Multi‐Configuration Time‐Dependent Hartree Methods: From Quantum to Semiclassical and Quantum‐Classical

Bonfanti, Matteo; Worth, Graham A.; Burghardt, Irène

doi:10.1002/9781119417774.ch12

Cited by 12 publications

(11 citation statements)

References 87 publications

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“…Besides computational efficiency, VC models are attractive because of their conceptual simplicity and the low effort required to parametrize high-dimensional PESs from a few electronic structure calculations. , These features distinguish them from related approaches, such as machine learning , or interpolation of diabatic Hamiltonians, which can potentially deliver more accurate PESs but currently are more difficult to use. For these reasons, VC models have been extensively used in combination with the multiconfigurational time-dependent Hartree (MCTDH) method − for quantum dynamics (QD) studies and to obtain vibronic spectra of moderately sized stiff molecules. ,,, In recent years, VC models experienced a renaissance in the field of exciton dynamics , and in the context of the Frenkel–Holstein vibronic exciton Hamiltonian . However, MCTDH dynamics with VC potentials still formally scales exponentially, becoming infeasible for large systems.…”

Section: Introductionmentioning

confidence: 99%

Surface Hopping Dynamics on Vibronic Coupling Models

et al. 2021

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See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.vibronic coupling (LVC)-parametrized potential energy surfaces.• Zobel, J. P.; Knoll, T.; González, L. Ultrafast and longtime excited-state kinetics of an NIR emissive vanadium-(III) complex. II. Elucidating triplet-to-singlet excitedstate dynamics. Chem. Sci. 2021, 12, 10791−10801. 2 This work showcases SH/LVC simulations of an open-shell metal complex with a degenerate triplet ground state for several picoseconds in f ull dimensionality.

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Section: Introductionmentioning

confidence: 99%

Surface Hopping Dynamics on Vibronic Coupling Models

et al. 2021

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“…Long-lived coherent vibronic features as observed in the present model complex indeed appear in spatially extended systems with dense electronic manifolds, too. 40,51,65 As underscored by our previous studies, 11,52 more approximate quantum-classical approaches may fail for this type of systems. For these reasons, on-the-fly dynamics for these systems, which is often restricted to Ehrenfest molecular dynamics 66 or Surface-Hopping dynamics, 67 remain highly challenging.…”

Section: Conclusion and Perspectivementioning

confidence: 90%

“…More approximate methods like Ehrenfest dynamics fail entirely for this system, 11 while Multi-Configuration Ehrenfest (MCE) approaches lead to slow convergence for a limited number of modes (typically for 10-40 modes). 11,52 For similar numbers of modes, vMCG dynamics are difficult to converge, too, while the G-MCTDH approach works well, but becomes rapidly expensive. 52 Using 2L-GMCTDH, converged calculations could be performed up to 100 modes.…”

Section: Introductionmentioning

confidence: 99%

“…11,52 For similar numbers of modes, vMCG dynamics are difficult to converge, too, while the G-MCTDH approach works well, but becomes rapidly expensive. 52 Using 2L-GMCTDH, converged calculations could be performed up to 100 modes. 36 Here, we go significantly beyond these previous studies and demonstrate the application of the ML-GMCTDH approach for up to 300 modes.…”

Section: Introductionmentioning

confidence: 99%

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Multi-layer Gaussian-based multi-configuration time-dependent Hartree (ML-GMCTDH) simulations of ultrafast charge separation in a donor–acceptor complex

Maiolo

Worth

Burghardt

2021

The Journal of Chemical Physics

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We report on first applications of the Multi-Layer Gaussian-based Multi-Configuration Time-Dependent Hartree (ML-GMCTDH) method [Römer et al., J. Chem. Phys. 138, 064106 (2013)], beyond its basic two-layer variant. The ML-GMCTDH scheme provides an embedding of a variationally evolving Gaussian wavepacket basis into a hierarchical tensor representation of the wavefunction. A first principles parametrized model Hamiltonian for ultrafast non-adiabatic dynamics in an oligothiophene-fullerene charge transfer complex is employed, relying on a two-state linear vibronic coupling model which combines a distribution of tuning type modes with an intermolecular coordinate that also modulates the electronic coupling.Efficient ML-GMCTDH simulations are carried out for up to 300 vibrational modes, using an implementation within the QUANTICS program. Excellent agreement with reference ML-MCTDH calculations is obtained.

