Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping

Li, Xusong; Xie, Yu; Da, Hu; Lan, Zhenggang

doi:10.1021/acs.jctc.7b00394

Cited by 52 publications

(53 citation statements)

References 103 publications

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“…Furthermore, Matute et al employed TD-DFT to predict the chromophore conformations, when they are bound to the proteins [21,45]. Also the photochemistry of related chromophore models was investigated by several groups [46,47,48,49] and more details on this topic, which involves an ultrafast double-bond isomerization, can be found in a recent review [50].…”

Section: Introductionmentioning

confidence: 99%

QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra

Wiebeler

Schapiro

2019

Molecules

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Cyanobacteriochromes are compact and spectrally diverse photoreceptor proteins that are promising candidates for biotechnological applications. Computational studies can contribute to an understanding at a molecular level of their wide spectral tuning and diversity. In this contribution, we benchmark methods to model a 110 nm shift in the UV/Vis absorption spectrum from a red- to a green-absorbing form of the cyanobacteriochrome Slr1393g3. Based on an assessment of semiempirical methods to describe the chromophore geometries of both forms in vacuo, we find that DFTB2+D leads to structures that are the closest to the reference method. The benchmark of the excited state calculations is based on snapshots from quantum mechanics/molecular mechanics molecular dynamics simulations. In our case, the methods RI-ADC(2) and sTD-DFT based on CAM-B3LYP ground state calculations perform the best, whereas no functional can be recommended to simulate the absorption spectra of both forms with time-dependent density functional theory. Furthermore, the difference in absorption for the lowest energy absorption maxima of both forms can already be modelled with optimized structures, but sampling is required to improve the shape of the absorption bands of both forms, in particular for the second band. This benchmark study can guide further computational studies, as it assesses essential components of a protocol to model the spectral tuning of both cyanobacteriochromes and the related phytochromes.

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Section: Introductionmentioning

confidence: 99%

QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra

Wiebeler

Schapiro

2019

Molecules

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“…This situation imposes significant challenges for analysis, which must be automatized, with clear quantitative classification criteria. [455][456][457] The second strategy to reduce computational costs commonly adopted is to downgrade the electronic structure level. NA-MQC dynamics are often run with small double- basis sets, with methods providing an incomplete treatment of electron correlation, as CASSCF (which misses dynamical electron correlation) or TDDFT (which misses nondynamical electron correlation).…”

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confidence: 99%

Recent Advances and Perspectives on Nonadiabatic Mixed Quantum–Classical Dynamics

2018

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Nonadiabatic mixed quantum-classical (NA-MQC) dynamics methods form a class of computational theoretical approaches in quantum chemistry tailored to investigate the time evolution of nonadiabatic phenomena in molecules and supramolecular assemblies. NA-MQC is characterized by a partition of the molecular system into two subsystems: one to be treated quantum mechanically (usually but not restricted to electrons) and another to be dealt with classically (nuclei). The two subsystems are connected through nonadiabatic couplings terms to enforce self-consistency. A local approximation underlies the classical subsystem, implying that direct dynamics can be simulated, without needing precomputed potential energy surfaces. The NA-MQC split allows reducing computational costs, enabling the treatment of realistic molecular systems in diverse fields. Starting from the three most well-established methods-mean-field Ehrenfest, trajectory surface hopping, and multiple spawning-this review focuses on the NA-MQC dynamics methods and programs developed in the last 10 years. It stresses the relations between approaches and their domains of application. The electronic structure methods most commonly used together with NA-MQC dynamics are reviewed as well. The accuracy and precision of NA-MQC simulations are critically discussed, and general guidelines to choose an adequate method for each application are delivered.

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“…This was mainly done in order to be compatible with the eventual generation of an LVC model based on the set of identified most important normal modes, in order to carry out MCTDH simulations in a way comparable to recent simulations. [29][30][31] Unlike the dimensionality-reduction techniques mentioned above (PCA, ISOMAP, diffusion map), 4,[11][12][13][14][15][16] our goal here is not to find an optimal arbitrary coordinate basis, but rather to identify the most important modes within the employed normal mode basis.…”

Section: B Normal Mode Transformationmentioning

confidence: 99%

“…11 One of the most convenient ways to generate such lowdimensional models is by applying some dimensionalityreduction techniques to full-dimensional AIMD trajectories. A significant body of literature was published on this topic, see, e.g., References 4,[11][12][13][14][15][16][17]. One of the most common approaches is to take the coordinate data from the trajectories, compute the covariance matrix, and perform a principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Important Normal Modes in Nonadiabatic Dynamics Simulations by Coherence, Correlation, and Frequency Analyses

Mai¹,

González²

2019

Preprint

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<div>Nonadiabatic dynamics simulations of molecular systems with a large number of nuclear degrees of freedom become increasingly feasible, but there is still a need to extract from such simulations a small number of most important modes of nuclear motion, for example to obtain general insight or to construct low-dimensional model potentials for further simulations. Standard techniques for this dimensionality reduction employ statistical methods that identify the modes that account for the largest variance in nuclear positions. However, large-amplitude motion is not necessarily a good proxy for the influence of a mode on the excited-state wave function evolution. Hence, here we report a number of analysis techniques aimed at extracting from nonadiabatic dynamics simulations the vibrational modes that are most strongly affected by the electronic excitation process and that most significantly affect the interaction of the electronic states. The first technique identifies coherent nuclear motion after excitation from the ratio between total variance and variance of the average trajectory. The second strategy employs linear regression to find normal modes that have a statistically significant effect on excitation energies, energy gaps, or wave function overlaps. The third approach uses time-frequency analysis to find normal modes where the vibrational frequencies change in the course of the dynamics simulation. All three techniques are applied to the case of surface hopping trajectories of [Re(CO)<sub>3</sub>(Im)(Phen)]<sup>+</sup> (Im=imidazole; Phen=1,10-phenanthroline), showing that in this transition metal complex the nonadiabatic dynamics is dominated by a small number of carbonyl and phenanthroline in-plane stretch modes.</div><div><br></div>

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Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping

Cited by 52 publications

References 103 publications

QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra

QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra

Recent Advances and Perspectives on Nonadiabatic Mixed Quantum–Classical Dynamics

Identification of Important Normal Modes in Nonadiabatic Dynamics Simulations by Coherence, Correlation, and Frequency Analyses

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