GenEvaPa: A generic evaporation package for modeling evaporation in molecular dynamics simulations

B Harris and GY Liu and R Faller, COMPUTER PHYSICS COMMUNICATIONS, 282, 108539 (2023).

DOI: 10.1016/j.cpc.2022.108539

This work presents a novel general tool for modeling the process of evaporation without the need for modifying existing software using Python. The tool was developed based on the MDAnalysis package, which is used to import a Molecular Dynamics trajectory. The tool then removes solvent molecules and outputs a new structure file to be used for further simulation and analysis. This process is designed to be iterated by using the resulting dynamic simulation trajectory as the input file. The evaporation is designed to randomly delete solvent molecules while preserving solvation shells around solutes. The evaporation rate can be controlled by the length of the MD simulations and the number of particles removed between dynamic simulations. Validity of the tool was tested extensively using the Gromacs suite. Advantages of this tool include its genericness, simplicity and user friendliness, as no significant modification of existing software platform or Gromacs specific tools are needed. Program summary Program title: GenEvaPa CPC Library link to program files: https://doi .org /10 .17632 /y5c3jnbjvs .1 Developer's repository link: github .com /bradsharris /GenEvaPa Licensing provisions: GNU General Public License 3 Programming language: Python >3 Nature of problem: Approximate evaporation/drying processes in atomistic and coarse-grained molecular dynamics simulations while maintaining solvation shells around solute of interest. Forced drying in this manner allows for the study of a range of concentrations and self- assembly interactions. Solution method: A python wrapper for existing molecular dynamics codes that randomly selects solvent for removal relative to a distance criteria around a solute to maintain solvation shells. Removal on final structure files maintains generic applicability for MD source codes and enables incorporation into automated loops to study longer drying. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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