ArticleChemical science2016
Machine learning exciton dynamics.
Article in Chemical science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model.The journal of physical chemistry letters · 2025Article
- Protein Effects on the Excitation Energies and Exciton Dynamics of the CP24 Antenna Complex.The journal of physical chemistry. B · 2024Article
- High-Resolution Frequency-Domain Spectroscopic and Modeling Studies of Photosystem I (PSI), PSI Mutants and PSI Supercomplexes.International journal of molecular sciences · 2024Review
- Article
- Nonadiabatic Derivative Couplings Calculated Using Information of Potential Energy Surfaces without Wavefunctions: Ab Initio and Machine Learning Implementations.Molecules (Basel, Switzerland) · 2023Article
- Ab Initio Molecular Cavity Quantum Electrodynamics Simulations Using Machine Learning Models.Journal of chemical theory and computation · 2023Article
- An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning.Molecules (Basel, Switzerland) · 2023Article
- Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes.Journal of chemical theory and computation · 2023Article
- Low-cost prediction of molecular and transition state partition functionsChemical science · 2022Article
- Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface.The Journal of chemical physics · 2021Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Molecular excited states through a machine learning lens.Nature reviews. Chemistry · 2021Review
- Machine-learned potentials for next-generation matter simulations.Nature materials · 2021Review
- PySurf: A Framework for Database Accelerated Direct Dynamics.Journal of chemical theory and computation · 2020Article
- Machine learning in chemical reaction space.Nature communications · 2020Article
- Designing and understanding light-harvesting devices with machine learning.Nature communications · 2020Review
- Machine Learning for Absorption Cross Sections.The journal of physical chemistry. A · 2020Article
- Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.The journal of physical chemistry letters · 2020Article
- Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules.Molecules (Basel, Switzerland) · 2020Review
- Machine learning enables long time scale molecular photodynamics simulations.Chemical science · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine learning technique, multi-layer perceptrons, as a tool to reduce the time required to compute excited state energies. With this approach we predict time-dependent density functional theory (TDDFT) excited state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO) complex. Additionally we compute spectral densities and exciton populations from the predictions. Different methods to determine multi-layer perceptron training sets are introduced, leading to several initial data selections. In addition, we compute spectral densities and exciton populations. Once multi-layer perceptrons are trained, predicting excited state energies was found to be significantly faster than the corresponding QM/MM calculations. We showed that multi-layer perceptrons can successfully reproduce the energies of QM/MM calculations to a high degree of accuracy with prediction errors contained within 0.01 eV (0.5%). Spectral densities and exciton dynamics are also in agreement with the TDDFT results. The acceleration and accurate prediction of dynamics strongly encourage the combination of machine learning techniques with
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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.