Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

Li, Jingbai; Reiser, Patrick; Boswell, Benjamin R.; Eberhard, André; Burns, Noah Z.; Friederich, Pascal; Lopez, Steven A.

doi:10.1039/d0sc05610c

Cited by 69 publications

(103 citation statements)

References 49 publications

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“…41,[43][44][45] The coupling vectors tend to infinity at zero energy gaps, therefore it is also helpful to train the model on these vectors multiplied by the energy gap. 41,[43][44][45]140 The problem of the arbitrary phase of the coupling vectors can be addressed by either correcting the phase before the ML training 41,140 or using ML itself to select the phases that lead to best predictions during the training process. 44 For nonadiabatic dynamics involving intersystem crossing (transition between states of different spin multiplicities), one may also need to learn spin-orbit couplings.…”

Section: [H2] Nonadiabatic Dynamicsmentioning

confidence: 99%

Molecular excited states through a machine learning lens

2021

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Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical (QM) methods. Advances in machine learning (ML) open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state-ofthe-art and highlight the critical issues to solve in the future. We overview a broad range of ML applications in excited-state research, which include the prediction of molecular properties, improvements of QM methods for the calculations of excited-state properties and the search of new materials. ML approaches can help us understand hidden factors that influence photo-This is a post-peer-review, pre-copyedit version of an article published in Nature Reviews Chemistry.

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Section: [H2] Nonadiabatic Dynamicsmentioning

confidence: 99%

Molecular excited states through a machine learning lens

2021

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“…All simulation times taken from studies of transition metal complexes, except surface hopping/machine learning studies which were applied to organic molecules. 243 , 244 MCTDH = multiconfigurational time-dependent Hartree. AIMS = ab initio multiple spawning.…”

Section: Bridging Time Scalesmentioning

confidence: 99%

“… 251 , 252 In contrast, dynamics based on ML featuring neural networks seem to handle these problematic regions better. 243 , 244 , 253 , 254 …”

Section: Bridging Time Scalesmentioning

confidence: 99%

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The Quest to Simulate Excited-State Dynamics of Transition Metal Complexes

Zobel

González

2021

JACS Au

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This Perspective describes current computational efforts in the field of simulating photodynamics of transition metal complexes. We present the typical workflows and feature the strengths and limitations of the different contemporary approaches. From electronic structure methods suitable to describe transition metal complexes to approaches able to simulate their nuclear dynamics under the effect of light, we give particular attention to build a bridge between theory and experiment by critically discussing the different models commonly adopted in the interpretation of spectroscopic experiments and the simulation of particular observables. Thereby, we review all the studies of excited-state dynamics on transition metal complexes, both in gas phase and in solution from reduced to full dimensionality.

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“…3 Machine learning (ML) approaches have the potential to revolutionise force-field based simulations, aiming to provide the best of both worlds, [4][5][6] and have indeed begun to provide new insights into a range of challenging research problems. [7][8][9][10][11][12][13][14][15][16] The development of an ML potential applicable to the whole periodic table mapping nuclear coordinates to total energies and forces is, however, precluded by the curse of dimensionality. Within small chemical subspaces, models can be achieved using neural networks (NNs), 6,[17][18][19][20][21] kernel-based methods such as the Gaussian Approximation Potential (GAP) framework 22,23 or gradient-domain machine learning (GDML), 24 and linear fitting with properly chosen basis functions, 25,26 each with different data requirements and transferability.…”

Section: Introductionmentioning

confidence: 99%