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Structural Decomposition of UV--Visible Spectral Variation: Azobenzene in Ethanol Solution
Authors:
Eemeli A. Eronen,
Johannes Niskanen
Abstract:
We present a structural interpretation of statistical variability in simulated liquid-phase UV--visible absorption spectra. We analyze the significant variation of the spectral response, caused by structural variation within the ensemble, using a response-targeted method known as emulator-based component analysis. In the high-dimensional input space, the method identifies a subspace of a few dimen…
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We present a structural interpretation of statistical variability in simulated liquid-phase UV--visible absorption spectra. We analyze the significant variation of the spectral response, caused by structural variation within the ensemble, using a response-targeted method known as emulator-based component analysis. In the high-dimensional input space, the method identifies a subspace of a few dimensions that accounts for most spectral variance. The resulting decomposition reveals the spectrally decisive structural features and filters out the irrelevant ones. For our test case, the ethanolic {\it trans}-azobenzene, the analysis implies an overrepresentation of certain structural characteristics following a photoexcitation at a given wavelength, potentially significant for the subsequent nuclear dynamics, photophysics, and photochemistry.
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Submitted 27 April, 2026; v1 submitted 5 May, 2025;
originally announced May 2025.
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Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid
Authors:
E. A. Eronen,
A. Vladyka,
Ch. J. Sahle,
J. Niskanen
Abstract:
Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24200 sulfur K$β$ X-ray emission spectra of aqueous sulfuric…
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Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24200 sulfur K$β$ X-ray emission spectra of aqueous sulfuric acid simulated at six different concentrations. We train a feed-forward neural network to predict the spectra from the corresponding descriptor vectors and find that the local many-body tensor representation, smooth overlap of atomic positions and atom-centered symmetry functions excel in this comparison. We found a similar hierarchy when applying the emulator-based component analysis to identify and separate the spectrally relevant structural characteristics from the irrelevant ones. In this case, the spectra were dominantly dependent on the concentration of the system, whereas adding the second most significant degree of freedom in the decomposition allowed for distinction of the protonation state of the acid molecule.
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Submitted 22 August, 2024; v1 submitted 13 February, 2024;
originally announced February 2024.
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Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
Authors:
Eemeli A. Eronen,
Anton Vladyka,
Florent Gerbon,
Christoph. J. Sahle,
Johannes Niskanen
Abstract:
We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse p…
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We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse problem of spectrum interpretation is achieved. Structural and spectral variation across the sampled phase space is notable. Using these data, we train a neural network to predict the intensities of spectral regions of interest from the structure. These regions are defined by the temperature-difference profile of the simulated spectra, and the analysis yields a structural interpretation for their behavior. Even though the utilized local many-body tensor representation implicitly encodes the secondary structure of the peptide, our approach proves that this information is irrecoverable from the spectra. A hard X-ray Raman scattering experiment confirms the overall sensibility of the simulated spectra, but the predicted temperature-dependent effects therein remain beyond the achieved statistical confidence level.
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Submitted 13 February, 2024; v1 submitted 14 June, 2023;
originally announced June 2023.
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Resonance-enhanced multiphoton ionization in the x-ray regime
Authors:
Aaron C. LaForge,
Sang-Kil Son,
Debadarshini Mishra,
Markus Ilchen,
Stephen Duncanson,
Eemeli Eronen,
Edwin Kukk,
Stanislaw Wirok-Stoletow,
Daria Kolbasova,
Peter Walter,
Rebecca Boll,
Alberto De Fanis,
Michael Meyer,
Yevheniy Ovcharenko,
Daniel E. Rivas,
Philipp Schmidt,
Sergey Usenko,
Robin Santra,
Nora Berrah
Abstract:
Here, we report on the nonlinear ionization of argon atoms in the short wavelength regime using ultraintense x rays from the European XFEL. After sequential multiphoton ionization, high charge states are obtained. For photon energies that are insufficient to directly ionize a $1s$ electron, a different mechanism is required to obtain ionization to Ar$^{17+}$. We propose this occurs through a two-c…
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Here, we report on the nonlinear ionization of argon atoms in the short wavelength regime using ultraintense x rays from the European XFEL. After sequential multiphoton ionization, high charge states are obtained. For photon energies that are insufficient to directly ionize a $1s$ electron, a different mechanism is required to obtain ionization to Ar$^{17+}$. We propose this occurs through a two-color process where the second harmonic of the FEL pulse resonantly excites the system via a $1s \rightarrow 2p$ transition followed by ionization by the fundamental FEL pulse, which is a type of x-ray resonance-enhanced multiphoton ionization (REMPI). This resonant phenomenon occurs not only for Ar$^{16+}$, but through multiple lower charge states, where multiple ionization competes with decay lifetimes, making x-ray REMPI distinctive from conventional REMPI. With the aid of state-of-the-art theoretical calculations, we explain the effects of x-ray REMPI on the relevant ion yields and spectral profile.
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Submitted 5 November, 2021; v1 submitted 15 October, 2021;
originally announced October 2021.