X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we address how choices in data representation affect model interpretability and accuracy, and we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used “pointwise” featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating the discovery of new functional materials.

Oxynitrides with the photoelectrochemical stability of oxides and desirable band energetics of nitrides comprise a promising class of materials for solar photochemistry. Challenges in synthesizing a wide variety of oxynitride materials has limited exploration of this class of functional materials, which we address using a reactive cosputtering combined with rapid thermal processing method to synthesize multi-cation-multi-anion libraries. We demonstrate the synthesis of a La_xTa_1-xO_yN_z thin film composition spread library and its characterization by both traditional thin film materials characterization and custom combinatorial optical spectroscopy and X-ray absorption near edge spectroscopy (XANES) techniques, ultimately establishing structure-chemistry-property relationships. We observe that over a substantial La-Ta composition range the thin films crystallize in the same perovskite LaTaON₂ structure with significant variation of anion chemistry. The relative invariance in optical band gap demonstrates a remarkable decoupling of composition and band energetics so that the composition can be optimized while retaining the desirable 2 eV band gap energy. We also demonstrate the intercalation of diatomic nitrogen into the La₃TaO₇ structure, which gives rise to a direct-allowed optical transition at 2.2 eV, less than half the value of the oxide's band gap. These findings motivate further exploration of the visible light response of this material that is predicted to be stable over a wide range of electrochemical potential.

Deployment of solar fuels technology requires photoanodes with long term stability, which can be accomplished using light absorbers that self-passivate under operational conditions. Several copper vanadates have been recently reported as promising photoanode materials, and their stability and self-passivation is demonstrated through a combination of Pourbaix calculations and combinatorial experimentation.

The solar photoelectrochemical generation of hydrogen and carbon-containing fuels comprises a critical energy technology for establishing sustainable energy resources. The photoanode, which is responsible for solar-driven oxygen evolution, has persistently limited technology advancement due to the lack of materials that exhibit both the requisite electronic properties and operational stability. Efforts to extend the lifetime of solar fuel devices increasingly focus on mitigating corrosion in the highly oxidizing oxygen evolution environment, motivating our development of a photoanode discovery pipeline that combines electronic structure calculations, Pourbaix stability screening, and high-throughput experiments. By applying the pipeline to ternary metal oxides containing manganese, we identify a promising class of corrosion-resistant materials and discover five oxygen evolution photoanodes, including the first demonstration of photoelectrocatalysis with Mn-based ternary oxides and the introduction of alkaline earth manganates as promising photoanodes for establishing a durable solar fuels technology.

The limited number of known low-band-gap photoelectrocatalytic materials poses a significant challenge for the generation of chemical fuels from sunlight. Using high-throughput ab initio theory with experiments in an integrated workflow, we find eight ternary vanadate oxide photoanodes in the target band-gap range (1.2-2.8 eV). Detailed analysis of these vanadate compounds reveals the key role of VO₄ structural motifs and electronic band-edge character in efficient photoanodes, initiating a genome for such materials and paving the way for a broadly applicable high-throughput-discovery and materials-by-design feedback loop. Considerably expanding the number of known photoelectrocatalysts for water oxidation, our study establishes ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight and demonstrates our high-throughput theory-experiment pipeline as a prolific approach to materials discovery.

Solar-driven oxygen evolution is a critical technology for renewably synthesizing hydrogen- and carbon-containing fuels in solar fuel generators. New photoanode materials are needed to meet efficiency and stability requirements, motivating materials explorations for semiconductors with (i) band-gap energy in the visible spectrum and (ii) stable operation in aqueous electrolyte at the electrochemical potential needed to evolve oxygen from water. Motivated by the oxygen evolution competency of many Mn-based oxides, the existence of several Bi-containing ternary oxide photoanode materials, and the variety of known oxide materials combining these elements with Sm, we explore the Bi-Mn-Sm oxide system for new photoanodes. Through the use of a ferri/ferrocyanide redox couple in high-throughput screening, BiMn2O5 and its alloy with Sm are identified as photoanode materials with a near-ideal optical band gap of 1.8 eV. Using density functional theory-based calculations of the mullite Bi3+Mn3+Mn4+O5 phase, we identify electronic analogues to the well-known BiVO4 photoanode and demonstrate excellent Pourbaix stability above the oxygen evolution Nernstian potential from pH 4.5 to 15. Our suite of experimental and computational characterization indicates that BiMn2O5 is a complex oxide with the necessary optical and chemical properties to be an efficient, stable solar fuel photoanode.

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform.