A method for regularization of evolutionary polynomial regression

Coelho, Francisco; Neto, João Pedro

doi:10.1016/j.asoc.2017.05.047

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…Yang et al. trained ML models, such as decision tree, RFR, ANN, linear regression, and polynomial regression, to predict the ITRs between graphene and hexagonal boron nitride based on 1650 MD simulation data entries . The results proved that the ANN algorithm was superior in predicting ITRs.…”

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confidence: 99%

“…In addition to predicting κ L , ML methods have also been applied to predict other thermal transport properties, such as interfacial thermal resistivity (ITR). Yang et al trained ML models, such as decision tree, 76 RFR, ANN, linear regression, 41 and polynomial regression, 77 to predict the ITRs between graphene and hexagonal boron nitride based on 1650 MD simulation data entries. 78 The results proved that the ANN algorithm was superior in predicting ITRs.…”

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A Critical Review of Machine Learning Techniques on Thermoelectric Materials

Wang

Ning

et al. 2023

J. Phys. Chem. Lett.

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Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.

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