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@@ -10,7 +10,7 @@ We provided two machine learning models for uses: **Model A** trained on `[M, R,
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$$(\frac{Fe}{Mg + Si})_{planet} = -1.35 \pm 0.36 + 4.84 \pm 0.92 \times (\frac{Fe}{Mg + Si})_{star}$$
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**Model A** has a better predictive accuracy, but its application is limited by some difficulties in measuring the tidal Love number `k2` of rocky exoplanets. **Model B** significantly breaks the density-composition degeneracy and accurately predicts the interior properties of rocky exoplanets. Along with the development of space-based observation technologies, orbital or shape observations could be possible to determine the Love number `k2` of rocky exoplanets and hence the machine learning models B and C would be applied more broadly.
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**Model A** has a better predictive accuracy, but its application is limited by some difficulties in measuring the tidal Love number `k2` of rocky exoplanets. **Model B** significantly breaks the density-composition degeneracy and accurately predicts the interior properties of rocky exoplanets. Along with the development of space-based observation technologies, orbital or shape observations could be possible to determine the tidal Love number `k2` of rocky exoplanets and hence the machine learning models B and C would be applied more broadly.
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## Quick Start
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### Step 1:

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