Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2107.10994

High-Level Coupled-Cluster Energetics by Merging Moment Expansions with Selected Configuration Interaction

Karthik Gururangan·J. Emiliano Deustua·Jun Shen·Piotr Piecuch
View original source ↗Read source document ↗Export metadata ↓

Abstract

Inspired by our earlier semi-stochastic work aimed at converging high-level coupled-cluster (CC) energetics [J. E. Deustua, J. Shen, and P. Piecuch, Phys. Rev. Lett. 119, 223003 (2017); J. Chem. Phys. 154, 124103 (2021)], we propose a novel form of the CC($P$;$Q$) theory in which the stochastic Quantum Monte Carlo propagations, used to identify dominant higher-than-doubly excited determinants, are replaced by the selected configuration interaction (CI) approach using the CIPSI algorithm. The advantages of the resulting CIPSI-driven CC($P$;$Q$) methodology are illustrated by a few molecular examples, including the dissociation of $\mathrm{F_2}$ and the automerization of cyclobutadiene, where we recover the electronic energies corresponding to the CC calculations with a full treatment of singles, doubles, and triples based on the information extracted from compact CI wave functions originating from relatively inexpensive Hamiltonian diagonalizations.

Explore related subjects

physics.chem-phphysics.comp-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Karthik Gururangan, J. Emiliano Deustua, Jun Shen, Piotr Piecuch. 2021-10-13. High-Level Coupled-Cluster Energetics by Merging Moment Expansions with Selected Configuration Interaction. https://doi.org/10.1063/5.0064400

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks

Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them from limited simulation data remains a challenging, important problem. Separate short trajectories may sample different metastable states without capturing transitions or establishing their relative equilibrium populations. For unbiased trajectories generated with the same Hamiltonian at a single temperature, alternate methods based on histogram reweighting cannot correct this imbalance. Here we present SPIBER, which combines the State Predictive Information Bottleneck (SPIB) with Generative Flow Networks (GFlowNets). SPIB uses deep learning to approximate slow degrees of freedom through a past-future information bottleneck, retaining information needed to predict future metastable states. We show that this compression limits conditional entropy variations in populated regions, allowing conditional mean potential energies, which are much easier to calculate, to be used to approximate free energy differences. Given sufficient local sampling to estimate these energies, they define the target distribution for GFlowNets, energy-based generative samplers that sample according to estimated thermodynamic stability rather than observed populations. For a particle in a radial double-well potential, for alanine dipeptide, and for the nine-residue peptide AIB9, SPIBER recovers free energy differences between sampled metastable states to within one thermal energy unit of reference values. The method combines collective-variable learning and free energy estimation in up to four latent dimensions, without requiring converged state populations or additional molecular dynamics simulations.

physics.chem-ph↗
arXiv2026

Intrinsic Matching Frustration in Fluctuating Finite Systems

We formulate intrinsic matching frustration (IMF), a fluctuation-induced, kinetics-independent reduction in the mean capacity permitted by a prescribed matching rule. For complementary one-to-one matching, the instantaneous capacity is set by the minority population, so fluctuations produce a nonzero mean deficit even when the two populations are balanced on average. At finite size, this deficit depends on the full distribution of the population difference and is determined by its variance alone only in the Gaussian limit. Compartmentalization hides matching capacity by preventing cancellation between local imbalances of opposite sign. Fusion releases this hidden capacity monotonically under coarse graining, producing a measurable recovery of product yield following local reaction to completion.

physics.chem-ph↗
arXiv2026

Phonon chirality as an additive control of CISS: a symmetry-protected law

Chirality-induced spin selectivity (CISS) is usually associated with molecular handedness. The possible contribution of chiral phonons is less established. We study a helical tight-binding model in which local phonon angular momentum modulates spin-dependent nearest-neighbor hopping. Fewest-switches surface hopping calculations give the transmitted spin polarization $\mathrm{SP}=aC+b\mathrm{PH}$. Here $C$ is the molecular chirality and $\mathrm{PH}$ is the phonon chirality. A mirror symmetry reverses $C$, $\mathrm{PH}$, and $\mathrm{SP}$ simultaneously. This symmetry excludes both a chirality-independent offset and a $C\cdot\mathrm{PH}$ term. The phonon contribution can therefore enhance, cancel, or reverse the molecular CISS signal.

physics.chem-ph↗