Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learned Coarse Grain Water Models for Evaporation Studies

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. Preprints 2020, 2020070126 (doi: 10.20944/preprints202007.0126.v1). Yesudasan, S.; Averett, R.; Chacko, S. Machine Learned Coarse Grain Water Models for Evaporation Studies. Preprints 2020, 2020070126 (doi: 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.

Keywords

Coarse Grain Models; Water Models; Nanoscale Evaporation; Nano Channels; Molecular Dynamics

Subject

ENGINEERING, Mechanical Engineering

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