论文标题
Gromacs随机动力学和BaoAB是等效的配置采样算法
GROMACS Stochastic Dynamics and BAOAB are equivalent configurational sampling algorithms
论文作者
论文摘要
用于分子动力学模拟的两个最广泛使用的Langevin积分器是Gromacs随机动力学（GSD）集成剂和拆分方法BAOAB。在这封信中，我们表明gromacs随机动力学积分器等于较少使用的分裂方法BAOA。立即得出，GSD和BAOAB采样了相同的配置，并且具有相同的高配置精度。我们的数值结果表明，GSD/BAOA比BAOAB具有更高的动力学精度。
Two of the most widely used Langevin integrators for molecular dynamics simulations are the GROMACS Stochastic Dynamics (GSD) integrator and the splitting method BAOAB. In this letter, we show that the GROMACS Stochastic Dynamics integrator is equal to the less frequently used splitting method BAOA. It immediately follows that GSD and BAOAB sample the same configurations and have the same high configurational accuracy. Our numerical results indicate that GSD/BAOA has higher kinetic accuracy than BAOAB.
