刚体扩散蒙特卡罗中的重要抽样
Importance Sampling in Rigid Body Diffusion Monte Carlo
摘要：
提出了一种基于格林函数短时展开的刚体扩散蒙特卡罗算法。我们表明，这种短时间的近似提供了正确的采样的角度自由度，并提供了一种通用的方法，将重要性采样的所有自由度。全重要性抽样算法显著提高了计算效率和基态性质的准确性，使分子范德华团簇中的转动和弯曲激发能直接得到研究.
英文摘要：
We present an algorithm for rigid body diffusion Monte Carlo with importance sampling, which is based on a rigorous short-time expansion of the Green's function for rotational motion in three dimensions. We show that this short-time approximation provides correct sampling of the angular degrees of freedom, and provides a general way to incorporate importance sampling for all degrees of freedom. The full importance sampling algorithm significantly improves both calculational efficiency and accuracy of ground state properties, and allows rotational and bending excitations in molecular van der Waals clusters to be studied directly.
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