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Non-adiabatic molecular dynamics simulations provide new insights into the exciton transfer in the Fenna–Matthews–Olson complex

Abstract (Expand)

A trajectory surface hopping approach, which uses machine learning to speed up the most time-consuming steps, has been adopted to investigate the exciton transfer in light-harvesting systems. The present … neural networks achieve high accuracy in predicting both Coulomb couplings and excitation energies. The latter are predicted taking into account the environment of the pigments. Direct simulation of exciton dynamics through light-harvesting complexes on significant time scales is usually challenging due to the coupled motion of nuclear and electronic degrees of freedom in these rather large systems containing several relatively large pigments. In the present approach, however, we are able to evaluate a statistically significant number of non-adiabatic molecular dynamics trajectories with respect to exciton delocalization and exciton paths. The formalism is applied to the Fenna–Matthews–Olson complex of green sulfur bacteria, which transfers energy from the light-harvesting chlorosome to the reaction center with astonishing efficiency. The system has been studied experimentally and theoretically for decades. In total, we were able to simulate non-adiabatically more than 30 ns, sampling also the relevant space of parameters within their uncertainty. Our simulations show that the driving force supplied by the energy landscape resulting from electrostatic tuning is sufficient to funnel the energy towards site 3, from where it can be transferred to the reaction center. However, the high efficiency of transfer within a picosecond timescale can be attributed to the rather unusual properties of the BChl a molecules, resulting in very low inner and outer-sphere reorganization energies, not matched by any other organic molecule, e.g., used in organic electronics. A comparison with electron and exciton transfer in organic materials is particularly illuminating, suggesting a mechanism to explain the comparably high transfer efficiency.

Authors: Monja Sokolov, David S. Hoffmann, Philipp M. Dohmen, Mila Krämer, Sebastian Höfener, Ulrich Kleinekathöfer, Marcus Elstner

Date Published: 9th Jul 2024

Publication Type: Journal

Formation of sdB-stars via common envelope ejection by substellar companions

Abstract

Not specified

Authors: M. Kramer, F. R. N. Schneider, S. T. Ohlmann, S. Geier, V. Schaffenroth, R. Pakmor, F. K. Röpke

Date Published: 1st Oct 2020

Publication Type: Journal

Formation of sdB-stars via common envelope ejection by substellar companions

Abstract

Not specified

Authors: M. Kramer, F. R. N. Schneider, S. T. Ohlmann, S. Geier, V. Schaffenroth, R. Pakmor, F. K. Röpke

Date Published: 1st Oct 2020

Publication Type: Journal

On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy

Abstract (Expand)

Nowadays astronomical catalogs contain patterns of hundreds of millions of objects with data volumes in the terabyte range. Upcoming projects will gather such patterns for several billions of objects … with peta- and exabytes of data. From a machine learning point of view, these settings often yield unsupervised, semi-supervised, or fully supervised tasks, with large training and huge test sets. Recent studies have demonstrated the effectiveness of prototype-based learning schemes such as simple nearest neighbor models. However, although being among the most computationally efficient methods for such settings (if implemented via spatial data structures), applying these models on all remaining patterns in a given catalog can easily take hours or even days. In this work, we investigate the practical effectiveness of GPU-based approaches to accelerate such nearest neighbor queries in this context. Our experiments indicate that carefully tuned implementations of spatial search structures for such multi-core devices can significantly reduce the practical runtime. This renders the resulting frameworks an important algorithmic tool for current and upcoming data analyses in astronomy.

Authors: Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, Fabian Gieseke

Date Published: 2013

Publication Type: InProceedings

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