Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.

A scheme utilizing excitation of core electrons followed by the resonant-Auger – interatomic Coulombic decay (RA-ICD) cascade was recently proposed as a means of controlling the generation site and energies of slow ICD electrons. This control mechanism was verified in a series of experiments in rare gas dimers. In this article, we present fully ab initio computed ICD electron and kinetic energy release spectra produced following 2p3/2 → 4s, 2p1/2 → 4s, and 2p3/2 → 3d core excitations of Ar in Ar2. We demonstrate that the manifold of ICD states populated in the resonant Auger process comprises two groups. One consists of lower energy ionization satellites characterized by fast interatomic decay, while the other consists of slow decaying higher energy ionization satellites. We show that accurate description of nuclear dynamics in the latter ICD states is crucial for obtaining theoretical electron and kinetic energy release spectra in good agreement with the experiment.

Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, “extended NIR”, ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

Potential energy surfaces (PESs) play a central role in our understanding of chemical reactions. Despite the impressive development of efficient electronic structure methods and codes, such computations still remain a difficult task for the majority of relevant systems. In this context, artificial neural networks (NNs) are promising candidates to construct the PES for a wide range of systems. However, the choice of suitable molecular descriptors remains a bottleneck for these algorithms. In this work, we show that a principal component analysis (PCA) is a powerful tool to prepare an optimal set of descriptors and to build an efficient NN: this protocol leads to a substantial improvement of the NNs in learning and predicting a PES. Furthermore, the PCA provides a means to reduce the size of the input space (i.e., number of descriptors) without losing accuracy. As an example, we applied this novel approach to the computation of the high-dimensional PES describing the keto–enol tautomerism reaction occurring in the acetone molecule.