The quantum Monte Carlo (QMC) method is a modern many-body computational methodology which proves very successful for the studies of systems with pronounced electron correlation effects. We describe basic procedures necessary to achieve chemical accuracy (1 kcal/mol) in the estimation of energy differences. In order to extend the capabilities of our QMC procedure, we recently initiated a new development aimed at the evaluation of interatomic forces in molecular systems within the QMC framework. Here we present our current progress and preliminary results.
The quantum Monte Carlo (QMC) method is a modern many-body computational methodology which proves very successful for the studies of systems with pronounced electron correlation effects. We describe basic procedures necessary to achieve chemical accuracy (1 kcal/mol) in the estimation of energy differences. In order to extend the capabilities of our QMC procedure, we recently initiated a new development aimed at the evaluation of interatomic forces in molecular systems within the QMC framework. Here we present our current progress and preliminary results.
Quantum Monte Carlo (QMC) is one of the most promising methods for solving quantum many-body problems. QMC combines known analytical properties of wave functions, results from other methods, and stochastic techniques, into a powerful tool for investigation of the electronic structure of real materials. Furthermore, the method has a favorable scaling ( O[N3] in the number of electrons), perfect scalability on parallel architectures, and can be applied to a wide range of systems. Our work has focused both on expanding the capabilities of QMC for calculation of quantities beyond energies, such as interatomic forces, and on application of the method to challenging problems in nanostructure materials research. For the calculation of interatomic forces we have implemented a finite difference correlated sampling method. The correlated sampling enables us to avoid the problem of statistical noise in evaluating the energy differences and allows us to obtain forces with an average 1% error. We have analyzed the effect of the method's main approximations on the estimate of the forces, both by theoretical arguments and by performing a variety of tests on small systems. Furthermore, we have used this newly developed method for estimating equilibrium geometries in a problem where other methods have failed. QMC enabled us to treat the electron correlation effects with high accuracy and to provide accurate predictions in a number of interesting applications. In particular, for carbon rings of C4N+2 stoichiometry we have resolved a long-standing problem regarding the competition between the aromaticity and second order Jahn-Teller effects. We have found that the dominant mechanism at small and intermediate sizes, which stabilizes the bond angle and bond length alternated geometries, is the secondorder Jahn-Teller effect while the aromatic isomer is always found to be a transition state. These high accuracy calculations which have involved multi-reference wave functions, extensive variational optimizations, and the use of massively parallel platforms demonstrate clearly the unique capabilities of the QMC approach.
The goals of our research have been to: (i) expand the applicability of the quantum Monte Carlo (QMC) methods to larger molecular systems and check the reliability of traditional approaches, (ii) determine the impact of correlation energy on a variety of systems in which correlation is important, (iii) investigate new silicon clusters which may generate unique materials, (iv) determine the most stable C$\sb{10}$ and C$\sb{20}$ clusters, (v) achieve kcal/mol accuracy for molecular reactions, and (vi) develop the QMC methodology by improving the QMC trial functions. In order to attain these goals, we have unambiguosly determined the role of correlation energy in silicon and carbon systems, calculated binding energies, barrier heights, heats of formation, and electron affinities for selected molecular systems with high accuracy, and gauged the performance and predictability of more than ten standard methods. We have also shown that the utilization of natural orbitals leads to a significant improvement in the quality of the trial wavefunctions. We demonstrate that, through a combination of advances in both accuracy and scaling, QMC is a highly attractive alternative to the traditional methods, and for some cases it appears to be the only currently available approach to provide parameter-free predictions for energies of systems with more than 40 electrons.
