This paper considers Schrödinger 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 distribution on the boundary of a stopped random process with Feynman-Kac weights. Practical computations require in addition the explicit approximation of the normal derivative of the groundstate on the boundary. We then propose to use this formulation in the case of the so-called fixed node approximation of Fermion groundstates, defined by the bottom eigenelements of the Schrödinger operator of a Fermionic system with Dirichlet conditions on the nodes (the set of zeros) of an initially guessed skew-symmetric function. We show that shape derivatives of the fixed node energy vanishes if and only if either (i) the distribution on the nodes of the stopped random process is symmetric; or (ii) the nodes are exactly the zeros of a skew-symmetric eigenfunction of the operator. We propose an approximation of the shape derivative of the fixed node energy that can be computed with a Monte-Carlo algorithm, which can be referred to as Nodal Monte-Carlo (NMC). The latter approximation of the shape derivative also vanishes if and only if either (i) or (ii) holds.
@article{M2AN_2010__44_5_977_0, author = {Rousset, Mathias}, title = {On a probabilistic interpretation of shape derivatives of Dirichlet groundstates with application to Fermion nodes}, journal = {ESAIM: Mathematical Modelling and Numerical Analysis - Mod\'elisation Math\'ematique et Analyse Num\'erique}, volume = {44}, year = {2010}, pages = {977-995}, doi = {10.1051/m2an/2010049}, mrnumber = {2731400}, zbl = {pre05798940}, language = {en}, url = {http://dml.mathdoc.fr/item/M2AN_2010__44_5_977_0} }
Rousset, Mathias. On a probabilistic interpretation of shape derivatives of Dirichlet groundstates with application to Fermion nodes. ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique, Tome 44 (2010) pp. 977-995. doi : 10.1051/m2an/2010049. http://gdmltest.u-ga.fr/item/M2AN_2010__44_5_977_0/
[1] A pedagogical introduction to Quantum Monte Carlo, in Mathematical Models and Methods for Ab Initio Quantum Chemistry, M. Defranceschi and C. Le Bris Eds., Lecture Notes in Chemistry 74, Springer (2000). | Zbl 0992.81002
and ,[2] Zero-variance zero-bias principle for observables in quantum Monte Carlo: Application to forces. J. Chem. Phys. 119 (2003) 10536-10552.
and ,[3] Diffusion Monte-Carlo with a fixed number of walkers. Phys. Rev. E 61 (2000) 4566-4575.
, and ,[4] Total forces in the diffusion Monte Carlo method with nonlocal pseudopotentials. Phys. Rev. B 78 (2008) 035134.
and ,[5] Nodal Pulay terms for accurate diffusion quantum Monte Carlo forces. Phys. Rev. B 77 (2008) 085111.
, and ,[6] Quantum Monte-Carlo simulations of Fermions. A mathematical analysis of the fixed-node approximation. Math. Mod. Meth. Appl. Sci. 16 (2006) 1403-1440. | Zbl 1098.81095
, and ,[7] Méthodes mathématiques en chimie quantique : Une introduction. Springer-Verlag (2006). | Zbl 1167.81001
, and ,[8] Computing accurate forces in quantum Monte Carlo using Pulay's corrections and energy minimization. J. Chem. Phys. 118 (2003) 7193-7201.
, and ,[9] Fermion nodes. J. Stat. Phys. 63 (1991) 1237-1267.
,[10] Ground state of the electron gas by a stochastic method. Phys. Rev. Lett. 45 (1980) 566-569.
and ,[11] Monte-Carlo simulation of a many-fermion study. Phys. Rev. B 16 (1977) 3081-3099.
, and ,[12] Boundary sensitivities for diffusion processes in time dependent domains. Appl. Math. Optim. 54 (2006) 159-187. | Zbl 1109.49043
, and ,[13] Feynman-Kac Formulae, Genealogical and Interacting Particle Systems with Applications. Springer Series Probability and its Applications, Springer (2004). | Zbl 1130.60003
,[14] Branching and Interacting Particle Systems approximations of Feynman-Kac formulae with applications to nonlinear filtering. Lecture Notes Math. 1729 (2000) 1-145. | Numdam | Zbl 0963.60040
and ,[15] Particle approximations of Lyapounov exponents connected to Schrödinger operators and Feynman-Kac semigroups. ESAIM: PS 7 (2003) 171-208. | Numdam | Zbl 1040.81009
and ,[16] Sequential Monte-Carlo Methods in Practice. Series Statistics for Engineering and Information Science, Springer (2001). | Zbl 0967.00022
, and ,[17] Sequential Monte Carlo samplers. J. Roy. Stat. Soc. B 68 (2006) 411-436. | Zbl 1105.62034
, and ,[18] Correlated sampling in quantum Monte Carlo: A route to forces. Phys. Rev. B 61 (2000) R16291-R16294.
and ,[19] On the perurbation of eigenvalues for the p-laplacian. C. R. Acad. Sci. Paris, Sér. 1 332 (2001) 893-898. | Zbl 0989.35103
and ,[20] Elliptic Partial Differential Equation of Second Order. Springer-Verlag (1983). | Zbl 0562.35001
and ,[21] Monte Carlo Methods in ab initio quantum chemistry. World Scientific (1994).
, and ,[22] An improved algorithm of fixed-node quantum Monte Carlo method with self-optimization process. J. Mol. Struct. Theochem 726 (2005) 93-97.
and ,[23] Brownian motion and stochastic calculus, Graduate Texts in Mathematics 113. Second edition, Springer-Verlag, New York (1991). | Zbl 0734.60060
and ,[24] Perturbation theory for linear operators, Grundlehren der Mathematischen Wissenschaften 132. Second edition Springer-Verlag, Berlin (1976). | Zbl 0342.47009
,[25] Methods of modern mathematical physics. IV. Analysis of operators. Academic Press (Harcourt Brace Jovanovich Publishers), New York (1978). | Zbl 0242.46001
and ,[26] On the control of an interacting particle estimation of Schrödinger ground states. SIAM J. Math. Anal. 38 (2006) 824-844. | Zbl 1174.60045
,[27] Optimization of quantum Monte Carlo wave functions by energy minimization. J. Chem. Phys. 126 (2007) 084102.
and ,[28] Zero-variance zero-bias quantum Monte Carlo estimators of the spherically and system-averaged pair density. J. Chem. Phys. 126 (2007) 244112.
, and ,[29] Energy and variance optimization of many-body wave functions. Phys. Rev. Lett. 94 (2005) 150201.
and ,