Reduced‐scaling coupled cluster response theory: Challenges and opportunities

Crawford, T. Daniel; Kumar, Ashutosh; Bazanté, Alexandre P.; Remigio, Roberto Di

doi:10.1002/wcms.1406

Cited by 39 publications

(44 citation statements)

References 284 publications

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“…Furthermore, since its publication in 2018, 17 separate projects to date have leveraged the Psi4NumPy framework to facilitate their development of novel quantum chemical methods. [101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117] Finally, Psi4NumPy is a thoroughly community-driven project; interested readers are highly encouraged to visit the repository 100 for the latest version of Psi4NumPy and to participate in "pull request" code review, issue tracking, or by contributing code to the project itself.…”

Section: A Psi4numpymentioning

confidence: 99%

PSI4 1.4: Open-source software for high-throughput quantum chemistry

Smith

Burns

Simmonett

et al. 2020

The Journal of Chemical Physics

Self Cite

681

477

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Psi4 is a free and open-source ab initio electronic structure program providing Hartree-Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of Psi4's core functionality via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSchema data format, facilitating interoperability. A rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCArchive Infrastructure project, make the latest version of Psi4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs. File list (2) download file view on ChemRxiv psi4.pdf (4.37 MiB) download file view on ChemRxiv supplementary_material.pdf (297.86 KiB)

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Section: A Psi4numpymentioning

confidence: 99%

PSI4 1.4: Open-source software for high-throughput quantum chemistry

Smith

Burns

Simmonett

et al. 2020

The Journal of Chemical Physics

Self Cite

681

477

View full text Add to dashboard Cite

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“…However, the trend is consistent with that observed in non-relativistic CCSD based on the frozen natural orbitals for response properties 40 . The source of the error in the response properties for the FNS based coupled cluster method is the "fundamental dichotomy" between the nature of electron correlation and field-response properties 41 . The accuracy of field-response properties strongly depend upon the number of diffuse functions 42 in the basis set.…”

Section: B Dipole Momentmentioning

confidence: 99%

“…The correlation energy, on the other hand, depends primarily on the higher angular momentum functions in the basis set 43 . Following the argument as in the case of their nonrelativistic counterparts 41 for field-response properties, one can argue that the virtual natural spinors with the highest diffuse character will have the lowest occupation number and will be removed first during the truncation of the virtual space in the FNS procedure. On the other hand, the canonical virtual spinors with the highest diffuse character will correspond to the lowest orbital energies, and thus they will be removed last when the virtual space is truncated in the canonical basis.…”

Section: B Dipole Momentmentioning

confidence: 99%

A lower scaling four-component relativistic coupled cluster method based on natural spinors

Chamoli,

Surjuse,

Nayak

et al. 2022

Preprint

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We present the theory, implementation, and benchmark results for a frozen natural spinors-based lower scaling four-component relativistic coupled cluster method. The natural spinors are obtained by diagonalizing the one-body reduced density matrix from a relativistic MP2 calculation based on four-component Dirac-Coulomb Hamiltonian. The correlation energy in the coupled cluster method converges more rapidly with respect to the size of the virtual space in the frozen natural spinor basis than that observed in the standard canonical spinors obtained from the Dirac-Hartree-Fock calculation. The convergence of properties is not smooth in the frozen natural spinor basis. However, the inclusion of the perturbative correction smoothens the convergence of the properties with respect to the size of the virtual space in the frozen natural spinor basis and greatly reduces the truncation errors for both energy and properties calculations. The accuracy of the frozen natural spinor based coupled cluster methods can be controlled by a single threshold and is a black box to use.

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“…Using these, second-order properties are computed in CC-LR as products of property integrals with perturbed one-electron reduced density matrices. 47 Contributions from one operator are folded into the 1-RDM, and contributions from the other are described by the property integrals. In the context of the DTR, the density representation would now require only this perturbed 1-RDM with no additional changes.…”

Section: Additional Propertiesmentioning

confidence: 99%

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

Peyton¹,

Briggs²,

D’Cunha³

et al. 2020

Preprint

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<div> <div> <div> <p>The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of train- ing data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system. </p> </div> </div> </div>

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Reduced‐scaling coupled cluster response theory: Challenges and opportunities

Cited by 39 publications

References 284 publications

PSI4 1.4: Open-source software for high-throughput quantum chemistry

PSI4 1.4: Open-source software for high-throughput quantum chemistry

A lower scaling four-component relativistic coupled cluster method based on natural spinors

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

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