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We study lithium systems over a range of number of atoms, e.g., atomic anion, dimer, metallic cluster, and body-centered cubic crystal by the diffusion Monte Carlo method. The calculations include both core and valence electrons in order to…

Computational Physics · Physics 2015-07-29 Kevin Rasch , Lubos Mitas

We investigate how the fixed-node diffusion Monte Carlo energy of solids depends on single-particle orbitals used in Slater--Jastrow wave functions. We demonstrate that the dependence can be significant, in particular in the case of 3d…

Materials Science · Physics 2010-09-09 Jindrich Kolorenc , Shuming Hu , Lubos Mitas

By combining density-functional theory (DFT) and wave function theory (WFT) via the range separation (RS) of the interelectronic Coulomb operator, we obtain accurate fixed-node diffusion Monte Carlo (FN-DMC) energies with compact…

Chemical Physics · Physics 2020-11-04 Anthony Scemama , Emmanuel Giner , Anouar Benali , Pierre-François Loos

Diffusion Monte Carlo (DMC) is being recognized as a higher-accuracy, albeit more computationally expensive, alternative to Density Functional Theory (DFT) for energy predictions of catalytic systems. A major computational bottleneck in the…

Materials Science · Physics 2022-10-12 Gopal R. Iyer , Brenda M. Rubenstein

Wavefunction correction scheme, which was developed as a variance reduction tool for the pure and fixed-node diffusion Monte Carlo (DMC) computations by Anderson and Freihaut, is applied to the DMC computations of fermions without using the…

Computational Physics · Physics 2010-03-29 Nazim Dugan , Inanc Kanik , Sakir Erkoc

This paper considers Schr\"odinger operators, and presents a probabilistic interpretation of the variation (or shape derivative) of the Dirichlet groundstate energy when the associated domain is perturbed. This interpretation relies on the…

Numerical Analysis · Mathematics 2013-01-29 Mathias Rousset

We study several aspects of the recently introduced fixed-phase spin-orbit diffusion Monte Carlo (FPSODMC) method, in particular, its relation to the fixed-node method and its potential use as a general approach for electronic structure…

Computational Physics · Physics 2017-10-18 Cody A. Melton , Lubos Mitas

A diffusion Monte Carlo algorithm is introduced that can determine the correct nodal structure of the wave function of a few-fermion system and its ground-state energy without an uncontrolled bias. This is achieved by confining signed…

Computational Physics · Physics 2020-02-05 Alexander A. Kunitsa , So Hirata

Diffusion Monte Carlo (DMC) simulations for fermions are becoming the standard to provide high quality reference data in systems that are too large to be investigated via quantum chemical approaches. DMC with the fixed-node approximation…

Chemical Physics · Physics 2016-07-06 Andrea Zen , Sandro Sorella , Michael J. Gillan , Angelos Michaelides , Dario Alfè

Diffusion Monte Carlo (DMC) calculations typically yield highly accurate results in solid-state and quantum-chemical calculations. However, operators that do not commute with the Hamiltonian are at best sampled correctly up to second order…

Other Condensed Matter · Physics 2009-11-13 R. Gaudoin , J. M. Pitarke

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection…

Chemical Physics · Physics 2025-09-03 Kousuke Nakano , Benjamin X. Shi , Dario Alfè , Andrea Zen

We present a study of fixed and partial-node approximations in Slater determinant basis sets, using full configuration interaction quantum Monte Carlo (FCIQMC) to perform sampling. Walker annihilation in the FCIQMC method allows…

Chemical Physics · Physics 2021-09-27 Nick S. Blunt

We elucidate the origin of large differences (two-fold or more) in the fixed-node errors between the first- vs second-row systems for single-configuration trial wave functions in quantum Monte Carlo calculations. This significant difference…

Computational Physics · Physics 2015-06-17 Kevin M. Rasch , Shuming Hu , Lubos Mitas

This paper considers the problem of sampling from non-logconcave distribution, based on queries of its unnormalized density. It first describes a framework, Denoising Diffusion Monte Carlo (DDMC), based on the simulation of a denoising…

Machine Learning · Statistics 2024-10-31 Ye He , Kevin Rojas , Molei Tao

The disiloxane molecule is a prime example of silicate compounds containing the Si-O-Si bridge. The molecule is of significant interest within the field of quantum chemistry, owing to the difficulty in theoretically predicting its…

We have applied the many-body ab-initio diffusion quantum Monte Carlo (DMC) method to study Zn and ZnO crystals under pressure, and the energetics of the oxygen vacancy, zinc interstitial and hydrogen impurities in ZnO. We show that DMC is…

Materials Science · Physics 2015-05-01 Juan A. Santana , Jaron T. Krogel , Jeongnim Kim , Paul R. C. Kent , Fernando A. Reboredo

We evaluated the non-additive contributions of the inter-molecular interactions in B-DNA stacking by using diffusion Monte Carlo methods with fixed node approximations (FNDMC). For some base-pair steps, we found that their non-additive…

Chemical Physics · Physics 2019-11-19 Ken Sinkou Qin , Tom Ichibha , Kenta Hongo , Ryo Maezono

The bold diagrammatic Monte Carlo (BDMC) method performs an unbiased sampling of Feynman's diagrammatic series using skeleton diagrams. For lattice models the efficiency of BDMC can be dramatically improved by incorporating dynamic…

Statistical Mechanics · Physics 2015-05-20 L. Pollet , N. V. Prokof'ev , B. V. Svistunov

The dissociation energies of four transition metal dimers are determined using diffusion Monte Carlo. The Jastrow, CI, and molecular orbital parameters of the wave function are both partially and fully optimized with respect to the…

Chemical Physics · Physics 2019-09-06 Jil Ludovicy , Kaveh Haghighi Mood , Arne Lüchow

Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a…

Machine Learning · Computer Science 2026-05-14 Wessel L. van Nierop , Nir Shlezinger , Ruud J. G. van Sloun