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Hase, Florian

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Hase

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Florian

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Hase, Florian

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2016 - 20173

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Author's Publications: search.filters.isAuthorOfPublication.e72f0f8c-028d-43a5-a0bd-e846ef6d45b7

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    Machine learning for quantum dynamics: deep learning of excitation energy transfer properties† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc03542j
    (Royal Society of Chemistry, 2017) Hase, Florian; Kreisbeck, Christoph; Aspuru-Guzik, Alan
    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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    Absence of Selection for Quantum Coherence in the Fenna–Matthews–Olson Complex: A Combined Evolutionary and Excitonic Study
    (American Chemical Society, 2017) Valleau, Stéphanie; Studer, Romain A.; Hase, Florian; Kreisbeck, Christoph; Saer, Rafael G.; Blankenship, Robert E.; Shakhnovich, Eugene; Aspuru-Guzik, Alan
    We present a study on the evolution of the Fenna–Matthews–Olson bacterial photosynthetic pigment–protein complex. This protein complex functions as an antenna. It transports absorbed photons—excitons—to a reaction center where photosynthetic reactions initiate. The efficiency of exciton transport is therefore fundamental for the photosynthetic bacterium’s survival. We have reconstructed an ancestor of the complex to establish whether coherence in the exciton transport was selected for or optimized over time. We have also investigated the role of optimizing free energy variation upon folding in evolution. We studied whether mutations which connect the ancestor to current day species were stabilizing or destabilizing from a thermodynamic viewpoint. From this study, we established that most of these mutations were thermodynamically neutral. Furthermore, we did not see a large change in exciton transport efficiency or coherence, and thus our results predict that exciton coherence was not specifically selected for.
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    Machine learning exciton dynamics
    (Royal Society of Chemistry (RSC), 2016) Hase, Florian; Valleau, Stephanie; Pyzer-Knapp, Edward; Aspuru-Guzik, Alan
    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 ab-initio methods.