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“…The costs are negligible compared with the same-level QC calculation. 12,29 Thus, many studies have used ML potentials as a prominent accelerator for nonadiabatic molecular dynamics (NAMD) simulations, for instance, multiconfigurational time-dependent Hartree (MCTDH) 30 and trajectory surface hopping (TSH) 31 calculations. Several groups have developed ML mixed quantum−classical NAMD approaches (called ML photodynamics in this Account), such as MLAtom 32 with Newton-X 33 and SchNarc 34 with SHARC.…”

Section: Introductionmentioning

confidence: 99%

A Look Inside the Black Box of Machine Learning Photodynamics Simulations

Lopez

2022

Acc. Chem. Res.

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Metrics & MoreArticle Recommendations * sı Supporting Information CONSPECTUS: Photochemical reactions are of great importance in chemistry, biology, and materials science because they take advantage of a renewable energy source, mild reaction conditions, and high atom economy. Light absorption can excite molecules to a higher energy electronic state of the same spin multiplicity. The following nonadiabatic processes induce molecular transformations that afford exotic molecular architectures and high-energy-isomers that are inaccessible by thermal means. Computational simulations now complement time-resolved instrumentation to reveal ultrafast excited-state mechanistic information for photochemical reactions that is essential in disentangling elusive spectroscopic features, excitedstate lifetimes, and excited-state mechanistic critical points. Nonadiabatic molecular dynamics (NAMD), powered by surface hopping techniques, is among the most widely applied techniques to model the photochemical reactions of medium-sized molecules. However, the computational efficiency is limited because of the requisite thousands of multiconfigurational quantum-chemical calculations multiplied by hundreds of trajectories. Machine learning (ML) has emerged as a revolutionary force in computational chemistry to predict the outcome of the resource-intensive multiconfigurational calculations on the fly. An ML potential trained with a substantial set of quantum-chemical calculations can predict the energies and forces with errors under chemical accuracy at a negligible cost. The integration of ML potentials in NAMD dramatically extends the maximum simulation time scale by ∼10 000fold to the nanosecond regime.In this Account, we present a comprehensive demonstration of ML photodynamics simulations and summarize our most recent applications in resolving complex photochemical reactions. First, we address three fundamental components of ML techniques for photodynamics simulations: the quantum-chemical data set, the ML potential, and NAMD. Second, we describe best practices in building training data and our procedure toward training the ML photodynamics model with our recent literature contributions. We introduce a convenient training data generation scheme combining Wigner sampling and geometrical interpolation. It trains reliable and effective ML potentials suitable for subsequent active learning to detect undersampled data. We demonstrate how active learning automatically discovers new mechanistic pathways and reproduces experimental results. We point out that atomic permutation is an essential data augmentation approach to improve the learnability of distance-based molecular descriptors for highly symmetric molecules. Third, we demonstrate the utility of ML-photodynamics by showing the results of ML photodynamics simulations of (1) photo-torquoselective 4π disrotatory electrocyclic ring closing of norbornyl cyclohexadiene, which reveals a thermal conversion from experimentally unobserved intermediates to the reactant in 1 ns; (2) [2 + 2] ph...

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Multi‐Configuration Time‐Dependent Hartree Methods: From Quantum to Semiclassical and Quantum‐Classical

Cited by 12 publications

References 87 publications

Surface Hopping Dynamics on Vibronic Coupling Models

Surface Hopping Dynamics on Vibronic Coupling Models

Multi-layer Gaussian-based multi-configuration time-dependent Hartree (ML-GMCTDH) simulations of ultrafast charge separation in a donor–acceptor complex

A Look Inside the Black Box of Machine Learning Photodynamics Simulations

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