Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
Robust model benchmarking and bias- imbalance in data-driven materials science: a case study on MODNet
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CrabNet outperforms MODNet and random forest models when predicting battery electrode properties from composition, with cross-validation and clustering confirming coherent groupings.
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A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
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Machine Learning for Electrode Materials: Property Prediction via Composition
CrabNet outperforms MODNet and random forest models when predicting battery electrode properties from composition, with cross-validation and clustering confirming coherent groupings.