Exact Fixed-node Quantum Monte Carlo： Self-optimizing Procedure

In this paper, a novel exact fixed-node quantum Monte Carlo (EFNQMC) algorithm was proposed, which is a self-optimizing and self-improving procedure. In contrast to the previous EFN-QMC method, the importance function of this method is optimized synchronistically in the diffusion procedure, but not be-fore beginning the EFNQMC computation. In order to optimize the importance function, the improved steepest descent tech-nique is used, in which the step size is automatically adjustable.The procedure is quasi-Newton type and converges super linear-ly. The present method also uses a novel trial function, which has correct electron-electron and electron-nucleus cusp condi-tious. The novel EFNQMC algorithm and the novel trial func-tion are employed to calculate the energies of 1 ^1A1 state of CH2, ^1Ag state of Cs and the ground-states of H2, LiH, Li2 and H2O.     

A new algorithm for the fixed-node quantum Monte Carlo method

A novel algorithm is proposed for the fixed-node quantum Monte Carlo (FNQMC) method.In contrast to previous procedures,its "guiding function" is not optimized prior to diffusion quantum Monte Carlo (DMC) computation but synchronistically in the diffusion process The new algorithm can not only save CPU time,but also make both of the optimization and diffusion carried out according to the same sampling fashion,reaching the goal to improve each other This new optimizing procedure converges super-linearly,and thus can accelerate the particle diffusion During the diffusion process,the node of the "guiding function" changes incessantly,which is conducible to reducing the "fixed-node error" The new algorithm has been used to calculate the total energies of states X3B1 and a1A1 of CH2 as well as π-X2B1 and λ-2A1 of NH2 The singlet-triplet energy splitting (λEsT) in CH2 and π energy splitting in NH2 obtained with this present method are (45 542±1.840) and (141.644±1.589) kJ/mol,respectively The calculated     

变分量子Monte Carlo的一个新算法

提出变分量子MonteCarlo(VMC)计算的新算法-极小化方差(MV)方法.它从局域能的内部结构出发,直接削减其波动以达到将VMC的方差减到极小的目的.给出了局域能的分析表达式,导出VMC的极小方差原理,并建立方差极小化的计算步骤.     

量子Monte Carlo处理激发态

提出了用于电子激发态的剩余函数变分量子ＭｏｎｔｅＣａｒｌｏ（ＳＦＶＭＣ）方法,已经证明：若激发态的初始波函数与基态的初始波函数属于对称性不同的不可约表示时,该激发态的ＳＦＶＭＣ方法与基态的ＳＦＶＭＣ方法完全相同;若激发态的初始波函数与基态的初始波函数有相同的对称性时,只要对激发态的初始波函数作正交性修正,则其态的ＳＦＶＭＣ方法亦可推到该激发态的情况。文章导出了这第二类激发态的ＳＦＶＭＣ方法的详细计     

一种高效的优化量子Monte Carlo波函数的新方法

提出了一个优化量子MonteCarlo波函数的新方法，与前人的方法相比，它不使用变分原理而是极小化Schrodinger方程的剩余量；不按Ψ2取样而是按Ψ2（EL－E）2取样；不用差分而是用分析导数；不使用传统的速降法而是使用一个步长自动调节的下降法，它具有拟牛顿性质，因而是超线性收敛的．由于这四项新技术的使用，使得波函数的优化快速、准确，收敛速度比前人提出的方法快3～5倍．     

固定节面量子Monte Carlo的一个新算法

提出了一个固定节面量子Monte Carlo的新算法,与前人的算法相比,其“指导函数”的优化不是在扩散前,而是在扩散过程中同步进行;这不仅在机时上是节省的,更重要的是优化与扩散两者按相同的取样方式进行,达到互相改善的目的;这一优化方案是超线性收敛的,它能加快粒子的扩散;在扩散过程中,“指导函数”的节面不断发生改变,这有利于减小“固定节面误差”.这一新算法已被运用到CH_2的X~3B_1态和a~1A_1态,以及NH_2的π-X~2B_1态和σ-A~2A_1态总能量的计算,由此算出了CH_2单一三重态的“劈开”能△E_(s-T)=(45.542±1.840)kJ/mol和NH_2的σ-π“劈开”能△E_(σ-π)=(141.644±1.589)kJ/mol.计算结果表明这一新算法在精度、统计误差和计算量方面比一般固定节面量子Monte Carlo方法都要优越得多.     

精确固定节面量子Monte：Carlo差值法

本文提出了精确固定节面量子Monte Carlo差值法，这个新算法能够在精确固定节面量子Monte Carlo方法的基础上直接计算两个体系之间的能量差，且使计算结果的统计误差达到10-2 kJ/mol 数量级，获得电子相关能90%以上。我们把这个新算法应用于分子势能面的研究中，使用一个“刚性移动”模型，利用Jacobi变换使分子两个几何构型的能量计算具有很好的正相关性，因而能得到准确的能量差值，于是精确的分子势能面就可以得到。这个新算法已经被使用到BH分子基态势能曲线和H₃分子势能面的研究。这个算法还可应用于分子光谱、化学反应能量变化值等领域的研究。     

精确固定节面量子Monte Carlo差值法

提出了精确固定节面量子Monte Carlo差值法, 这个新算法能够在精确固定节面量子Monte Carlo方法的基础上直接计算两个体系之间的能量差, 且使计算结果的统计误差达到10-5 hartree 数量级, 获得电子相关能90%以上. 我们把这个新算法应用于分子势能面的研究中, 使用一个“刚性移动”模型, 利用Jacobi变换使分子两个几何构型的能量计算具有很好的正相关性, 因而能得到准确的能量差值, 由此就可以得到精确的分子势能面.     

《有机化学实验》课程教改的新思路——基于VRML的虚拟实验研究与开发

介绍基于VRML的《有机化学实验》网络课程的基本结构,开发流程和系统设计等内容,重点探讨了网上课程的系统设计与教学设计,主要包括 :网上教学模式设计、教学内容设计、屏幕界面设计、导航设计、交互设计与VRML技术等内容,同时对该课程的使用效果作了简单的探讨。     

Differential diffusion quantum Monte Carlo method: determination of potential energy surfaces of molecules

A differential approach for self-optimizing diffusion Monte Carlo calculation was proposed in this paper, which is a new algorithm combining three techniques such as optimizing, diffusion and correlation sampling. This method can be used to directly compute the energy differential between two systems in the diffusion process, making the statistical error of calculation be reduced to order of 10-5 hartree, and recover about more than 80% of the correlation energy. We employed this approach to set up a potential energy surface of a molecule, used a "rigid move" model, and utilized Jacobi transformation to make energy calculation for two configurations of a molecule having good positive correlation. So, an accurate energy differential could be obtained, and the potential energy surface with good quality can be depicted. In calculation, a technique called "post-equilibrium remaining sample was set up firstly, which can save about 50% of computation expense. This novel algorithm was used to study the potenti