Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator. Due to physical constraints, the time step of the integrator need to be small to maintain sufficient precision. This limits the efficiency of simulation. To this end, we introduce a graph neural network (GNN) based model, MDNet, to predict the evolution of coordinates and momentum with large time steps. In addition, MDNet can easily scale to a larger system, due to its linear complexity with respect to the system size. We demonstrate the performance of MDNet on a 4000-atom system with large time steps, and show that MDNet can predict good equilibrium and transport properties, well aligned with standard MD simulations.
翻译:分子动态模拟(MD)通过用数字集成器解决牛顿运动方程式和数字集成器来预测原子的轨迹。 由于物理限制, 集成器的时间步骤需要小一些才能保持足够的精确度。 这限制了模拟的效率。 为此, 我们引入了一个基于图形神经网络( GNN) 的模型MDNet, 以便用大的时间步骤来预测坐标和动力的演变。 此外, MDNet由于其系统大小的线性复杂性, 很容易缩到一个更大的系统。 我们展示了MDNet在4000原子系统中的性能, 并用大的时间步骤, 并显示MDNet可以预测良好的平衡和运输特性, 与标准的MD模拟完全一致 。