Journal
CHEMICAL PHYSICS LETTERS
Volume 768, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cplett.2021.138360
Keywords
Surface wettability; Artificial neural network; Lattice gas model; Monte Carlo simulation
Funding
- National Research Foundation of Korea (NRF) - Korea government [2018R1A2A2A05019776]
- National Research Foundation of Korea [2018R1A2A2A05019776] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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An artificial neural network (ANN) model successfully predicts the wettability of a surface patterned with rectangular pillars and performs well against Monte Carlo simulations using the lattice gas model.
An array of the nanoscale grooves is often engraved to tune the wettability of a surface. Predicting the wettability of such a grooved surface is challenging because of the complex interplay of the geometry and energetics of the surface and temperature. Herein, we present an artificial neural network (ANN) model which predicts the wettability of a surface periodically patterned with the rectangular pillars. The present ANN model performs well against an extensive set of Monte Carlo simulations using the lattice gas model.
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