The development of a combined Quantum Monte Carlo (QMC) - Effective Fragment Molecular Orbital (EFMO) method is described. The combined QMC-EFMO method inherits the advantages of both methods: the high accuracy of the QMC computational results and the favourable computational scaling due to the EFMO fragmentation of large systems. The combined QMC-EFMO method is illustrated for several water-containing clusters.
The development of a combined Quantum Monte Carlo (QMC) - Effective Fragment Molecular Orbital (EFMO) method is described. The combined QMC-EFMO method inherits the advantages of both methods: the high accuracy of the QMC computational results and the favourable computational scaling due to the EFMO fragmentation of large systems. The combined QMC-EFMO method is illustrated for several water-containing clusters.
We present the quantum effective fragment molecular orbital (Q-EFMO) method, a hybrid framework that evaluates selected molecular-fragment correlation energies with the variational quantum eigensolver and assembles them through the size-consistent EFMO energy expression. A Hartree-Fock EFMO calculation supplies monomer references, many-body polarization, and distant-pair EFP interactions; independent VQE/UCCSD calculations provide correlation increments for monomers and selected near-field dimers. Consequently, the maximum quantum register is determined by the largest active fragment or fragment pair rather than by total system size. For a three-layer LiH benchmark in STO-3G, a full 2.5 to 3.5 Angstrom separation scan shows that two orbital-reduction schemes approach the full CCSD reference monotonically. The more aggressive scheme remains below 1 kcal/mol for separations of 2.6 Angstrom and greater and reaches 0.10 kcal/mol at 3.5 Angstrom, using 6 qubits for monomers and 12 for the largest dimers, compared with 14 qubits for the frozen-core dimer baseline. An implementation-specific cost proxy decreases from 1000 to 40, a factor of 25. These noise-free results establish the equations, software path, and resource scaling; broader chemical validation, larger bases, and hardware tests remain necessary.
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.
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.
Various strategies to implement efficiently quantum Monte Carlo (QMC) simulations for large chemical systems are presented. These include: (i) the introduction of an efficient algorithm to calculate the computationally expensive Slater matrices. This novel scheme is based on the use of the highly localized character of atomic Gaussian basis functions (not the molecular orbitals as usually done), (ii) the possibility of keeping the memory footprint minimal, (iii) the important enhancement of single-core performance when efficient optimization tools are used, and (iv) the definition of a universal, dynamic, fault-tolerant, and load-balanced framework adapted to all kinds of computational platforms (massively parallel machines, clusters, or distributed grids). These strategies have been implemented in the QMC=Chem code developed at Toulouse and illustrated with numerical applications on small peptides of increasing sizes (158, 434, 1056, and 1731 electrons). Using 10-80 k computing cores of the Curie machine (GENCI-TGCC-CEA, France), QMC=Chem has been shown to be capable of running at the petascale level, thus demonstrating that for this machine a large part of the peak performance can be achieved. Implementation of large-scale QMC simulations for future exascale platforms with a comparable level of efficiency is expected to be feasible.
Over the past two decades, continuum quantum Monte Carlo (QMC) has proved to be an invaluable tool for predicting of the properties of matter from fundamental principles. By solving the Schrödinger equation through a stochastic projection, it achieves the greatest accuracy and reliability of methods available for physical systems containing more than a few quantum particles. QMC enjoys scaling favorable to quantum chemical methods, with a computational effort which grows with the second or third power of system size. This accuracy and scalability has enabled scientific discovery across a broad spectrum of disciplines. The current methods perform very efficiently at the terascale. The quantum Monte Carlo Endstation project is a collaborative effort among researchers in the field to develop a new generation of algorithms, and their efficient implementations, which will take advantage of the upcoming petaflop architectures. Some aspects of these developments are discussed here. These tools will expand the accuracy, efficiency and range of QMC applicability and enable us to tackle challenges which are currently out of reach. The methods will be applied to several important problems including electronic and structural properties of water, transition metal oxides, nanosystems and ultracold atoms.