We present a first-principles based formalism to provide a quantitative measure of the thermodynamic instability and propensity for electrochemical stabilization, passivation or corrosion of metastable materials in aqueous media. We demonstrate that this

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 VO 4 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 fuels materials | density-functional theory | high-throughput experiments | complex oxides | photocatalysis T he use of predictive simulation in combination with experiments for the accelerated discovery and rational design of functional materials is a challenge of significant contemporary interest. High-throughput computing and materials databases (1-3), largely based on density-functional theory (DFT), have recently enabled rapid screening of solid-state compounds with simulation for multiple properties and functionalities (4-10). Since their advent just a few years ago, these DFT-based databases and analytics tools have already been used to identify more than 20 new functional materials that were later confirmed by experiments across a number of applications (8), motivating concerted efforts to validate theory predictions with experiments (11). However, in photoelectrochemistry for the renewable synthesis of solar fuels, efficient metal-oxide photoanode materials--photoelectrocatalysts for the oxygen evolution reaction (OER)--remain critically missing (12). Forty years of experimental research has yielded just 16 metal-oxide photoanode compounds with band-gap energy in the desirable 1.2-2.8-eV range that strongly overlaps with the solar spectrum. Prior high-throughput computational screening studies have yet to expand this list (6, 7, 13), in part due to quantitative limitations in predictability of the electronic structure--especially band-gap energy, E g , and the valence band maximum (VBM) energy, E VBM --from the chemical composition and crystal structure. Our integration of ab initio theory with high-throughput experiments has yielded a most prolific materials discovery effort, as demonstrated by the identification of 12 water oxidation photoelectrocatalysts in the target band-gap range, including our recently reported 4 copper vanadates (14) and 8 additional metal vanadates reported here.Monoclinic BiVO 4 (15) has received substantial attention as a solar fuels photoanode material due to its promising OER photoactivity. It has a desirable 2.4-eV band...

Benchmarking metrics for materials discovery via sequential learning are presented, to assess the efficacy of existing algorithms and to be scientific in our assessment of accelerated science.

High-throughput experimentation provides efficient mapping of composition-property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in FeO, CuVO, and BiVO. The applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra.

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.

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.

This section provides supplementary information on quality control and visualization of the composition space. Table S1 lists 15 EDS composition measurements of metal oxides on BiVO4 after inkjet printing and calcination. Table S2 lists the definition of composition symbols used to map the 4-component composition space as a series of 12 pseudo-ternary composition triangles. Figure S1 shows the results of XRD characterization and XRF mapping of the BiVO4 film prior to deposition of the catalyst library. The XRD measurement was performed with a 0.5˚ incident angle to limit the sampling depth, and the resulting diffraction pattern demonstrates successful formation of the BiVO4 phase with no detectable secondary phases. The combinatorial photoelectrochemical measurements rely on a uniform light absorber layer, and the uniformity of the BiVO4 composition over the photoanode library region is demonstrated by the XRF measurement. Table S1. EDS composition measurements of 15 metal oxides deposited onto BiVO4. The maximum composition deviation of 0.06 and mean composition deviation of 0.034 are within the uncertainty of EDS composition measurements for thin, discontinuous films.

Discovery of new materials drives the deployment of new technologies.Complex technological requirements demand precisely tailored material functionalities, and materials scientists are driven to search for these new materials in compositionally complex and often non-equilibrium spaces containing three, four or more elements. The phase behavior of these highorder composition spaces is mostly unknown and unexplored. High-throughput methods can offer strategies for efficiently searching complex and multidimensional material genomes for these much needed new materials and can also suggest a processing pathway for synthesizing them. However, highthroughput structural characterization is still relatively under-developed for rapid material discovery. Here, a synchrotron X-ray diffraction and fluorescence experiment for rapid measurement of both X-ray powder patterns and compositions for an array of samples in a material library is presented. The experiment is capable of measuring more than 5000 samples per day, as demonstrated by the acquisition of high-quality powder patterns in a bismuthvanadium-iron oxide composition library. A detailed discussion of the scattering geometry and its ability to be tailored for different material systems is provided, with specific attention given to the characterization of fiber textured thin films. The described prototype facility is capable of meeting the structural characterization needs for the first generation of high-throughput material genomic searches.