P<scp>si</scp>4N<scp>um</scp>P<scp>y</scp>: An Interactive Quantum Chemistry Programming Environment for Reference Implementations and Rapid Development

Smith, Daniel G. A.; Burns, Lori A.; Sirianni, Dominic A.; Nascimento, Daniel R.; Kumar, Ashutosh; James, Andrew M.; Schriber, Jeffrey B.; Zhang, Tianyuan; Zhang, Boyi; Abbott, Adam S.; Berquist, Eric; Lechner, Marvin H.; Cunha, Leonardo A.; Heide, Alexander G.; Waldrop, Jonathan M.; Takeshita, Tyler Y.; Alenaizan, Asem; Neuhauser, Daniel; King, Rollin A.; Simmonett, Andrew C.; Turney, Justin M.; Schaefer, Henry F.; Evangelista, Francesco A.; DePrince, A. Eugene; Crawford, T. Daniel; Patkowski, Konrad; Sherrill, C. David

doi:10.1021/acs.jctc.8b00286

Cited by 143 publications

(144 citation statements)

References 25 publications

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“…50 For comparison, we also report results obtained using the orbitaloptimized Møller-Plesset method (pOMP2) and its reference OMP2 as implemented in Psi4. 54,55 The implementation of the pOMP2 approach follows closely the prescriptions described in ref., 39 where all the amplitudes with non-zero seniority 56 (the number of singly occupied orbitals in a determinant or an orbital configuration) are eliminated from CCD-Λ equations.…”

Section: Classical Electronic Structure Calculationsmentioning

confidence: 99%

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

Sokolov

Barkoutsos

Ollitrault

et al. 2020

The Journal of Chemical Physics

161

149

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The Coupled Cluster (CC) method is used to compute the electronic correlation energy in atoms and molecules and often leads to highly accurate results. However, its usual implementation in the projected form becomes nonvariational when the excitations are truncated and therefore fails to describe quantum states characterized by strong electronic correlations. Thanks to its exponential form, CC can be naturally adapted to quantum algorithms. In particular, the quantum unitary CC (q-UCC) is a popular wavefunction Ansatz for the Variational Quantum Eigensolver (VQE). The variational nature of this approach can lead to significant advantages compared to its classical equivalent (in the projected form), in particular for the description of strong electronic correlation. However, due to the large number of gate operations required in q-UCC, approximations need to be introduced in order to make this approach implementable in a stateof-the-art quantum computer. In this work, we propose several variants of the standard q-UCCSD Ansatz in which only a subset of excitations is included. In particular, we investigate the singlet and pair q-UCCD approaches combined with orbital optimization. We show that these approaches can capture the dissociation/distortion profiles of challenging systems such as H 4 , H 2 O and N 2 molecules, as well as the one-dimensional periodic Fermi-Hubbard chain. The results, which are in good agreement with the exact solutions, promote the future use of q-UCC methods for the solution of challenging electronic structure problems in quantum chemistry.

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Section: Classical Electronic Structure Calculationsmentioning

confidence: 99%

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

Sokolov

Barkoutsos

Ollitrault

et al. 2020

The Journal of Chemical Physics

161

149

View full text Add to dashboard Cite

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“…The training dataset was created by subsampling MD trajectories at constant temperature (500K) using the FHI-aims package [76]. In the case of keto-MDA, enol-MDA and ethanol we recomputed the training configurations using all-electron CCSD(T), while in the case of Aspirin we used all-electron CCSD [77][78][79](see Supporting Information for further detains).…”

Section: Molecular Dynamicsmentioning

confidence: 99%

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

Sauceda

Chmiela

Poltavsky

et al. 2019

The Journal of Chemical Physics

134

178

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We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the "gold standard" CCSD(T) method. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and n → π * interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.

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“…This provides a powerful hands-on technique for rapid ADC method development, debugging or interactive teaching sessions directly in the web browser. adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals=4) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals= [2,3]) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals=2, frozen_core=2) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals= [2,3], frozen_virtual=9) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals=4) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals= [2,3]) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals=2, frozen_core=2) adcc.cvs_adc2x(scfres, n_singlets=20, n_core_orbitals= [2,3], frozen_virtual=9) F1s and C1s in CVS C1s in CVS F1s frozen C1s in CVS, top 10% virtuals frozen Figure 6: Carbon K-edge X-ray absorption spectrum of 1,1-difluoroethene, using different subspaces in CVS as well as by freezing core and/or virtual orbitals. Commands for calculating these spectra are shown above the Figure.…”

Section: Comparison Of Adc Methodsmentioning

confidence: 99%

adcc: A versatile toolkit for rapid development of algebraic‐diagrammatic construction methods

Herbst

Scheurer

Fransson

et al. 2020

WIREs Comput Mol Sci

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ADC-connect (adcc) is a hybrid python/C++ module for performing excited state calculations based on the algebraic-diagrammatic construction scheme for the polarisation propagator (ADC). Key design goal is to restrict adcc to this single purpose and facilitate connection to external packages, e.g., for obtaining the Hartree-Fock references, plotting spectra, or modelling solvents. Interfaces to four self-consistent field codes have already been implemented, namely pyscf, psi4, molsturm, and veloxchem. The computational workflow, including the numerical solvers, are implemented in python, whereas the working equations and other expensive expressions are done in C++. This equips adcc with adequate speed, making it a flexible toolkit for both rapid development of ADC-based computational spectroscopy methods as well as unusual computational workflows. This is demonstrated by three examples. Presently, ADC methods up to third order in perturbation theory are available in adcc, including the respective core-valence separation and spin-flip variants. Both restricted or unrestricted Hartree-Fock references can be employed. GRAPHICAL TABLE OF CONTENTSadcc adcc: A versatile toolkit for research and teaching in computational spectroscopy based on the algebraic-diagrammatic construction scheme for the polarisation propagator (ADC).

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Psi4NumPy: An Interactive Quantum Chemistry Programming Environment for Reference Implementations and Rapid Development

Cited by 143 publications

References 25 publications

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents?

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

adcc: A versatile toolkit for rapid development of algebraic‐diagrammatic construction methods

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