoint | log | +
| infgcn_md17_phenol | +MD17_EC_Phenol | +20.2144% | +1 | +1hour17min | +infgcn_md17_phenol | +checkpoint | log | +
| infgcn_md17_resorcinol | +MD17_EC_Resorcinol | +15.8850% | +1 | +1hour23min | +infgcn_md17_resorcinol | +checkpoint | log | +
| infgcn_qm9 | +QM9_EC | +1.7542% | +1 | +75hour41min | +infgcn_qm9 | +checkpoint | log | +
| infgcn_cubic | +MP_EC (cubic) | +47.3829% | +1 | +12hour6min | +infgcn_cubic | +checkpoint | log | +
| infgcn_omol25_mc_5k | +OMol25_EC_5k | +12.6260% | +4 | +66hour28min | +infgcn_omol25 | +checkpoint | log | +
| 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 | +
| Parameter | +Description | +
|---|---|
| --config_path | +Configuration file path | +
| --checkpoint_path | +Model weights file path | +
| --cif_file_path | +Path to CIF files for prediction | +
| --save_path | +Path to save prediction results | +
| Parameter | +Description | +
|---|---|
| -c | +Configuration file path | +
| Global.do_train | +Set to False for testing | +
| Global.do_eval | +Whether to evaluate on validation set | +
| Global.do_test | +Whether to evaluate on test set | +
| Trainer.pretrained_model_path | +Your model weights path | +
| Trainer.output_dir | +Output directory for log files | +
| Model Name | +Dataset | +Energy MAE(meV/atom) | +Force MAE(meV/A) | +Stress MAE(GPa) | +Magmom MAE(μB) | +GPUs | +Training time | +Config | +Checkpoint | Log | +
|---|---|---|---|---|---|---|---|---|---|
| chgnet_mptrj | +MPtrj_2022.9_full | +30 | +77 | +4.348 | +0.032 | +~ | +~ | +chgnet_mptrj | +checkpoint | log | +
| chgnet_oc20_s2ef_energy | +oc20_s2ef | +- | +- | +- | +- | +~ | +~ | +chgnet_oc20_s2ef_energy | +checkpoint | log | +
| chgnet_oc20_s2ef_forces | +oc20_s2ef | +- | +- | +- | +- | +~ | +~ | +chgnet_oc20_s2ef_forces | +checkpoint | log | +