ArticleChemical science2017
Machine learning for quantum dynamics: deep learning of excitation energy transfer properties.
Article in Chemical science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Quantum Biology Using Ultrafast Integrative and Molecular Engineering Tools: A Perspective.Computational and structural biotechnology journal · 2026Review
- Photosynthesis under far-red light-evolutionary adaptations and bioengineering of light-harvesting complexes.FEBS letters · 2026Review
- Application of the Time-Domain Multichromophoric Fluorescence Resonant Energy Transfer Method in the NISE Programme.Journal of chemical theory and computation · 2025Article
- High-Resolution Frequency-Domain Spectroscopic and Modeling Studies of Photosystem I (PSI), PSI Mutants and PSI Supercomplexes.International journal of molecular sciences · 2024Review
- Recent progress in atomistic modeling of light-harvesting complexes: a mini review.Photosynthesis research · 2023Review
- Thermal site energy fluctuations in photosystem I: new insights from MD/QM/MM calculations.Chemical science · 2023Article
- Low-cost prediction of molecular and transition state partition functionsChemical science · 2022Article
- Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics.Nature communications · 2022Article
- 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
- Quantum Chemistry Calculations for Metabolomics.Chemical reviews · 2021Review
- BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.Chemical science · 2020Article
- Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber.Scientific reports · 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
- Pushing property limits in materials discoveryChemical science · 2020Article
- Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach.RSC advances · 2020Article
- Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules.Molecules (Basel, Switzerland) · 2020Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment-protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We propose the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies exhibit similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.
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Registered trials
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