Foundation of Multi‐Configurational Quantum Chemistry

Manni, Giovanni Li; Guther, Kai; Ma, Dongxia; Dobrautz, Werner

doi:10.1002/9781119417774.ch6

Cited by 17 publications

(31 citation statements)

References 93 publications

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“…The spin-free one-and two-body RDMs in terms of unitary group generators 127 are defined as (following the convention of Helgaker, Jørgensen, and Olsen 3 ) (7) with E ̂ij † = E ̂ji , and…”

Section: Guga-fciqmcmentioning

confidence: 99%

“…The complete active space self-consistent field (CASSCF) method is a well-established approach in quantum chemistry for the treatment of strongly correlated electron systems with substantial multireference character. − Important static correlation effects are rigorously described within the active space , consisting of the most important orbitals and electrons, while the effect of the environment (electrons not included in the active space) is accounted for at the mean-field level via a variational orbital optimization (the SCF procedure). One- and two-body reduced density matrices (1- and 2-RDMs) within the active space are necessary to perform orbital rotations between the active orbitals and the environment, whether a second-order Newton–Raphson formulation ,,− or the simplified super-CI technique with an average Fock operator is utilized .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Another strategy is to use methods that approximate the full-CI wave function in the active space, like the density matrix renormalization group approach (DMRG), − full configuration interaction quantum Monte Carlo (FCIQMC), ,− selected configuration interaction (selected-CI) approaches − (recently implemented in a spin-adapted form − ), the correlation energy extrapolation by intrinsic scaling method, , or the occupation restricted multiple active spaces (ORMAS), , as well as the related generalized active space (GAS) approach, − as CI eigensolvers within the CASSCF framework. However, while GAS was designed with the same GUGA framework discussed in the present work to enforce spin-adaptation, ORMAS is Slater-determinant-based and recently made use of the spin-flip configuration interaction method to ensure the correct spin multiplicity …”

Section: Introductionmentioning

confidence: 99%

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Spin-Pure Stochastic-CASSCF via GUGA-FCIQMC Applied to Iron–Sulfur Clusters

Dobrautz

Weser

Bogdanov

et al. 2021

J. Chem. Theory Comput.

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113

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In this work, we demonstrate how to efficiently compute the one- and two-body reduced density matrices within the spin-adapted full configuration interaction quantum Monte Carlo (FCIQMC) method, which is based on the graphical unitary group approach (GUGA). This allows us to use GUGA-FCIQMC as a spin-pure configuration interaction (CI) eigensolver within the complete active space self-consistent field (CASSCF) procedure and hence to stochastically treat active spaces far larger than conventional CI solvers while variationally relaxing orbitals for specific spin-pure states. We apply the method to investigate the spin ladder in iron–sulfur dimer and tetramer model systems. We demonstrate the importance of the orbital relaxation by comparing the Heisenberg model magnetic coupling parameters from the CASSCF procedure to those from a CI-only (CASCI) procedure based on restricted open-shell Hartree–Fock orbitals. We show that the orbital relaxation differentially stabilizes the lower-spin states, thus enlarging the coupling parameters with respect to the values predicted by ignoring orbital relaxation effects. Moreover, we find that, while CASCI results are well fit by a simple bilinear Heisenberg Hamiltonian, the CASSCF eigenvalues exhibit deviations that necessitate the inclusion of biquadratic terms in the model Hamiltonian.

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Section: Guga-fciqmcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spin-Pure Stochastic-CASSCF via GUGA-FCIQMC Applied to Iron–Sulfur Clusters

Dobrautz

Weser

Bogdanov

et al. 2021

J. Chem. Theory Comput.

Self Cite

113

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show abstract

“…In contrast to single-reference (SR) methods, which describe the ground state by a single configuration expressed by a Slater determinant, multiconfigurational (MC) methods consider more than one configuration/Slater determinant and multireference (MR) methods , use such linear combination of multiple configurations/Slater determinants for the ground state to generate further excitations. By construction, MR/MC approaches offer a more flexible description than the SR ansatz, and they are also capable of describing open-shell electronic ground states, which can frequently be encountered in transition metal complexes due to the partially filled d-shell of the metal atom, and they are also capable to describe ligand dissociation.…”

Section: Electronic Structure Methods For Dynamics Of Transition Meta...mentioning

confidence: 99%

The Quest to Simulate Excited-State Dynamics of Transition Metal Complexes

Zobel

González

2021

JACS Au

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This Perspective describes current computational efforts in the field of simulating photodynamics of transition metal complexes. We present the typical workflows and feature the strengths and limitations of the different contemporary approaches. From electronic structure methods suitable to describe transition metal complexes to approaches able to simulate their nuclear dynamics under the effect of light, we give particular attention to build a bridge between theory and experiment by critically discussing the different models commonly adopted in the interpretation of spectroscopic experiments and the simulation of particular observables. Thereby, we review all the studies of excited-state dynamics on transition metal complexes, both in gas phase and in solution from reduced to full dimensionality.

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In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary,

Campos‐Gonzalez‐Angulo,

Pablo‐García

et al. 2024

Advanced Materials

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Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

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Foundation of Multi‐Configurational Quantum Chemistry

Cited by 17 publications

References 93 publications

Spin-Pure Stochastic-CASSCF via GUGA-FCIQMC Applied to Iron–Sulfur Clusters

Spin-Pure Stochastic-CASSCF via GUGA-FCIQMC Applied to Iron–Sulfur Clusters

The Quest to Simulate Excited-State Dynamics of Transition Metal Complexes

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

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