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     

基于EMD-PSO优化阈值的COD光谱去噪研究

在对水体COD进行光谱检测时，光谱数据中会掺杂大量噪声，影响后续对水体COD的定量分析。为了降低噪声的影响，提出了一种经验模态分解-粒子群(EMD-PSO)优化阈值去噪模型，首先对EMD分解得到的特征模函数进行噪声主导分量和信号主导分量的划分，通过PSO优化选取噪声主导分量部分的最佳阈值，对数据进行截取、重构，获得去噪光谱信号。以不同浓度梯度的COD标准溶液进行测试，实验结果表明：EMD-PSO优化阈值去噪模型相对于EMD软、硬阈值去噪，信噪比有所提高，均方误差由软、硬阈值的0.624 7和0.672 5降至0.026 19,样品浓度与吸光度线性相关系数更高，达到0.997 98。