arXiv subjects

Eemeli A. Eronen

Publications and source records attributed to Eemeli A. Eronen.

3 recordsLinked to original sources

arXiv2026

Structural Decomposition of UV--Visible Spectral Variation: Azobenzene in Ethanol Solution

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.

cond-mat.soft↗

arXiv2024

Implementation of the Emulator-based Component Analysis

We present a PyTorch-powered implementation of the emulator-based component analysis used for ill-posed numerical non-linear inverse problems, where an approximate emulator for the forward problem is known. This emulator may be a numerical model, an interpolating function, or a fitting function such as a neural network. With the help of the emulator and a data set, the method seeks dimensionality reduction by projection in the variable space so that maximal variance of the target (response) values of the data is covered. The obtained basis set for projection in the variable space defines a subspace of the greatest response for the outcome of the forward problem. The method allows for the reconstruction of the coordinates in this subspace for an approximate solution to the inverse problem. We present an example of using the code provided as a Python class.

math.NA↗

arXiv2024

Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy

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.

cond-mat.soft↗