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Computation of nodal surfaces in fixed-node diffusion Monte Carlo calculations using a genetic algorithm

机译:使用遗传算法在固定节点扩散蒙特卡罗计算中计算节点面

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The fixed-node diffusion Monte Carlo (DMC) algorithm is a powerful way of computing excited state energies in a remarkably diverse number of contexts in quantum chemistry and physics. The main difficulty in implementing the procedure lies in obtaining a good estimate of the nodal surface of the excited state in question. Although the nodal surface can sometimes be obtained from symmetry or by making approximations this is not always the case. In any event, nodal surfaces are usually obtained in an ad hoc way. In fact, the search for nodal surfaces can be formulated as an optimization problem within the DMC procedure itself. Here we investigate the use of a genetic algorithm to systematically and automatically compute nodal surfaces. Application is made to the computation of excited states of the HCN-~4He complex and to the computation of tunneling splittings in the hydrogen bonded HCl-HCl complex.
机译:固定节点扩散蒙特卡洛(DMC)算法是在量子化学和物理学中大量不同的上下文中计算激发态能量的有效方法。实施该程序的主要困难在于获得对所讨论的激发态的节点表面的良好估计。尽管有时可以通过对称或近似获得节点表面,但情况并非总是如此。无论如何,节点表面通常是临时获得的。实际上,可以在DMC程序本身中将对节点表面的搜索表述为优化问题。在这里,我们研究了使用遗传算法来系统地自动计算节点表面。应用于计算HCN-〜4He配合物的激发态和计算氢键HCl-HCl配合物中的隧穿裂隙。

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