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--- |
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library_name: sklearn |
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tags: |
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- tabular-regression |
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- materials property prediction |
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- baseline-trainer |
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--- |
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**Model Description** |
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The magnet Curie temperature (Tc [K]) predictor model has been trained using a supervised learning approach on a specific set of magnet classes having 14:2:1 phases. |
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The dataset to train the Tc prediction model is a distinct literature source. |
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Further, the Tc values for various 14:2:1 magnet phases at room temperature are considered for dataset creation. |
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It predicts the Tc value using the chemical composition as a feature. |
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E.g: To predict the Tc value Nd2Fe14B1 magnet composition, the features are Nd=2, Fe=14, and B=1. |
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**Application & Limitations** |
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Input feature as the chemical composition of the test sample should match the sequence of the features described in the config file. |
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The trained model is valid for 14:2:1 phases only, which are stoichiometric compositions and the predicted Tc value is in Kelvin and at room temperature. |
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**Model pipeline** |
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The voting regressor to predict the Tc combines the following four base models and equal weight is assigned to each base model. |
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1. Extra tree regressor (ET) |
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2. Extreme gradient boosting (XGB) |
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3. Random forest regressor (RF) |
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4. Adaptive boosted RF regressor (AB) |
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# How to use the trained model for inference |
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```python |
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import json |
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import joblib |
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import pandas as pd |
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Tc_predictor = joblib.load('Magnet_Tc_predictor.joblib') # trained model |
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config = json.load(open('config.json')) |
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features = config['features'] # feature vector |
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#data = pd.read_excel("data.xlsx") # read test file with new compositions |
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data = data[features] |
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Predicted_value = Tc_predictor.predict(data) # predict Tc values |
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print("Predicted Tc value is: {0:.2f}'.format(Predicted_value)") |
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``` |
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