Schematic diagram of the workflow from ML-based prediction to DFT validation and experimental discovery (IMAGE)

Tsinghua University Press

Schematic diagram of the workflow from ML-based prediction to DFT validation and experimental discovery

Caption

Data on all known stable MAX phases were collected from experimental literature, and non-existent MAX phases were identified using existing first-principles results. Models such as the Random Forest Classifier (RFC), Support Vector Classifier (SVC), and Gradient Boosting Trees (GBT) were employed to predict the stability of MAX phases. The accuracy of the ML models was verified by the criteria of thermodynamic stability and intrinsic stability from DFT calculations, which also guided the experimental synthesis.

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Journal of Advanced Ceramics, Tsinghua University Press

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