This repository is based on our published work, bringing AI to the forefront of organic battery material discovery! ✨
- Journal Publication: Our research, "AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries," is published in the Journal of Polymer Science. 📖
- Preprint Version: You can also check out the preprint on arXiv: arXiv:2502.13899. 🧪
This project includes the following key files to help you explore and utilize our models:
multi_task_learning.ipynb: 🧠 This notebook handles loading the organic battery dataset, performing data scaling and splitting, and training models across 5 folds for robust results.meta_learner.ipynb: 📊 Here, we load the 5 multi-task models (one for each fold) and then train on the validation dataset (with testing on the training set) to refine predictions.inverse_design.ipynb: 💡 Use this notebook to specify reference candidate(s), modify SMILES strings, and predict new properties using the power of our pre-trained models for inverse design!