yinliang420/LDH-OER: v1.0.0 - Initial Release: Models, Training Pipelines, and Datasets

🚀 Overview We are pleased to announce the initial release of the LDH-OER repository. This release provides the complete codebase implementation for our research on high-entropy catalysts. This package includes the Attention-Enhanced models, training pipelines for overpotential and doping prediction, and scripts for inference ("yuce"). ✨ Key Features 🧠 Model & Training Core Architecture: model.py contains the implementation of the deep learning model. Task-Specific Training: Specialized training scripts for different targets: overpotential.py: For training the overpotential prediction model. doping.py: For training the doping prediction model. Fine-tuning: Scripts for layer-specific fine-tuning (finallayer_finetuning.py, firstlayer_fintuning.py). 🔮 Inference & Analysis Prediction Scripts: Ready-to-use scripts for making predictions: yuce_overpotential.py: Predict overpotential for new structures. yuce_doping.py: Predict doping effects. Interpretability: attention_map.py to generate attention maps for analyzing active sites. 📊 Datasets The datasets and model checkpoints used in this study are hosted on Hugging Face. Hugging Face Hub: https://huggingface.co/datasets/yinliang22/oer_dataset Content: Processed input data, trained model checkpoints, and prediction datasets. 📂 File Structure Highlights The repository follows a flat structure for ease of use: model.py: Main model definition. overpotential.py / doping.py: Main training scripts. yuce_overpotential.py / yuce_doping.py: Inference (prediction) scripts. dataprocess.ipynb: Jupyter notebook for data preprocessing and exploration. requirements.txt: Python dependencies. 🔧 Getting Started Installation Clone the repository and install the required dependencies: Bash git clone https://github.com/yinliang420/LDH-OER.git cd LDH-OER pip install -r requirements.txt 2. Download Data Download the necessary datasets and checkpoints from Hugging Face: Python #You can use the huggingface_hub library from huggingface_hub import snapshot_download snapshot_download( repo_id="yinliang22/oer_dataset", local_dir="./oer_dataset", repo_type="dataset" ) 3. Usage Examples Training a model: To train the model for overpotential prediction: python overpotential.py To train the model for doping prediction: python doping.py Making Predictions (Inference): To predict overpotential using a trained model: python yuce_overpotential.py 📝 Citation If you use this code or data in your research, please cite our paper: Decoding Active Sites in High-Entropy Catalysts via Attention-Enhanced Model