Graph Neural Networks Accelerated Molecular Dynamics
- URL: http://arxiv.org/abs/2112.03383v1
- Date: Mon, 6 Dec 2021 22:11:00 GMT
- Title: Graph Neural Networks Accelerated Molecular Dynamics
- Authors: Zijie Li, Kazem Meidani, Prakarsh Yadav, Amir Barati Farimani
- Abstract summary: We developed a GNN Accelerated Molecular Dynamics (GAMD) model that achieves fast and accurate force predictions.
Our results show that GAMD can accurately predict the dynamics of two typical molecular systems, Lennard-Jones (LJ) particles and Water (LJ+Electrostatics)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Molecular Dynamics (MD) simulation is a powerful tool for understanding the
dynamics and structure of matter. Since the resolution of MD is atomic-scale,
achieving long time-scale simulations with femtosecond integration is very
expensive. In each MD step, numerous redundant computations are performed which
can be learnt and avoided. These redundant computations can be surrogated and
modeled by a deep learning model like a Graph Neural Network (GNN). In this
work, we developed a GNN Accelerated Molecular Dynamics (GAMD) model that
achieves fast and accurate force predictions and generates trajectories
consistent with the classical MD simulations. Our results show that GAMD can
accurately predict the dynamics of two typical molecular systems, Lennard-Jones
(LJ) particles and Water (LJ+Electrostatics). GAMD's learning and inference are
agnostic to the scale, where it can scale to much larger systems at test time.
We also performed a comprehensive benchmark test comparing our implementation
of GAMD to production-level MD softwares, where we showed GAMD is competitive
with them on the large-scale simulation.
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