Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires

Joshi, Shirish; Singh, Sanjeev Kumar; Dubey, Santosh

doi:10.1080/15502287.2023.2186974

International Journal for Computational Methods in Engineering

2023

DOI: 10.1080/15502287.2023.2186974

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Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires

Shirish Joshi

Sanjeev Kumar Singh

Santosh Dubey

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2024

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The optimization of evaporation rate in graphene-water system by machine learning algorithm

Qiao,

Yang,

Gao

et al. 2024

Journal of Applied Physics

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Solar interfacial evaporation, as a novel practical freshwater production method, requires continuous research on how to improve the evaporation rates to increase water production. In this study, sets of data were obtained from molecule dynamics simulation and literature, in which the parameters included height, diameter, height–radius ratio, evaporation efficiency, and evaporation rate. Initially, the correlation between the four input parameters and the output of the evaporation rate was examined through traditional pairwise plots and Pearson correlation analysis, revealing weak correlations. Subsequently, the accuracy and generalization performance of the evaporation rate prediction models established by neural network and random forest were compared, with the latter demonstrating superior performance and reliability confirmed via random data extraction. Furthermore, the impact of different percentages (10%, 20%, and 30%) of the data on the model performance was explored, and the result indicated that the model performance is better when the test set is 20% and all the constructed model converge. Moreover, the mean absolute error and mean squared error of the evaporation rate prediction model for the three ratios were calculated to evaluate their performance. However, the relationship between the height- radius ratio and optimal evaporation rate was investigated using the enumeration method, and it was determined that the evaporation efficiency was optimal when the height–radius ratio was 6. Finally, the importance of height, diameter, height– radius ratio, and evaporation efficiency were calculated to optimize evaporator structure, increase evaporation rate, and facilitate the application of interfacial evaporation in solar desalination.

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The optimization of evaporation rate in graphene-water system by machine learning algorithm

Qiao,

Yang,

Gao

et al. 2024

Journal of Applied Physics

View full text Add to dashboard Cite

show abstract

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Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires

Cited by 1 publication

References 49 publications

The optimization of evaporation rate in graphene-water system by machine learning algorithm

The optimization of evaporation rate in graphene-water system by machine learning algorithm

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