期刊
JOURNAL OF COLLOID AND INTERFACE SCIENCE
卷 613, 期 -, 页码 587-596出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcis.2022.01.037
关键词
Interfacial thermal transport; Surfactant adsorption; Deep learning; Molecular dynamics; Encoding-decoding convolutional neural; network; Multi-nanoscale scheme
资金
- JST CREST Grant, Japan [JPMJCR17I2]
This study proposes a method that combines deep learning with molecular dynamics to predict the thermophysical properties of liquid interfaces quickly and accurately. By optimizing the algorithm, the prediction accuracy is improved while reducing computational costs.
Hypothesis: Recent advances in deep learning (DL) have enabled high level of real-time prediction of thermophysical properties of materials. On the other hand, molecular dynamics (MD) have been long used as a numerical microscope to observe detailed interfacial conditions but require separate simulations that are computationally costly. Hence, it should be possible to combine MD and DL to obtain high resolution interfacial details at a low computational cost. Experiment: We proposed a novel DL encoding-decoding convolutional neural network (CNN) coupled with MD to realize the mapping from micro solid-liquid interface geometry to molecular temperature and density distribution of liquid containing surfactant. A multi-nanoscale optimization scheme was further proposed to reduce the uncertainty of DL prediction at the expense of local details to obtain more resilient predictors. Findings: The statistical results showed that the proposed CNN had high prediction accuracy and could reproduce the heat transfer and adsorption phenomena under the influence of various factors including liquid composition, wettability, and solid surface roughness, while the computational efficiency was greatly improved. Our DL method with the support of multi-nanoscale learning strategies can achieve the fast and accurate visualization and prediction of various interfacial properties of liquid and assist for interfacial material design. (c) 2022 Elsevier Inc. All rights reserved.
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