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• W4280497914 abstract "A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system." @default.
• W4280497914 created "2022-05-22" @default.
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• W4280497914 date "2022-05-12" @default.
• W4280497914 modified "2023-09-29" @default.
• W4280497914 title "A neural network-assisted open boundary molecular dynamics simulation method" @default.
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• W4280497914 doi "https://doi.org/10.1063/5.0083198" @default.
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