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相关论文: A simple and efficient approach to the optimizatio…

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We present a simple, robust and highly efficient method for optimizing all parameters of many-body wave functions in quantum Monte Carlo calculations, applicable to continuum systems and lattice models. Based on a strong zero-variance…

其他凝聚态物理 · 物理学 2009-11-11 C. J. Umrigar , Julien Toulouse , Claudia Filippi , S. Sorella , R. G. Hennig

An appropriate iterative scheme for the minimization of the energy, based on the variational Monte Carlo (VMC) technique, is introduced and compared with existing stochastic schemes. We test the various methods for the 1D Heisenberg ring…

强关联电子 · 物理学 2009-11-11 Sandro Sorella

We study three wave function optimization methods based on energy minimization in a variational Monte Carlo framework: the Newton, linear and perturbative methods. In the Newton method, the parameter variations are calculated from the…

化学物理 · 物理学 2015-06-26 Julien Toulouse , C. J. Umrigar

An algorithm is proposed to optimize quantum Monte Carlo (QMC) wave functions based on New ton's method and analytical computation of the first and second derivatives of the variati onal energy. This direct application of the variational…

化学物理 · 物理学 2016-09-08 Xi Lin , Hongkai Zhang , Andrew M. Rappe

We present a simple, robust and efficient method for varying the parameters in a many-body wave function to optimize the expectation value of the energy. The effectiveness of the method is demonstrated by optimizing the parameters in…

其他凝聚态物理 · 物理学 2016-08-31 C. J. Umrigar , Claudia Filippi

A quantum Monte Carlo method is introduced to optimize excited state trial wavefunctions. The method is applied in a correlation function Monte Carlo calculation to compute ground and excited state energies of bosonic van der Waals clusters…

计算物理 · 物理学 2009-11-06 M. P. Nightingale , Vilen Melik-Alaverdian

The energy variance optimization algorithm over a fixed ensemble of configurations in variational Monte Carlo is formally identical to a problem of fitting data: we reexamine it from a statistical maximum-likelihood point of view. We detect…

原子与分子团簇 · 物理学 2009-11-07 Dario Bressanini , Gabriele Morosi , Massimo Mella

We use a variational Monte Carlo algorithm to solve the electronic structure of two-dimensional semiconductor quantum dots in external magnetic field. We present accurate many-body wave functions for the system in various magnetic field…

介观与纳米尺度物理 · 物理学 2009-11-11 Ari Harju

Variational Monte Carlo methods have recently been applied to the calculation of excited states; however, it is still an open question what objective function is most effective. A promising approach is to optimize excited states using a…

计算物理 · 物理学 2023-12-04 William A. Wheeler , Kevin G. Kleiner , Lucas K. Wagner

We review a recent approach for the simulation of many-body interacting systems based on an efficient generalization of the Lanczos method for Quantum Monte Carlo simulations. This technique allows to perform systematic corrections to a…

强关联电子 · 物理学 2007-05-23 Sandro Sorella

We have employed the steepest descent method to optimise the variational ground state quantum Monte Carlo wave function for He, Li, Be, B and C atoms. We have used both the direct energy minimisation and the variance minimisation…

计算物理 · 物理学 2015-05-19 M. Ebrahim Foulaadvand , Mohammad Zarenia

Quantum mechanics for many-body systems may be reduced to the evaluation of integrals in 3N dimensions using Monte-Carlo, providing the Quantum Monte Carlo ab initio methods. Here we limit ourselves to expectation values for trial…

计算物理 · 物理学 2010-11-22 John Robert Trail , Ryo Maezono

With our recently proposed effective Hamiltonian via Monte Carlo, we are able to compute low energy physics of quantum systems. The advantage is that we can obtain not only the spectrum of ground and excited states, but also wave functions.…

高能物理 - 格点 · 物理学 2015-06-25 Xiang-Qian Luo , C. Q. Huang , J. Q. Jiang , H. Jirari , H. Kroeger , K. Moriarty

Compact and accurate wave functions can be constructed by quantum Monte Carlo methods. Typically, these wave functions consist of a sum of a small number of Slater determinants multiplied by a Jastrow factor. In this paper we study the…

凝聚态物理 · 物理学 2009-10-30 Chien-Jung Huang , C. J. Umrigar , M. P. Nightingale

We investigate Monte Carlo energy and variance minimization techniques for optimizing many-body wave functions. Several variants of the basic techniques are studied, including limiting the variations in the weighting factors which arise in…

凝聚态物理 · 物理学 2009-10-31 P. R. C. Kent , R. J. Needs , G. Rajagopal

This review covers applications of quantum Monte Carlo methods to quantum mechanical problems in the study of electronic and atomic structure, as well as applications to statistical mechanical problems both of static and dynamic nature. The…

chem-ph · 物理学 2016-10-26 M. P. Nightingale , C. J. Umrigar

A compression algorithm is introduced for multi-determinant wave functions which can greatly reduce the number of determinants that need to be evaluated in quantum Monte Carlo calculations. We have devised an algorithm with three levels of…

计算物理 · 物理学 2015-06-17 Gihan L. Weerasinghe , Pablo Lopez Rios , Richard J. Needs

The quantum Monte Carlo algorithm is arguably one of the most powerful computational many-body methods, enabling accurate calculation of many properties in interacting quantum systems. In the presence of the so-called sign problem, the…

强关联电子 · 物理学 2018-02-23 Chia-Chen Chang , Miguel A. Morales

A quantum Monte Carlo method of determining Jastrow-Slater wave functions for which the energy is stationary with respect to variations in the single-particle orbitals is presented. A potential is determined by a least-squares fitting of…

凝聚态物理 · 物理学 2007-05-23 Stephen Fahy , Claudia Filippi

We construct an effective Hamiltonian via Monte Carlo from a given action. This Hamiltonian describes physics in the low energy regime. We test it by computing spectrum, wave functions and thermodynamical observables (average energy and…

量子物理 · 物理学 2009-10-31 H. Jirari , H. Kröger , X. Q. Luo , K. J. M. Moriarty
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