Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

Subject Areas

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

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