Prediction of nanoscale thermal transport and adsorption of liquid containing surfactant at solid-liquid interface via deep learning.

Journal: Journal of colloid and interface science
PMID: 35063787

Abstract

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.

Authors

  • Yuting Guo
    State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
  • Gaoyang Li
    Graduate School of Biomedical Engineering, Tohoku University, Sendai 9808577, Japan.
  • Takuya Mabuchi
    Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan; Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira Aoba-ku, Sendai, Miyagi 980-8577, Japan.
  • Donatas Surblys
    Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.
  • Taku Ohara
    Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.
  • Takashi Tokumasu
    Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.

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