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Two-dimensional electronic spectroscopy has become one of the main experimental tools for analyzing the dynamics of excitonic energy transfer in large molecular complexes. Simplified theoretical models are usually employed to extract model parameters from the experimental spectral data. Here we show that computationally expensive but exact theoretical methods encoded into a neural network can be used to extract model parameters and infer structural information such as dipole orientation from two dimensional electronic spectra (2DES) or reversely, to produce 2DES from model parameters. We propose to use machine learning as a tool to predict unknown parameters in the models underlying recorded spectra and as a way to encode computationally expensive numerical methods into efficient prediction tools. We showcase the use of a trained neural network to efficiently compute disordered averaged spectra and demonstrate that disorder averaging has non-trivial effects for polarization controlled 2DES.
Challenges facing an understanding of the nature of low-energy excited states in photosynthesis
(2016)
While the majority of the photochemical states and pathways related to the biological capture of solar energy are now well understood and provide paradigms for artificial device design, additional low-energy states have been discovered in many systems with obscure origins and significance. However, as low-energy states are naively expected to be critical to function, these observations pose important challenges. A review of known properties of low energy states covering eight photochemical systems, and options for their interpretation, are presented. A concerted experimental and theoretical research strategy is suggested and outlined, this being aimed at providing a fully comprehensive understanding.
Computing the Hierarchical Equations of Motion (HEOM) is by itself a challenging problem, and so is writing portable production code that runs efficiently on a variety of architectures while scaling from PCs to supercomputers. We combined both challenges to push the boundaries of simulating quantum systems, and to evaluate and improve methodologies for scientific software engineering.
Our contributions are threefold: We present the first distributed memory implementation of the HEOM method (DM-HEOM), we describe an interdisciplinary development workflow, and we provide guidelines and experiences for designing distributed, performance-portable HPC applications with MPI-3, OpenCL and other state-of-the-art programming models. We evaluated the resulting code on multi- and many-core CPUs as well as GPUs, and demonstrate scalability on a Cray XC40 supercomputer for the PS I molecular light harvesting complex.
During a two year period between 2014 and 2016 the coma of comet 67P/Churyumov-Gerasimenko (67P/C-G) has been probed by the Rosetta spacecraft. Density data for 14 gas species was recorded with the COmet Pressure Sensor (COPS) and the Double Focusing Mass Spectrometer (DFMS) being two sensors of the ROSINA instrument. The combination with an inverse gas model yields emission rates on each of 3996 surface elements of a surface shape for the cometary nucleus.
The temporal evolution of gas production, of relative abundances, and peak productions weeks after perihelion are investigated. Solar irradiation and gas production are in a complex relation revealing features differing for gas species, for mission time, and for the hemispheres of the comet. This characterization of gas composition allows one to correlate 67P/C-G to other solar and interstellar comets, their formation conditions and nucleus properties, see [Bodewits D., et al., 2020 Nature Astronomy].
The coma of comet 67P/Churyumov-Gerasimenko has been probed by the Rosetta spacecraft and shows a variety of different molecules. The ROSINA COmet Pressure Sensor and the Double Focusing Mass Spectrometer provide in-situ densities for many volatile compounds including the 14 gas species H2O, CO2, CO, H2S, O2, C2H6, CH3OH, H2CO, CH4, NH3, HCN, C2H5OH, OCS, and CS2. We fit the observed densities during the entire comet mission between August 2014 and September 2016 to an inverse coma model. We retrieve surface emissions on a cometary shape with 3996 triangular elements for 50 separated time intervals. For each gas we derive systematic error bounds and report the temporal evolution of the production, peak production, and the time-integrated total production. We discuss the production for the two lobes of the nucleus and for the northern and southern hemispheres. Moreover we provide a comparison of the gas production with the seasonal illumination.
Surface localization of gas sources on comet 67P/Churyumov-Gerasimenko based on DFMS/COPS data
(2019)
We reconstruct the temporal evolution of the source distribution for the four major gas species H2O, CO2, CO, and O2 on the surface of comet 67P/Churyumov-Gerasimenko during its 2015 apparition. The analysis applies an inverse coma model and fits to data between August 6th 2014 and September 5th 2016 measured with the Double Focusing Mass Spectrometer (DFMS) of the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) and the COmet Pressure Sensor (COPS). The spatial distribution of gas sources with their temporal variation allows one to construct surface maps for gas emissions and to evaluate integrated productions rates. For all species peak production rates and integrated productions rates per orbit are evaluated separately for the northern and the southern hemisphere. The nine most active emitting areas on the comet’s surface are defined and their correlation to emissions for each of the species is discussed.
We reconstruct the temporal evolution of surface emissions for the four major gas species H2O, CO2, CO, and O2 emitted during the 2015 apparition of comet 67P/Churyumov-Gerasimenko (67P/C-G). Measurements from the Double Focusing Mass Spectrometer (DFMS) of the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) and the COmet Pressure Sensor (COPS) are used to determine the gas sources on the surface with an inverse gas model for the entire coma. For all species, peak production rates and integrated production rates per orbit are evaluated separately for the northern and the southern hemisphere. Complemented with the total mass production, this allows us to estimate the dust-to-gas ratio of the
emitted material.
During the apparition of comet 67P/Churyumov-Gerasimenko (67P/C-G) solar irradiation causes varying rates for sublimation of volatile species from the cometary nucleus. Because sublimation processes take place close to the cometary surface, the relative abundance of volatiles in the coma and the ice composition are related to each other. To quantify this relation we assume a model for the expansion of a collisionless gas from the surface into the surrounding space. We use an inverse model approach to relate the in situ measurements of gas densities from the two Rosetta instruments COPS (COmet Pressure Sensor) and DFMS (Double Focusing Mass Spectrometer) at the positions of the spacecraft to the locations of surface gas emissions during the Rosetta mission 2014-2016. We assume the temporally integrated gas emissions to be representative for the ice composition close to the surface. Our analysis shows characteristic differences in the ice compositions between both hemispheres of 67P/C-G. In particular CO2 ice has a reduced abundance on the northern hemisphere. In contrast to the hemispherical differences, the two lobes do not show significant differences in terms of their ice composition.
The relation between ice composition in the nucleus of comet 67P/Churyumov-Gerasimenko on the one hand and relative abundances of volatiles in the coma on the other hand is important for the interpretation of density measurements in the environment of the cometary nucleus. For the 2015 apparition, in situ measurements from the two ROSINA (Rosetta Orbiter Spectrometer for Ion and Neutral Analysis) sensors COPS (COmet Pressure Sensor) and DFMS (Double Focusing Mass Spectrometer) determined gas densities at the spacecraft position for the 14 gas species H2O, CO2, CO, H2S, O2, C2H6, CH3OH, H2CO, CH4, NH3, HCN, C2H5OH, OCS, and CS2. We derive the spatial distribution of the gas emissions on the complex shape of the nucleus separately for 50 subintervals of the two-year mission time. The most active patches of gas emission are identified on the surface. We retrieve the relation between solar irradiation and observed emissions from these patches. The emission rates are compared to a minimal thermophysical model to infer the surface active fraction of H2O and CO2. We obtain characteristic differences in the ice composition close to the surface between the two hemispheres with a reduced abundance of CO2 ice on the northern hemisphere (locations with positive latitude). We do not see significant differences for the ice composition on the two lobes of 67P/C-G.