A new algorithm is presented for the sparse representation and evaluation of Slater determinants in the quantum Monte Carlo (QMC) method. The approach, combined with the use of localized orbitals in a Slater-type orbital basis set, significantly extends the size molecule that can be treated with the QMC method. Application of the algorithm to systems containing up to 390 electrons confirms that the cost of evaluating the Slater determinant scales linearly with system size.
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http://dx.doi.org/10.1002/jcc.20205 | DOI Listing |
Nanomaterials (Basel)
January 2025
Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada.
Monte Carlo (MC) simulations have become important in advancing nanoparticle (NP)-based applications for cancer imaging and therapy. This review explores the critical role of MC simulations in modeling complex biological interactions, optimizing NP designs, and enhancing the precision of therapeutic and diagnostic strategies. Key findings highlight the ability of MC simulations to predict NP bio-distribution, radiation dosimetry, and treatment efficacy, providing a robust framework for addressing the stochastic nature of biological systems.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Departamento de Física, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso 2390123, Chile.
In this work, we study the magnetocaloric effect (MCE) in a working substance corresponding to a square lattice of spins with possible orientations, known as the "-state clock model". When the -state clock model has Q≥5 possible configurations, it presents the famous Berezinskii-Kosterlitz-Thouless (BKT) phase associated with vortex states. We calculate the thermodynamic quantities using Monte Carlo simulations for even numbers, ranging from Q=2 to Q=8 spin orientations per site in a lattice.
View Article and Find Full Text PDFJ Phys Chem Lett
January 2025
Department of Chemistry and Biochemistry, George Mason University, Fairfax, Virginia 22030, United States.
The simulation of non-Markovian quantum dynamics plays an important role in the understanding of charge and exciton dynamics in the condensed phase environment, yet such a simulation remains computationally expensive on classical computers. In this work, we develop a variational quantum algorithm that is capable of simulating non-Markovian quantum dynamics on quantum computers. The algorithm captures the non-Markovian effect by employing the Ehrenfest trajectories and Monte Carlo sampling of their thermal distribution.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Center for Computational Quantum Physics, The Flatiron Institute, 162 Fifth Avenue, New York, New York, 10010, United States.
We present a generalization of the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method to cavity quantum-electrodynamical (QED) matter systems. The method can be formulated in both the Coulomb and the dipole gauge. We verify its accuracy by benchmarking calculations on a set of small molecules against full configuration interaction and state-of-the-art QED coupled cluster (QED-CCSD) calculations.
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