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Works cited by this work

69 works

Work: Tribological Properties Assessment of Metallic Glasses Through a Genetic Algorithm-Optimized Machine Learning Model

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  2. Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

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  3. Role of Alloying Composition on Mechanical Properties of CuZr Metallic Glasses During the Nanoindentation Process

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  5. Characterization of nanoscale structural heterogeneity in metallic glasses: A machine learning study

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  6. An Introduction to Genetic Algorithms

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  7. Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

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  8. Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach

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  9. Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach

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  10. Prediction of Vickers hardness of amorphous alloys based on interpretable machine learning

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  11. Machine learning modeling for the prediction of plastic properties in metallic glasses

    Nicolás Amigó, Simón Palominos, Felipe J. Valencia

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