jablonkagroup/corral-environment-tasks huggingface dataset:
Task: Batch Retrieve Oxide Polymorphs
Environment: ml (level_1 subtask)
Description: Generate a diverse list of oxide compositions for dataset creation. Retrieve polymorphs for all oxide compositions from Materials Project database. For each composition, collect multiple polymorphs including both stable and metastable structures. Consolidate all individual polymorph files into a single comprehensive dataset. Each entry should have `source_composition`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file
Submission format: path to the json file (eg. polymorph_data.json)
Task: Prepare ML-Ready Dataset
Environment: ml (level_1 subtask)
Description: Transform the consolidated dataset into ML-ready format with engineered features, proper train/test splits. Create a metadata json file with `features`, `train_path` and `test_path`.
Allowed tools: prepare_tabular_dataset
Submission format: path to the json file (eg. ml_dataset_metadata.json)
Task: Train XGBoost Formation Energy Model
Environment: ml (level_1 subtask)
Description: Train an XGBoost regression model to predict formation energies of oxide polymorphs. Use the prepared dataset with optimized hyperparameters for oxide materials. Focus on achieving good generalization performance across different oxide families and structural types.
Allowed tools: train_xgboost_model, perform_cross_validation
Submission format: path to the model checkpoint file (eg. trained_xgboost_model.pkl)
Task: Evaluate Model Performance
Environment: ml (level_1 subtask)
Description: Evaluate the trained XGBoost model using test set and cross-validation metrics. Save the results as a JSON file with keys `test_set_evaluation` and `cross_validation_results`. The test_set_evaluation dictionary must contain `mae`, `rmse`, `r2`, and `feature_importance`. The cross_validation_results dictionary must contain `r2_mean` and `r2_std`.
Allowed tools: evaluate_xgboost_model, perform_cross_validation
Submission format: path to the results file (eg. model_evaluation_results.json)
Task: Batch Retrieve Nitride Polymorphs
Environment: ml (level_1 subtask)
Description: Generate a diverse list of nitride compositions for dataset creation. Retrieve polymorphs for all nitride compositions from Materials Project database. For each composition, collect multiple polymorphs including both stable and metastable structures. Consolidate all individual polymorph files into a single comprehensive dataset. Each entry should have `source_composition`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file
Submission format: path to the json file (eg. polymorph_data.json)
Task: Train XGBoost Formation Energy Model
Environment: ml (level_1 subtask)
Description: Train an XGBoost regression model to predict formation energies of nitride polymorphs. Use the prepared dataset with optimized hyperparameters for nitride materials. Focus on achieving good generalization performance across different nitride families and structural types.
Allowed tools: train_xgboost_model, perform_cross_validation
Submission format: path to the model checkpoint file (eg. trained_xgboost_model.pkl)
Task: Batch Retrieve Sulphide Polymorphs
Environment: ml (level_1 subtask)
Description: Generate a diverse list of sulphide compositions for dataset creation. Retrieve polymorphs for all sulphide compositions from Materials Project database. For each composition, collect multiple polymorphs including both stable and metastable structures. Consolidate all individual polymorph files into a single comprehensive dataset. Each entry should have `source_composition`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file
Submission format: path to the json file (eg. polymorph_data.json)
Task: Train XGBoost Formation Energy Model
Environment: ml (level_1 subtask)
Description: Train an XGBoost regression model to predict formation energies of sulphide polymorphs. Use the prepared dataset with optimized hyperparameters for sulphide materials. Focus on achieving good generalization performance across different sulphide families and structural types.
Allowed tools: train_xgboost_model, perform_cross_validation
Submission format: path to the model checkpoint file (eg. trained_xgboost_model.pkl)