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Springer eBooks

Theory of Diffusion Monte Carlo Method

Ryo Maezono

Published Nov 8, 2025• 0 citations
Publisher
DOI
10.1007/978-981-95-4134-8_7

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Speeding Up and Quantifying Approximation Error in Continuum Quantum Monte Carlo Solid-State Calculations

William D. Parker

Quantum theory has successfully explained the mechanics of much of the microscopic world.However, Schrödinger's equations are difficult to solve for many-particle systems.Meanfield theories such as Hartree-Fock and density functional theory account for much of the total energy of electronic systems but fail on the crucial correlation energy that predicts solid cohesion and material properties.Monte Carlo methods solve differential and integral equations with error independent of the number of dimensions in the problem.Variational Monte Carlo (VMC) applies the variational principle to optimize the wave function used in the Monte Carlo integration of Schrödinger's time-independent equation.Diffusion Monte Carlo (DMC) represents the wave function by electron configurations diffusing stochastically in imaginary time to the ground state.Approximations in VMC and DMC make the problem tractable but introduce error in parameter-controlled and uncontrolled ways.The many-electron wave function consists of single-particle orbitals.The orbitals are combined in a functional form to account for electron exchange and correlation.Plane waves are a convenient basis for the orbitals.However, plane-wave orbitals grow in evaluation cost with basis-set completeness and system size.To speed up the calculation, polynomials approximate the plane-wave sum.Four polynomial methods tested are: Lagrange interpolation, pp-spline interpolation, B-spline interpolation and B-spline approximation.The polynomials all increase speed by an order of the number of particles.B-spline approximation most consistently maintains accuracy in the seven systems tested.However, polynomials increase the memory needed by a factor of two to eight.B-spline approximation with a separate approximation for the Laplacian of List of TablesTable Page 5.1 DMC number of steps vs. statistical uncertainty . . . . . . . . . . . . . . .5.2 Percent VMC time evaluating plane waves in Si . . . . . . . . . . . . . . . .5.3 Orbital, energy and fluctuation error in three metals . . . . . . . . . . . . .5.4 Orbital, energy and fluctuation error in two semiconductors . . . . . . . . .5.5 Orbital, energy and fluctuation error in two insulators . . . . . . . . . . . .6.1 DFT, VMC and DMC total & formation energies vs. plane-wave cutoff . .6.2 DMC energy vs. configuration population . . . . . . . . . . . . . . . . . . .6.3 DMC energy vs. time step . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.4 DMC total and formation energies at L-and Baldereschi points . . . . . . .6.5 DMC total and formation energies with finite-size corrections . . . . . . . .6.6 DMC total and formation energies vs. number of atoms . . . . . . . . . . .6.7 DMC total and formation energies with different pseudopotential methods .6.8 DFT & DMC total energies with different exchange-correlation potentials .6.9 DMC formation energies for varying Jastrow factors and pseudopotentials .6.10 DMC error summary . . . . . . . . . . . . . . .

•Jan 1, 2010•0 citations
Quantum theory has successfully explained the mechanics of much of the microscopic world.However, Schrödinger's equations are difficult to solve for many-particle systems.Meanfield theories such as Hartree-Fock and density functional theory account for much of the total energy of electronic systems but fail on the crucial correlation energy that predicts solid cohesion and material properties.Monte Carlo methods solve differential and integral equations with error independent of the number of dimensions in the problem.Variational Monte Carlo (VMC) applies the variational principle to optimize the wave function used in the Monte Carlo integration of Schrödinger's time-independent equation.Diffusion Monte Carlo (DMC) represents the wave function by electron configurations diffusing stochastically in imaginary time to the ground state.Approximations in VMC and DMC make the problem tractable but introduce error in parameter-controlled and uncontrolled ways.The many-electron wave function consists of single-particle orbitals.The orbitals are combined in a functional form to account for electron exchange and correlation.Plane waves are a convenient basis for the orbitals.However, plane-wave orbitals grow in evaluation cost with basis-set completeness and system size.To speed up the calculation, polynomials approximate the plane-wave sum.Four polynomial methods tested are: Lagrange interpolation, pp-spline interpolation, B-spline interpolation and B-spline approximation.The polynomials all increase speed by an order of the number of particles.B-spline approximation most consistently maintains accuracy in the seven systems tested.However, polynomials increase the memory needed by a factor of two to eight.B-spline approximation with a separate approximation for the Laplacian of List of TablesTable Page 5.1 DMC number of steps vs. statistical uncertainty . . . . . . . . . . . . . . .5.2 Percent VMC time evaluating plane waves in Si . . . . . . . . . . . . . . . .5.3 Orbital, energy and fluctuation error in three metals . . . . . . . . . . . . .5.4 Orbital, energy and fluctuation error in two semiconductors . . . . . . . . .5.5 Orbital, energy and fluctuation error in two insulators . . . . . . . . . . . .6.1 DFT, VMC and DMC total & formation energies vs. plane-wave cutoff . .6.2 DMC energy vs. configuration population . . . . . . . . . . . . . . . . . . .6.3 DMC energy vs. time step . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.4 DMC total and formation energies at L-and Baldereschi points . . . . . . .6.5 DMC total and formation energies with finite-size corrections . . . . . . . .6.6 DMC total and formation energies vs. number of atoms . . . . . . . . . . .6.7 DMC total and formation energies with different pseudopotential methods .6.8 DFT & DMC total energies with different exchange-correlation potentials .6.9 DMC formation energies for varying Jastrow factors and pseudopotentials .6.10 DMC error summary . . . . . . . . . . . . . . .