ArticleJournal of chemical theory and computation2023
Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules.Journal of chemical theory and computation · 2026Article
- Modified chlorophyll pigment at ChlChemical science · 2025Article
- Improvement of Exciton Hamiltonians for Photosynthetic Reaction Centers by Bayesian Optimization.ACS omega · 2025Article
- Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model.The journal of physical chemistry letters · 2025Article
- Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians.Nature communications · 2025Article
- Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials.The journal of physical chemistry letters · 2025Article
- Development of Molecular Dynamics Parameters and Theoretical Analysis of Excitonic and Optical Properties in the Light-Harvesting Complex II.Journal of chemical theory and computation · 2025Article
- Protein Effects on the Excitation Energies and Exciton Dynamics of the CP24 Antenna Complex.The journal of physical chemistry. B · 2024Article
- Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.The journal of physical chemistry. A · 2024Article
- Electronic Excited States from Physically Constrained Machine Learning.ACS central science · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
We propose a machine learning (ML)-based strategy for an inexpensive calculation of excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical molecular dynamics simulations of LHCs in their natural environment in combination with ML prediction of the excitonic Hamiltonian of the embedded aggregate of pigments. The proposed ML model can reproduce the effects of geometrical fluctuations together with those due to electrostatic and polarization interactions between the pigments and the protein. The training is performed on the chlorophylls of the major LHC of plants, but we demonstrate that the model is able to extrapolate well beyond the initial training set. Moreover, the accuracy in predicting the effects of the environment is tested on the simulation of the small changes observed in the absorption spectra of the wild-type and a mutant of a minor LHC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.