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.

翻译：分子动态模拟(MD)是了解物质动态和结构的有力工具。 由于MD的分辨率是原子尺度, 实现长时间尺度的与Femto二集成的模拟非常昂贵。 在每一个MD步骤中,都会进行许多可以学习和避免的重复计算。 这些多余的计算可以由像图形神经网络(GNN)这样的深层次学习模型来代替和建模。 在这项工作中, 我们开发了一个GNN加速分子动态模型(GAMD)模型, 该模型能够实现快速和准确的武力预测,并产生与经典MD模拟一致的轨迹。 我们的结果表明, GAMD可以准确预测两种典型的分子系统,即Lennard-Jones(LJ)粒子和Water(LJ+Eectrostatics)的动态。 GAMD的学习和推断对于规模来说是不可忽视的, 在那里, 它可以在测试时缩放到大得多的系统。 我们还进行了全面的基准测试, 将我们GAMD的运用与生产级MD软件进行比较, 我们显示GAMD在大规模模拟中具有竞争力。