Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.

Gaining More Insights from Synchrotron-Based X-ray Spectroscopy for Alkali Ion Rechargeable Batteries

Alkali ion rechargeable batteries play a significant part in portable electronic devices and electronic vehicles. The rapid development of renewable energy technology nowadays demands batteries with even higher energy density for grid storage. To fulfill such demand, extensive research efforts have been devoted to optimizing electrochemical properties as well as developing novel energy storage schemes and designing new systems. In the investigation process, synchrotron-based X-ray spectroscopy plays a vital role in investigating the detailed degradation mechanism and developing novel energy storage schemes. Herein, we critically review the applications of synchrotron-based X-ray spectroscopy in battery research in recent years. This review begins with a discussion of the different scientific issues in alkali ion rechargeable batteries within various time and space scales. Subsequently, the principle of synchrotron-based X-ray spectroscopy is introduced, and the characteristics of various characterization techniques are summarized and compared. Typical application cases of synchrotron-based X-ray spectroscopy are then introduced into battery investigations. The final part presents perspectives in the development direction of both alkali ion rechargeable battery systems and synchrotron-based X-ray spectroscopy in the future.

Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nanometers to microns). The output of an SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave; the latter is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While isotropic structural arrangement can be interpreted by analysis of the azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the "computational reverse engineering analysis for scattering experiments" (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. The CREASE-2D workflow identifies the structural features whose computed I(q, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental I(q, θ). We expect that this CREASE-2D method will be a valuable tool for materials' researchers who need direct interpretation of the 2D scattering profiles in contrast to analyzing azimuthally averaged 1D I(q) vs q profiles that can lose important information related to structural anisotropy.

Enhancing antioxidant properties of CeO2 nanoparticles with Nd3+ doping: structural, biological, and machine learning insights

The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO2 nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO2 nanoparticles. Incorporating Nd3+ induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO2 was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce3+/Ce4+ ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO2. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO2 nanoparticles. This study presents the first exploration of the influence of Nd3+ on the structural, optical, and biological attributes of CeO2, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials.

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.