Deep learning study of tyrosine reveals that roaming can lead to photodamage

Westermayr, Julia; Gastegger, Michael; Vörös, Dóra; Panzenboeck, Lisa; Joerg, Florian; González, Leticia; Marquetand, Philipp

doi:10.1038/s41557-022-00950-z

Cited by 36 publications

(32 citation statements)

References 65 publications

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“…The correlation between the photodegradation kinetics-based dFe speciation and phytoplankton uptake-based dFe bioavailability is used for the test of the BDIS validation. Density functional theory (DFT) is a quantum chemical method 29 for studying the electronic structure of molecules. Based on DFT, the geometry optimization and chemical description for the molecular structures of 12 species of Fe−organic ligands (1:1) are performed; using the Gaussian09 program 30 package with B3LYP exchange−correlation functional and 6-311+G** basis set, the configurational formation energy (CFE) of dFe complexes is calculated.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Luo

Huang

et al. 2022

Anal. Chem.

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Via the photodegradation of dissolved iron (dFe) complexes in the euphotic zone, released free Fe(III) is the most important source of bioavailable iron for eukaryotic phytoplankton. There is an urgent need to establish bioavailability-based dissolved iron speciation (BDIS) methods. Herein, an intelligent system with dFe pretreatment and a colorimetric sensor is developed for real-time monitoring of newly generated Fe(III) ions. According to the photodegradation kinetics of dFe, including kinetic constant and photogenerated time of free Fe(III) ions, 3 sources, 6 kinds, and 12 species of dFe are determined by our photocatalytic-assisted colorimetric sensor and deep learning model within 20.0 min. The algal dFe-uptake for 4 days can be predicted by BDIS with correlation coefficient 0.85, which could be explained by the hard and soft acids and bases theory (HSAB) and density functional theory (DFT). These results successfully demonstrate the proof-of-concept for photodegradation kinetics-based speciation and bioavailability assessments of dissolved metals.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Luo

Huang

et al. 2022

Anal. Chem.

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“…Marquetand and coworkers applied the SchNarc approach to investigate the photodissociation reaction of tyrosine that shows a dissociation channel of a hydrogen radical with a chemically non-intuitive path which is called roaming. 201 Roaming was originally explored experimentally and computationally in formaldehyde by Bowman and coworkers in 2004 but real-time experimental observation were not achieved until 2020. 202,203 The NNP is learned to reproduce 29 energy values and force values for electronic singlet and triplet states and 812 spin-orbit couplings.…”

Section: Reaction Ratesmentioning

confidence: 99%

Neural network potentials for chemistry: concepts, applications and prospects

et al. 2023

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“…The use of CASSCF data must be carefully validated against multireference methods, such as multireference configuration interaction (MRCI) and extended multistate complete active space second-order perturbation theory (XMS-CASPT2). We recommend the ANO-type double-ζ basis set or comparable aug-cc-pVDZ basis set because they provide a balance between computational cost and accuracy and capture diverse excitation types, including Rydberg states. , Moreover, Bowan, Lester, and co-workers showed that in the dissociation of CH 3 COO Criegee intermediates and more recently Marquet and and co-workers showed that in the dissociation of tyrosine, there are no available reference methods can describe the entire reaction space, requiring a combination of QC methods to be employed.…”

Section: The ML Photodynamics Approachmentioning

confidence: 99%

A Look Inside the Black Box of Machine Learning Photodynamics Simulations

Lopez

2022

Acc. Chem. Res.

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Metrics & MoreArticle Recommendations * sı Supporting Information CONSPECTUS: Photochemical reactions are of great importance in chemistry, biology, and materials science because they take advantage of a renewable energy source, mild reaction conditions, and high atom economy. Light absorption can excite molecules to a higher energy electronic state of the same spin multiplicity. The following nonadiabatic processes induce molecular transformations that afford exotic molecular architectures and high-energy-isomers that are inaccessible by thermal means. Computational simulations now complement time-resolved instrumentation to reveal ultrafast excited-state mechanistic information for photochemical reactions that is essential in disentangling elusive spectroscopic features, excitedstate lifetimes, and excited-state mechanistic critical points. Nonadiabatic molecular dynamics (NAMD), powered by surface hopping techniques, is among the most widely applied techniques to model the photochemical reactions of medium-sized molecules. However, the computational efficiency is limited because of the requisite thousands of multiconfigurational quantum-chemical calculations multiplied by hundreds of trajectories. Machine learning (ML) has emerged as a revolutionary force in computational chemistry to predict the outcome of the resource-intensive multiconfigurational calculations on the fly. An ML potential trained with a substantial set of quantum-chemical calculations can predict the energies and forces with errors under chemical accuracy at a negligible cost. The integration of ML potentials in NAMD dramatically extends the maximum simulation time scale by ∼10 000fold to the nanosecond regime.In this Account, we present a comprehensive demonstration of ML photodynamics simulations and summarize our most recent applications in resolving complex photochemical reactions. First, we address three fundamental components of ML techniques for photodynamics simulations: the quantum-chemical data set, the ML potential, and NAMD. Second, we describe best practices in building training data and our procedure toward training the ML photodynamics model with our recent literature contributions. We introduce a convenient training data generation scheme combining Wigner sampling and geometrical interpolation. It trains reliable and effective ML potentials suitable for subsequent active learning to detect undersampled data. We demonstrate how active learning automatically discovers new mechanistic pathways and reproduces experimental results. We point out that atomic permutation is an essential data augmentation approach to improve the learnability of distance-based molecular descriptors for highly symmetric molecules. Third, we demonstrate the utility of ML-photodynamics by showing the results of ML photodynamics simulations of (1) photo-torquoselective 4π disrotatory electrocyclic ring closing of norbornyl cyclohexadiene, which reveals a thermal conversion from experimentally unobserved intermediates to the reactant in 1 ns; (2) [2 + 2] ph...

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Deep learning study of tyrosine reveals that roaming can lead to photodamage

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 36 publications

References 65 publications

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Neural network potentials for chemistry: concepts, applications and prospects

A Look Inside the Black Box of Machine Learning Photodynamics Simulations

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