Electronic structure quantum Monte Carlo Quantum Monte Carlo (QMC) is an advanced simulation methodology for studies of manybody quantum systems. The QMC approaches combine analytical insights with stochastic computational techniques for efficient solution of several classes of important many-body problems such as the stationary Schrödinger equation. QMC methods of various flavors have been applied to a great variety of systems spanning continuous and lattice quantum models, molecular and condensed systems, BEC-BCS ultracold condensates, nuclei, etc. In this review, we focus on the electronic structure QMC, i.e., methods relevant for systems described by the electron-ion Hamiltonians. Some of the key QMC achievements include direct treatment of electron correlation, accuracy in predicting energy differences and favorable scaling in the system size. Calculations of atoms, molecules, clusters and solids have demonstrated QMC applicability to real systems with hundreds of electrons while providing 90-95% of the correlation energy and energy differences typically within a few percent of experiments. Advances in accuracy beyond these limits are hampered by the so-called fixed-node approximation which is used to circumvent the notorious fermion sign problem. Many-body nodes of fermion states and their properties have therefore become one of the important topics for further progress in predictive power and efficiency of QMC calculations. Some of our recent results on the wave function nodes and related nodal domain topologies will be briefly reviewed. This includes analysis of few-electron systems and descriptions of exact and approximate nodes using transformations and projections of the highly-dimensional nodal hypersurfaces into the 3D space. Studies of fermion nodes offer new insights into topological properties of eigenstates such as explicit demonstrations that generic fermionic ground states exhibit the minimal number of two nodal domains. Recently proposed trial wave functions based on Pfaffians with pairing orbitals are presented and their nodal properties are tested in calculations of first row atoms and molecules. Finally, backflow "dressed" coordinates are introduced as another possibility for capturing correlation effects and for decreasing the fixed-node bias.
Quantum Monte Carlo (QMC) methods are powerful, stochastic techniques for computing the properties of interacting electrons and nuclei with an accuracy comparable to the standard post-Hartree--Fock methods of quantum chemistry. Whilst the favourable scaling of QMC methods enables a quantum, many-body treatment of much larger systems, the lack of accurate and efficient total energy derivatives, required to compute atomic forces, has hindered their widespread adoption. The work contained within this thesis provides an efficient procedure for calculating exact derivatives of QMC results. This procedure uses the programming technique of algorithmic differentiation (AD), which allows access to the derivatives of a computed function by applying chain rule differentiation to the underlying source code. However, this thesis shows that a straightforward differentiation of a stochastic function fails to capture the important contribution to the derivative from probabilistic decisions. A general approach for calculating the derivatives of a stochastic function is presented, where a similar adaptation of AD applied to the diffusion Monte Carlo (DMC) algorithm yields exact DMC atomic forces. The approach is validated by performing the largest ever DMC force calculations, which demonstrate the feasibility of treating systems containing thousands of electrons. The efficiency of AD also enables molecular dynamics simulations driven entirely by DMC, adding new functionality to the QMC toolkit. Another focus of this thesis is using the phenomenon of stochastic coherence to correlate DMC simulations, allowing finite difference derivatives to be obtained with a small error. Whilst this method is far easier to implement than AD, preliminary results show an instability when treating larger systems. A different approach is obtained from extrapolating this method to a finite difference step size of zero, producing algebraic expressions for a direct differentiation of the DMC algorithm.
It has become increasingly feasible to use quantum Monte Carlo (QMC) methods to study correlated fermion systems for realistic Hamiltonians. We give a summary of these techniques targeted at researchers in the field of correlated electrons, focusing on the fundamentals, capabilities, and current status of this technique. The QMC methods often offer the highest accuracy solutions available for systems in the continuum, and, since they address the many-body problem directly, the simulations can be analyzed to obtain insight into the nature of correlated quantum behavior.
Quantum Monte Carlo (QMC) methods have received considerable attention over past decades due to their great promise for providing a direct solution to the many-body Schrodinger equation in electronic systems. Thanks to their low scaling with the number of particles, QMC methods present a compelling competitive alternative for the accurate study of large molecular systems and solid state calculations. In spite of such promise, the method has not permeated the quantum chemistry community broadly, mainly because of the fixed-node error, which can be large and whose control is difficult. In this Perspective, we present a systematic application of large scale multideterminant expansions in QMC and report on its impressive performance with first row dimers and the 55 molecules of the G1 test set. We demonstrate the potential of this strategy for systematically reducing the fixed-node error in the wave function and for achieving chemical accuracy in energy predictions. When compared to traditional quantum chemistry methods like MP2, CCSD(T), and various DFT approximations, the QMC results show a marked improvement over all of them. In fact, only the explicitly correlated CCSD(T) method with a large basis set produces more accurate results. Further developments in trial wave functions and algorithmic improvements appear promising for rendering QMC as the benchmark standard in large electronic systems.