AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

Pentanuclear iron complex for water oxidation: spectroscopic analysis of reactive intermediates in solution and catalyst immobilization into the MOF-based photoanode

Photoelectrochemical water splitting can produce green hydrogen for industrial use and CO2-neutral transportation, ensuring the transition from fossil fuels to green, renewable energy sources. The iron-based electrocatalyst [FeII4FeIII(μ-3-O)(μ-L)6]3+ (LH = 3,5-bis(2-pyridyl)pyrazole) (1), discovered in 2016, is one of the fastest molecular water oxidation catalysts (WOC) based on earth-abundant elements. However, its water oxidation reaction mechanism has not been yet fully elucidated. Here, we present in situ X-ray spectroscopy and electron paramagnetic resonance (EPR) analysis of electrochemical water oxidation reaction (WOR) promoted by (1) in water-acetonitrile solution. We observed transient reactive intermediates during the in situ electrochemical WOR, consistent with a coordination sphere expansion prior to the onset of catalytic current. At a pre-catalytic (~+1.1 V vs. Ag/AgCl) potential, the distinct g~2.0 EPR signal assigned to FeIII/FeIV interaction was observed. Prolonged bulk electrolysis at catalytic (~+1.6 V vs. Ag/AgCl) potential leads to the further oxidation of Fe centers in (1). At the steady state achieved with such electrolysis, the formation of hypervalent FeV=O and FeIV=O catalytic intermediates was inferred with XANES and EXAFS fitting, detecting a short Fe=O bond at ~1.6 Å. (1) was embedded into MIL-126 MOF with the formation of (1)-MIL-126 composite. The latter was tested in photoelectrochemical WOR and demonstrated an improvement of electrocatalytic current upon visible light irradiation in acidic (pH=2) water solution. The presented spectroscopic analysis gives further insight into the catalytic pathways of multinuclear systems and should help the subsequent development of more energy- and cost-effective catalysts of water splitting based on earth-abundant metals. Photoelectrocatalytic activity of (1)-MIL-126 confirms the possibility of creating an assembly of (1) inside a solid support and boosting it with solar irradiation towards industrial applications of the catalyst.

The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d-3p electron exchange force and Kβ emission core-hole lifetime.