XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

Abstract

Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse.

Data-driven design of electrocatalysts: principle, progress, and perspective

“…From data collection to performance prediction, various methods are being developed. [22][23][24] Meanwhile, many excellent reviews have emerged. They mainly focus on a certain class of catalytic reactions, highlight the new approaches to ML, or discuss the catalytic process revealed by algorithms.…”

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

“…Machine learning tools have been successfully employed for interpreting the spectra obtained from X-ray absorption-based spectroscopic techniques (XAS). 76 , 77 , 78 , 79 , 80 , 81 These include X-ray absorption near-edge structure (XANES), X-ray absorption fine structure, extended X-ray absorption fine structure (EXAFS), and electron energy loss-based spectroscopic techniques such as energy loss near-edge structure (ELNES) or extended electron energy loss fine structure (EXELFS). 82 A few applications of these techniques in establishing the catalyst/material surface structure are discussed in this section.…”

“…Using techniques such as PCA, Z score normalization, and k-means clustering, Kartashov et al. 77 developed software tools to pre-process the XAS data and offer a means of data mining to develop new insights about Pd nanoparticles. For more information on the machine-learning-based interpretation of X-ray absorption spectra, available databases for obtaining large-scale spectral datasets, and simulation codes for generating large datasets for XANES, the readers are suggested to refer to the review article by Timonshenko et al.…”

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

“…A list of scattering paths is required; this is generally obtained using reference structures from public databases (e.g., Materials Project, Inorganic Crystal Structure Database, , Cambridge Structural Database, etc.). While there have been many programs developed over the years, the Demeter suite of programs is now the most widely used program worldwide, but it is no longer being supported or developed. − Larch is a newer Python-based tool that includes both a GUI and a scripting-based interface; this is discussed later in Section …”