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Monte Carlo Methods In Ab Initio Quantum Chemistry
TLDR
A review of ab initio quantum chemistry introduction to Monte Carlo methods can be found in this article, where the variational Monte Carlo method and the quantum Monte Carlo exact Green's function methods are discussed.Abstract:
Review of ab initio quantum chemistry introduction to Monte Carlo methods the variational Monte Carlo method quantum Monte Carlo exact Green's function methods released node methods excited states properties other than energy determination of interaction potentials, stationary geometries, energy derivatives valence-electron and acceleration methods.read more
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Quantum Monte Carlo simulations of solids
TL;DR: In this paper, the authors describe variational and fixed-node diffusion quantum Monte Carlo methods and how they may be used to calculate the properties of many-electron systems and describe a selection of applications to ground and excited states of solids and clusters.
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The theory of variational hybrid quantum-classical algorithms
TL;DR: Peruzzo et al. as mentioned in this paper developed a variational adiabatic ansatz and explored unitary coupled cluster where they established a connection from second order unitary cluster to universal gate sets through a relaxation of exponential operator splitting.
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Linear scaling electronic structure methods
TL;DR: In this paper, the physical decay properties of the density matrix were studied for both metals and insulators, and several strategies for constructing O(N) algorithms were presented and critically examined.
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Electronic structure and catalysis on metal surfaces
TL;DR: First principles calculations are discussed, which can aid in the establishment of chemisorption trends across the transition metals, in the characterization of reaction pathways on individual metals, and in the design of novel catalysts.
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Applications of Monte Carlo methods to statistical physics
TL;DR: An introductory review of the Monte Carlo method for the statistical mechanics of condensed matter systems is given in this paper, where basic principles (random number generation, simple sampling versus importance sampling, Markov chains and master equations) are explained and some classical applications (self-avoiding walks, percolation, the Ising model) are sketched.