Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow

Fig. 3: Example applications of deep learning for spectral data.

a Predicting structure information from the X-ray diffraction374, Reprinted according to the terms of the CC-BY license374. Copyright 2020. b Predicting catalysis properties from computational electronic density of states data. Reprinted according to the terms of the CC-BY license202. Copyright 2021.

Databases and software libraries

Currently, large-scale and element-diverse spectral data mainly exist in computational databases. For example, in ref. 160, the authors calculated the infrared spectra, piezoelectric tensor, Born effective charge tensor, and dielectric response as a part of the JARVIS-DFT DFPT database. The Materials Project has established the largest computational X-ray absorption database (XASDb), covering the K-edge X-ray near-edge fine structure (XANES)161^,162 and the L-edge XANES163 of a large number of material structures. The database currently hosts more than 400,000 K-edge XANES site-wise spectra and 90,000 L-edge XANES site-wise spectra of many compounds in the Materials Project. There are considerably fewer experimental XAS spectra, being on the order of hundreds, as seen in the EELSDb and the XASLib. Collecting large experimental spectra databases that cover a wide range of elements is a challenging task. Collective efforts focused on curating data extracted from different sources, as found in the RRUFF Raman, XRD and chemistry database164, the open Raman database165, and the SOP spectra library166. However, data consistency is not guaranteed. It is also now possible for contributors to share experimental data in a Materials Project curated database, MPContribs167. This database is supported by the US Department of Energy (DOE) providing some expectation of persistence. Entries can be kept private or published and are linked to the main materials project computational databases. There is an ongoing effort to capture data from DOE-funded synchrotron light sources.
Recent advances in sources, detectors, and experimental instrumentation have made high-throughput measurements of experimental spectra possible, giving rise to new possibilities for spectral data generation and modeling. Such examples include the HTEM database10 that contains 50,000 optical absorption spectra and the UV-Vis database of 180,000 samples from the Joint Center for Artificial Photosynthesis. Some of the common spectra databases for spectra data are shown in Table 3. There are beginning to appear cloud-based software as a service platforms for high-throughput data analysis, for example, pair-distribution function (PDF) in the cloud (https://pdfitc.org)168 which are backed by structured databases, where data can be kept private or made public. This transition to the cloud from data analysis software installed and run locally on a user’s computer will facilitate the sharing and reuse of data by the community.