论文标题
扭矩采样的降低变化定向分布函数
Reduced-variance orientational distribution functions from torque sampling
论文作者
论文摘要
我们介绍了一种在计算机模拟中采样定向分布功能的方法。该方法基于与平衡中各向异性颗粒相互作用的经典多体系统的精确扭矩平衡方程。我们不是传统的事件计数，而是通过作用在粒子上的扭矩的定向积分来重建方向分布函数。我们使用Langevin动力学和过度引导的Brownian动力学以及具有两个颗粒间相互作用势来测试两维和三维中的扭矩采样方法。在所有情况下，扭矩采样方法都比传统计数方法获得的定向分布函数的曲线具有更好的精度。扭矩采样方法的准确性与bin尺寸无关，因此可以通过任意小角度分辨率解析定向分布函数。
We introduce a method to sample the orientational distribution function in computer simulations. The method is based on the exact torque balance equation for classical many-body systems of interacting anisotropic particles in equilibrium. Instead of the traditional counting of events, we reconstruct the orientational distribution function via an orientational integral of the torque acting on the particles. We test the torque sampling method in two- and three-dimensions, using both Langevin dynamics and overdamped Brownian dynamics, and with two interparticle interaction potentials. In all cases the torque sampling method produces profiles of the orientational distribution function with better accuracy than those obtained with the traditional counting method. The accuracy of the torque sampling method is independent of the bin size, and hence it is possible to resolve the orientational distribution function with arbitrarily small angular resolutions.
