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• W4205811555 abstract "The processes which occur after molecules absorb light underpin an enormous range of fundamental technologies and applications, including photocatalysis to enable new chemical transformations, sunscreens to protect against the harmful effects of UV overexposure, efficient photovoltaics for energy generation from sunlight, and fluorescent probes to image the intricate details of complex biomolecular structures. Reflecting this broad range of applications, an enormously versatile set of experiments are now regularly used to interrogate light-driven chemical dynamics, ranging from the typical ultrafast transient absorption spectroscopy used in many university laboratories to the inspiring central facilities around the world, such as the next-generation of X-ray free-electron lasers.Computer simulations of light-driven molecular and material dynamics are an essential route to analyzing the enormous amount of transient electronic and structural data produced by these experimental sources. However, to date, the direct simulation of molecular photochemistry remains a frontier challenge in computational chemical science, simultaneously demanding the accurate treatment of molecular electronic structure, nuclear dynamics, and the impact of nonadiabatic couplings.To address these important challenges and to enable new computational methods which can be integrated with state-of-the-art experimental capabilities, the past few years have seen a burst of activity in the development of direct quantum dynamics methods, merging the machine learning of potential energy surfaces (PESs) and nonadiabatic couplings with accurate quantum propagation schemes such as the multiconfiguration time-dependent Hartree (MCTDH) method. The result of this approach is a new generation of direct quantum dynamics tools in which PESs are generated in tandem with wave function propagation, enabling accurate on-the-fly simulations of molecular photochemistry. These simulations offer an alternative route toward gaining quantum dynamics insights, circumventing the challenge of generating ab initio electronic structure data for PES fitting by instead only demanding expensive energy evaluations as and when they are needed.In this Account, we describe the chronological evolution of our own contributions to this field, focusing on describing the algorithmic developments that enable direct MCTDH simulations for complex molecular systems moving on multiple coupled electronic states. Specifically, we highlight active learning strategies for generating PESs during grid-based quantum chemical dynamics simulations, and we discuss the development and impact of novel diabatization schemes to enable direct grid-based simulations of photochemical dynamics; these developments are highlighted in a series of benchmark molecular simulations of systems containing multiple nuclear degrees of freedom moving on multiple coupled electronic states. We hope that the ongoing developments reported here represent a major step forward in tools for modeling excited-state chemistry such as photodissociation, proton and electron transfer, and ultrafast energy dissipation in complex molecular systems." @default.
• W4205811555 created "2022-01-26" @default.
• W4205811555 creator A5029969579 @default.
• W4205811555 creator A5054262500 @default.
• W4205811555 date "2022-01-04" @default.
• W4205811555 modified "2023-10-01" @default.
• W4205811555 title "Predicting Molecular Photochemistry Using Machine-Learning-Enhanced Quantum Dynamics Simulations" @default.
• W4205811555 cites W1498377335 @default.
• W4205811555 cites W1964882117 @default.
• W4205811555 cites W1971362449 @default.
• W4205811555 cites W1971841581 @default.
• W4205811555 cites W1984510786 @default.
• W4205811555 cites W1988841270 @default.
• W4205811555 cites W1989061999 @default.
• W4205811555 cites W1993571434 @default.
• W4205811555 cites W1996982917 @default.
• W4205811555 cites W2001864887 @default.
• W4205811555 cites W2008931657 @default.
• W4205811555 cites W2010858159 @default.
• W4205811555 cites W2012149178 @default.
• W4205811555 cites W2021891874 @default.
• W4205811555 cites W2028201857 @default.
• W4205811555 cites W2042951945 @default.
• W4205811555 cites W2043300624 @default.
• W4205811555 cites W2043767213 @default.
• W4205811555 cites W2048195126 @default.
• W4205811555 cites W2055032509 @default.
• W4205811555 cites W2061791226 @default.
• W4205811555 cites W2064019417 @default.
• W4205811555 cites W2068259753 @default.
• W4205811555 cites W2068920655 @default.
• W4205811555 cites W2075523156 @default.
• W4205811555 cites W2081232414 @default.
• W4205811555 cites W2084937640 @default.
• W4205811555 cites W2110930696 @default.
• W4205811555 cites W2125410420 @default.
• W4205811555 cites W2134344701 @default.
• W4205811555 cites W2168051561 @default.
• W4205811555 cites W2168738696 @default.
• W4205811555 cites W2302414742 @default.
• W4205811555 cites W2316626263 @default.
• W4205811555 cites W2331104528 @default.
• W4205811555 cites W2332668224 @default.
• W4205811555 cites W2340440493 @default.
• W4205811555 cites W2416641138 @default.
• W4205811555 cites W2478347486 @default.
• W4205811555 cites W2491218394 @default.
• W4205811555 cites W2515801595 @default.
• W4205811555 cites W2537997428 @default.
• W4205811555 cites W2579529507 @default.
• W4205811555 cites W2582543345 @default.
• W4205811555 cites W2594315916 @default.
• W4205811555 cites W2598842853 @default.
• W4205811555 cites W2614682050 @default.
• W4205811555 cites W2738306938 @default.
• W4205811555 cites W2745303652 @default.
• W4205811555 cites W2753746018 @default.
• W4205811555 cites W2781623278 @default.
• W4205811555 cites W2786023940 @default.
• W4205811555 cites W2889435141 @default.
• W4205811555 cites W2900662840 @default.
• W4205811555 cites W2905224208 @default.
• W4205811555 cites W2907561373 @default.
• W4205811555 cites W2912834459 @default.
• W4205811555 cites W2914757272 @default.
• W4205811555 cites W2922505363 @default.
• W4205811555 cites W2939308250 @default.
• W4205811555 cites W2981194347 @default.
• W4205811555 cites W2983019972 @default.
• W4205811555 cites W2985674185 @default.
• W4205811555 cites W2994621057 @default.
• W4205811555 cites W3008668545 @default.
• W4205811555 cites W3016545710 @default.
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• W4205811555 cites W3022900486 @default.
• W4205811555 cites W3038485868 @default.
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• W4205811555 cites W3099978324 @default.
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• W4205811555 doi "https://doi.org/10.1021/acs.accounts.1c00665" @default.
• W4205811555 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34982533" @default.
• W4205811555 hasPublicationYear "2022" @default.
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