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ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [J. Chem. Theory Comput., 2022, 19(1): 109-121]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations, allowing for multi-GPU simulations of large systems. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals, which otherwise would not have fit on a single GPU. We also support GPU-accelerated multi-slater determinant trial wavefunctions [arXiv:2406.08314] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu$_2$O$_2$]$^{2+}$ and [Fe$_2$S$_2$(SCH$_3$)]$^{2-}$. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.
Hedin's equations provide an elegant route to compute the exact one-body Green's function (or propagator) via the self-consistent iteration of a set of non-linear equations. Its first-order approximation, known as $GW$, corresponds to a resummation of ring diagrams and has shown to be extremely successful in physics and chemistry. Systematic improvement is possible, although challenging, via the introduction of vertex corrections. Considering anomalous propagators and an external pairing potential, we derive a new self-consistent set of closed equations equivalent to the famous Hedin equations but having as a first-order approximation the particle-particle (pp) $T$-matrix approximation where one performs a resummation of the ladder diagrams. This pp version of Hedin's equations offers a way to go systematically beyond the $T$-matrix approximation by accounting for low-order pp vertex corrections.
The Bethe–Salpeter equation (BSE) is the key equation in many-body perturbation theory based on Green's functions to access response properties. Within the GW approximation to the exchange-correlation kernel, the BSE has been successfully applied to several finite and infinite systems. However, it also shows some failures, such as underestimated triplet excitation energies, lack of double excitations, ground-state energy instabilities in the dissociation limit, etc. In this work, we study the performance of the BSE within the GW approximation as well as the T-matrix approximation for the excitation energies of the exactly solvable asymmetric Hubbard dimer. This model allows one to study various correlation regimes by varying the on-site Coulomb interaction U as well as the degree of the asymmetry of the system by varying the difference of potential Δv between the two sites. We show that, overall, the GW approximation gives more accurate excitation energies than GT over a wide range of U and Δv. However, the strongly correlated (i.e., large U) regime still remains a challenge.
We introduce a novel algorithm that leverages stochastic sampling techniques to compute the perturbative triples correction in the coupled-cluster (CC) framework. By combining elements of randomness and determinism, our algorithm achieves a favorable balance between accuracy and computational cost. The main advantage of this algorithm is that it allows for the calculation to be stopped at any time, providing an unbiased estimate, with a statistical error that goes to zero as the exact calculation is approached. We provide evidence that our semi-stochastic algorithm achieves substantial computational savings compared to traditional deterministic methods. Specifically, we demonstrate that a precision of 0.5 millihartree can be attained with only 10\% of the computational effort required by the full calculation. This work opens up new avenues for efficient and accurate computations, enabling investigations of complex molecular systems that were previously computationally prohibitive.
Sujets
Pesticide
Atomic and molecular structure and dynamics
Ab initio calculation
Corrélation électronique
3470+e
Electron electric moment
Electron correlation
X-ray spectroscopy
Pesticides Metabolites Clustering Molecular modeling Environmental fate Partial least squares
A posteriori Localization
Basis set requirements
Electron electric dipole moment
Relativistic corrections
Abiotic degradation
Aimantation
États excités
Spin-orbit interactions
Atomic charges
New physics
Analytic gradient
Single-core optimization
Relativistic quantum chemistry
Configuration interactions
Mécanique quantique relativiste
Dirac equation
Polarizabilities
BIOMOLECULAR HOMOCHIRALITY
Configuration interaction
CP violation
3115am
Carbon Nanotubes
AB-INITIO CALCULATION
Parity violation
Atomic charges chemical concepts maximum probability domain population
Argile
Adiabatic connection
3115ae
BSM physics
Auto-énergie
Parallel speedup
Azide Anion
Chimie quantique
CIPSI
Valence bond
3115bw
3115aj
Relativistic quantum mechanics
3115ag
Density functional theory
Wave functions
Approximation GW
Atomic data
Atrazine
Time-dependent density-functional theory
Argon
Time reversal violation
A priori Localization
Atomic processes
Molecular descriptors
3115vj
Ground states
Dipole
Dispersion coefficients
BENZENE MOLECULE
QSAR
Petascale
Atrazine-cations complexes
Xenon
Perturbation theory
Acrolein
Quantum chemistry
Configuration Interaction
Biodegradation
Atomic and molecular collisions
Excited states
Numerical calculations
Large systems
Quantum Monte Carlo
Diatomic molecules
Rydberg states
3315Fm
AB-INITIO
Hyperfine structure
Quantum Chemistry
Line formation
Diffusion Monte Carlo
AROMATIC-MOLECULES
Atom
ALGORITHM
Ion
Coupled cluster calculations
Atoms
Anderson mechanism
Range separation
3115vn
Coupled cluster
Green's function
Molecular properties
Chemical concepts
Fonction de Green