Version 1
: Received: 5 July 2020 / Approved: 7 July 2020 / Online: 7 July 2020 (11:03:25 CEST)
How to cite:
Yesudasan, S.; Averett, R.; Chacko, S. Machine Learned Coarse Grain Water Models for Evaporation Studies. Preprints2020, 2020070126. https://doi.org/10.20944/preprints202007.0126.v1
Yesudasan, S.; Averett, R.; Chacko, S. Machine Learned Coarse Grain Water Models for Evaporation Studies. Preprints 2020, 2020070126. https://doi.org/10.20944/preprints202007.0126.v1
Yesudasan, S.; Averett, R.; Chacko, S. Machine Learned Coarse Grain Water Models for Evaporation Studies. Preprints2020, 2020070126. https://doi.org/10.20944/preprints202007.0126.v1
APA Style
Yesudasan, S., Averett, R., & Chacko, S. (2020). Machine Learned Coarse Grain Water Models for Evaporation Studies. Preprints. https://doi.org/10.20944/preprints202007.0126.v1
Chicago/Turabian Style
Yesudasan, S., Rodney Averett and Sibi Chacko. 2020 "Machine Learned Coarse Grain Water Models for Evaporation Studies" Preprints. https://doi.org/10.20944/preprints202007.0126.v1
Abstract
Evaporation studies of water using classical molecular dynamics simulations are largely limited due to its high computational expense. We aim at addressing the computational issues by developing a coarse grain model for evaporation of water on solid surfaces by combining four water molecules into a single bead. Most commonly used mono atomic pair potentials like Lennard Jones, Morse, Mie and three body potential like Stillinger-Weber are optimized using a combination of Genetic algorithm and Nelder-Mead algorithm. Among them, Stillinger-Weber based model shows excellent agreement of density and Enthalpy of vaporization with experimental results for a wide range of temperatures. Further, the new water model is used to simulate contact angle of water and thin film evaporation from surfaces with different wettabilities.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
