Direct diabatization based on nonadiabatic couplings: the N/D method

Varga, Zoltán; Parker, Kelsey; Truhlar, Donald G.

doi:10.1039/c8cp03410a

Cited by 17 publications

(24 citation statements)

References 63 publications

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“…In addition, if a global diabatic representation were known, we could circumvent the heavy task of calculating such couplings around conical intersections; unfortunately, such a representation is not available directly and must be constructed with some diabatization scheme, which relies on the choice of a diabatic criterion. Among them, ab initio diabatization approaches are based on the properties of adiabatic electronic wave functions in terms of configurations [1][2][3][4][5][6][7][8][9][10][11][12][13] . Recently, a valence-bond-based compression approach for diabatization (VBCAD) has been proposed 14 ; it allows a low-size diabatic Hamiltonian matrix to be built automatically.…”

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confidence: 99%

Diabatization around Conical Intersections with a New Phase-Corrected Valence-Bond-Based Compression Approach

Zhang

Wang

Lasorne

et al. 2021

J. Phys. Chem. Lett.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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mentioning

confidence: 99%

Diabatization around Conical Intersections with a New Phase-Corrected Valence-Bond-Based Compression Approach

Zhang

Wang

Lasorne

et al. 2021

J. Phys. Chem. Lett.

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“…Today, a lot more variants exist. Examples are diabatization using explicit derivative couplings, , the propagation diabatization procedure, diabatization by localization, Procrustes diabatization, or diabatization by ansatz. ,, Further, methods can be based on couplings or other properties, − configuration uniformity, block-diagonalization, , a hybrid diagonalization combining block-diagonalization and diabatization by ansatz, , CI vectors, whereas some procedures are carried out at least partly using ML. ,,,, …”

Section: Quantum Chemical Theory and Methodsmentioning

confidence: 99%

Machine Learning for Electronically Excited States of Molecules

2020

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Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

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“…Examples are the propagation diabatization procedure, 348 diabatization by localization, 349 Procrustes diabatization 239 or diabatization by ansatz. 140,350 Further, methods can be based on couplings or other properties, [351][352][353][354] configuration uniformity, 355 blockdiagonalization, 356,357 CI vectors 358 or (partly) on ML. 140,141,350,[359][360][361][362]…”

Section: Diabatic Basismentioning

confidence: 99%

Machine learning for electronically excited states of molecules

Westermayr,

Marquetand

2020

Preprint

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Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on how machine learning is employed not only to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods, approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

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Direct diabatization based on nonadiabatic couplings: the N/D method

Abstract: We propose a new diabatization method that is direct, orbital-free, and adiabatic-equivalent based on directly calculated nonadiabatic couplings of states and the adiabatic energy gradients.

Cited by 17 publications

References 63 publications

Diabatization around Conical Intersections with a New Phase-Corrected Valence-Bond-Based Compression Approach

Diabatization around Conical Intersections with a New Phase-Corrected Valence-Bond-Based Compression Approach

Machine Learning for Electronically Excited States of Molecules

Machine learning for electronically excited states of molecules

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