Generalization of the local diabatization approach for propagating electronic degrees of freedom in nonadiabatic dynamics

Shakiba, Mohammad Reza; Akimov, Alexey V.

doi:10.1007/s00214-023-03007-7

Cited by 6 publications

(8 citation statements)

References 48 publications

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“…To account for possible state crossings and phase inconsistencies of the dynamical basis functions along the guiding trajectories, eq is integrated using the local diabatization approach: C ( t + Δ t ) = T 0.25em exp ( − E false( t false) + T + E false( t + normalΔ t false) T 2 ℏ Δ t ) C ( t ) …”

Section: Theory and Methodsmentioning

confidence: 99%

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Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics

Shakiba,

Akimov

2024

J. Chem. Theory Comput.

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In this work, we report a simple, efficient, and scalable machinelearning (ML) approach for mapping non-self-consistent Kohn−Sham Hamiltonians constructed with one kind of density functional to the nearly selfconsistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, ( 2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C 60 fullerene and Si 75 H 64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.

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Section: Theory and Methodsmentioning

confidence: 99%

Section: Theory and Methodsmentioning

confidence: 99%

Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics

Shakiba,

Akimov

2024

J. Chem. Theory Comput.

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“…The electronic propagation is conducted using a Trotter splitting approach and the generalized local diabatization scheme as implemented in Libra: , C ′ = U X F true( C ( t ) ; Δ t 2 true) C ( t ) C ″ = bold-italicT U MF false( normalΔ t false) C ′ C ( t + Δ t ) = U X F ( C ″ ; Δ t 2 ) C ″ Here, U MF and U XF are the evolution operators in the adiabatic basis corresponding to propagation according to the Ĥ BO and Ĥ XF operators, respectively, and computed as U M F ( Δ t ) = exp ( − i H B O ( t ) +<...…”

Section: Methodsmentioning

confidence: 99%

Nonadiabatic Dynamics with Exact Factorization: Implementation and Assessment

Han,

Akimov

2024

J. Chem. Theory Comput.

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In this work, we report our implementation of several independent-trajectory mixed-quantum-classical (ITMQC) nonadiabatic dynamics methods based on exact factorization (XF) in the Libra package for nonadiabatic and excited-state dynamics. Namely, the exact factorization surface hopping (SHXF), mixed quantum-classical dynamics (MQCXF), and mean-field (MFXF) are introduced. Performance of these methods is compared to that of several traditional surface hopping schemes, such as the fewest-switches surface hopping (FSSH), branching-corrected surface hopping (BCSH), and the simplified decay of mixing (SDM), as well as conventional Ehrenfest (mean-field, MF) method. Based on a comprehensive set of 1D model Hamiltonians, we find the ranking SHXF ≈ MQCXF > BCSH > SDM > FSSH ≫ MF, with the BCSH sometimes outperforming the XF methods in terms of describing coherences. Although the MFXF method can yield reasonable populations and coherences for some cases, it does not conserve the total energy and is therefore not recommended. We also find that the branching correction for auxiliary trajectories is important for the XF methods to yield accurate populations and coherences. However, the branching correction can worsen the quality of the energy conservation in the MQCXF. Finally, we find that using the time-dependent Gaussian width approximation used in the XF methods for computing decoherence correction can improve the quality of energy conservation in the MQCXF dynamics. The parameter-free scheme of Subotnik for computing the Gaussian widths is found to deliver the best performance in situations where such widths are not known a priori.

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“…Computing the nonadiabatic dynamics in atomistic and solid-state systems has been made possible by adopting quantum-classical trajectory surface hopping (TSH) techniques, ,− among which Tully’s fewest switches surface hopping (FSSH) has been one of the most popular choices due to its simplicity to implement and clear physical picture. Together with the FSSH, a number of other TSH schemes have been in use, aiming to address known deficiencies of the FSSH, such as its overcoherence − or its reliance on the ill-behaved nonadiabatic couplings (NACs). ,− Such methods replace the fully quantum mechanical treatment of nuclear evolution with a classical evolution while keeping the description of electronic degrees of freedom quantal. To account for quantum-mechanical branching, nuclear wavepackets are mimicked by swarms of classical or semiclassical trajectories, coupled or uncoupled to each other, evolving on the individual or effective potential energy surfaces (PES).…”

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confidence: 99%

“…Also note that i ( t + Δ t ) can change compared to i ( t ) adiabatically as a result of the trivial crossing resolution. This resolution is done based on the local diabatization reprojection matrix as detailed elsewhere Supporting Information.…”

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confidence: 99%

Generalization of the local diabatization approach for propagating electronic degrees of freedom in nonadiabatic dynamics

Cited by 6 publications

References 48 publications

Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics

Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics

Nonadiabatic Dynamics with Exact Factorization: Implementation and Assessment

Energy-Conserving and Thermally Corrected Neglect of Back-Reaction Approximation Method for Nonadiabatic Molecular Dynamics

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