ArticleNature chemistry2022
Deep learning study of tyrosine reveals that roaming can lead to photodamage.
Article in Nature chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
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- Ultrafast solvent-modulated roaming mechanism in bromoform revealed by femtosecond X-ray solution scattering.Nature communications · 2026Article
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
- COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin-Orbit Couplings and Dynamics.The journal of physical chemistry. A · 2025Article
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.npj computational materials · 2025Article
- On the Simulation of Photoreactions Using Restricted Open-Shell Kohn-Sham Theory.Molecules (Basel, Switzerland) · 2024Article
- The OpenMolcasJournal of chemical theory and computation · 2023Article
- Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach.The journal of physical chemistry letters · 2023Article
- Neural network potentials for chemistry: concepts, applications and prospects.Digital discovery · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully 'selected' to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive-with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled 'roaming' dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.
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35655007What OpenQuestion holds
Registered trials
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