Can we use on-the-fly quantum simulations to connect molecular structure and sunscreen action?

Richings, Gareth W.; Robertson, Christopher; Habershon, Scott

doi:10.1039/c8fd00228b

Cited by 16 publications

(28 citation statements)

References 38 publications

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“…16,17 The treatment of non-adiabatic dynamics (that is, involving transitions between more than one electronic states) within the KRR-based framework outlined above has also been demonstrated. Here, our approach to non-adiabatic dynamics (within the framework of either the standard grid-based method or MCTDH) builds on the same ideas as described above, using KRR to generate a global PES approximation; 16,17,19,29,30 however, in the case of nonadiabatic dynamics, we use KRR to generate a global description of the relevant diabatic states and their coupling terms. As described below, the diabatic representation usually affords electronic states which are smoother than the corresponding adiabatic representation and free of singularities; 1 such diabatic states are therefore more amenable to typical wavefunction-based quantum dynamics simulations.…”

Section: Introductionmentioning

confidence: 99%

Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes

Richings

Habershon

2020

J. Phys. Chem. A

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Section: Introductionmentioning

confidence: 99%

Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes

Richings

Habershon

2020

J. Phys. Chem. A

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“… 77 , 152 , 158 In these studies, between one and seven hidden layers with varying numbers of nodes were used. KRR methods were mainly applied to interpolate diabatic potentials 145 , 146 , 149 , 252 , 660 and in studies focusing on more than one molecular systems. 358 In general, only a few studies focused on extrapolation throughout chemical compound space in the excited states.…”

Section: Conclusion and Future Perspectivesmentioning

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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“…87,[262][263][264][265][266] Besides the PES generation itself, recent works use GPR to fit the diabatic PESs in reduced dimensions. [267][268][269][270] One of the largest ML-enhanced quantum dynamics simulation was recently performed on a 14-dimensional energy landscape for a mycosporine-like amino acid 271 .…”

Section: Please Cite This Article As Doi:101063/50047760mentioning

confidence: 99%

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr

Gastegger

Schütt³

et al. 2021

The Journal of Chemical Physics

131

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Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of highdimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties -be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.

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Can we use on-the-fly quantum simulations to connect molecular structure and sunscreen action?

Abstract: Direct MCTDH quantum dynamics simulations, with automatic active coordinate generation, applied to potential molecular sunscreens.

Cited by 16 publications

References 38 publications

Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes

Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes

Machine Learning for Electronically Excited States of Molecules

Perspective on integrating machine learning into computational chemistry and materials science

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