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--- |
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license: mit |
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language: en |
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tags: |
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- materials science |
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- synthesis prediction |
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- lightgbm |
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- cheminformatics |
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datasets: [] |
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metrics: |
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- accuracy |
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- f1 |
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--- |
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# Synthesis Condition Predictor |
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This model predicts optimal temperature bins and atmosphere categories for inorganic material synthesis. |
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It was trained on a dataset of text-mined synthesis procedures. Here is the source of the dataset: https://www.nature.com/articles/s41597-019-0224-1 |
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**Models Included:** |
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* Temperature Bin Prediction (LightGBM) |
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* Atmosphere Category Prediction (LightGBM) |
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**Intended Use:** |
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To assist researchers in designing synthesis experiments by predicting key process parameters. |
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Input a target material, precursors, and basic operational details to get predictions. |
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**How to Use:** |
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```python |
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# Ensure your inference script and its dependencies are in the PYTHONPATH |
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# from synthesis_predictor_hf_repo.src.inference import predict_synthesis_outcome, load_all_artifacts_once |
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# Or, if running from a cloned repo where 'src' is a subdirectory: |
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# from src.inference import predict_synthesis_outcome, load_all_artifacts_once |
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# if not load_all_artifacts_once(): |
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# print("Failed to load model artifacts.") |
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# else: |
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# raw_input_example = { |
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# 'target_formula_raw': "YBa2Cu3O7", |
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# 'precursor_formulas_raw': ["Y2O3", "BaCO3", "CuO"], |
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# 'operations_simplified_list': [ |
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# {'type': 'MixingOperation', 'string': 'Ball milling for 2h', 'conditions': {'duration': [{'value':2, 'unit':'h'}]}}, |
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# {'type': 'HeatingOperation', 'string': 'Calcined at 920C for 10h in air', |
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# 'conditions': {'heating_temperature': [{'value':920}], 'heating_time': [{'value':10}], 'atmosphere':'air'}}, |
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# {'type': 'HeatingOperation', 'string': 'Sintered at 950C for 20h in O2', |
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# 'conditions': {'heating_temperature': [{'value':950}], 'heating_time': [{'value':20}], 'atmosphere':'Oxygen'}} |
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# ], |
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# 'reactants_coeffs': [("Y2O3", 0.5), ("BaCO3", 2.0), ("CuO", 3.0)], # Example, adjust as needed |
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# 'products_coeffs': [("YBa2Cu3O7", 1.0)] # Example |
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# } |
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# predictions = predict_synthesis_outcome(raw_input_example) |
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# print(predictions) |
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``` |
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**Limitations:** |
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* The model's accuracy is around 68-72%. |
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* Predictions are based on patterns in the training data and may not generalize to all chemical systems. |
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* The feature engineering for process parameters in the inference script relies on the user providing an `operations_simplified_list` that can be parsed by the internal logic. The quality of these inputs directly affects prediction accuracy. |
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**Training Data:** |
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The model was trained on a proprietary dataset of text-mined inorganic synthesis procedures. (Kononova et al.) |
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https://www.nature.com/articles/s41597-019-0224-1 |
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**Evaluation Results:** |
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The models were evaluated on a hold-out test set. |
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**1. Tuned Temperature Bin Prediction Model:** |
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* **Overall Test Set Accuracy:** 0.6821 |
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* **Overall Test Set F1 Score (Weighted):** 0.6785 |
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* **Per-Class Performance (Test Set):** |
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``` |
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precision recall f1-score support |
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TempBin_1_(1_to_900] 0.77 0.79 0.78 954 |
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TempBin_2_(900_to_1100] 0.62 0.53 0.57 743 |
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TempBin_3_(1100_to_1300] 0.58 0.58 0.58 768 |
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TempBin_4_(1300_to_3000] 0.72 0.80 0.76 715 |
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accuracy 0.68 3180 |
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macro avg 0.67 0.68 0.67 3180 |
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weighted avg 0.68 0.68 0.68 3180 |
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``` |
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**2. Tuned Atmosphere Category Prediction Model:** |
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* **Overall Test Set Accuracy:** 0.7193 |
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* **Overall Test Set F1 Score (Weighted):** 0.7174 |
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* **Per-Class Performance (Test Set):** |
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``` |
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precision recall f1-score support |
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Inert 0.59 0.38 0.46 139 |
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Other_Atm_Target 1.00 0.44 0.62 9 |
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Oxidizing 0.67 0.71 0.69 1552 |
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Reducing 0.70 0.47 0.56 100 |
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Unknown_Atm_Category 0.76 0.76 0.76 2098 |
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accuracy 0.72 3898 |
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macro avg 0.74 0.55 0.62 3898 |
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weighted avg 0.72 0.72 0.72 3898 |
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``` |
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) |
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