Automated monitoring of electrocatalyst corrosion as a function of electrochemical history and electrolyte formulation.

Automated platforms assessing the stability of electrocatalysts are key to accelerate the deployment of clean energy technologies. Here, we present a robust system that allows the study of corrosion behavior in conjunction with the electrochemical protocol and electrolyte composition over many individual electrodes. Oxygen reduction reaction on Pt is used as a proof-of-concept platform, where the influence of the potential window and phosphoric acid (PA) addition on Pt dissolution is probed.

The Materials Provenance Store.

We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations.

Agents for sequential learning using multiple-fidelity data.

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values.

Machine learning-accelerated design and synthesis of polyelemental heterostructures.

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties.

Benchmarking the acceleration of materials discovery by sequential learning.

Sequential learning (SL) strategies, iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials.

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes.

Combinatorial (photo)electrochemical studies of the (Ni-Mn)Ox system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap.

Combinatorial Discovery of Lanthanum-Tantalum Oxynitride Solar Light Absorbers with Dilute Nitrogen for Solar Fuel Applications.

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 LaTaON 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.

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment.

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.

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System.

Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial X-ray diffraction data sets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of X-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs' phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system.

High Throughput Light Absorber Discovery, Part 2: Establishing Structure-Band Gap Energy Relationships.

Combinatorial materials science strategies have accelerated materials development in a variety of fields, and we extend these strategies to enable structure-property mapping for light absorber materials, particularly in high order composition spaces. High throughput optical spectroscopy and synchrotron X-ray diffraction are combined to identify the optical properties of Bi-V-Fe oxides, leading to the identification of BiVFeO as a light absorber with direct band gap near 2.7 eV.

High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis.

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.

Stability and self-passivation of copper vanadate photoanodes under chemical, electrochemical, and photoelectrochemical operation.

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.

Combinatorial thin film composition mapping using three dimensional deposition profiles.

Many next-generation technologies are limited by material performance, leading to increased interest in the discovery of advanced materials using combinatorial synthesis, characterization, and screening. Several combinatorial synthesis techniques, such as solution based methods, advanced manufacturing, and physical vapor deposition, are currently being employed for various applications. In particular, combinatorial magnetron sputtering is a versatile technique that provides synthesis of high-quality thin film composition libraries.

Generating information-rich high-throughput experimental materials genomes using functional clustering via multitree genetic programming and information theory.

High-throughput experimental methodologies are capable of synthesizing, screening and characterizing vast arrays of combinatorial material libraries at a very rapid rate. These methodologies strategically employ tiered screening wherein the number of compositions screened decreases as the complexity, and very often the scientific information obtained from a screening experiment, increases. The algorithm used for down-selection of samples from higher throughput screening experiment to a lower throughput screening experiment is vital in achieving information-rich experimental materials genomes.

High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer.

We have developed an on-the-fly scanning spectrometer operating in the UV-visible and near-infrared that can simultaneously perform transmission and total reflectance measurements at the rate better than 1 sample per second. High throughput optical characterization is important for screening functional materials for a variety of new applications. We demonstrate the utility of the instrument for screening new light absorber materials by measuring the spectral absorbance, which is subsequently used for deriving band gap information through Tauc plot analysis.

Colorimetric screening for high-throughput discovery of light absorbers.

High-throughput screening is a powerful approach for identifying new functional materials in unexplored material spaces. With library synthesis capable of producing 10(5) to 10(6) samples per day, methods for material screening at rates greater than 1 Hz must be developed. For the discovery of new solar light absorbers, this throughput cannot be attained using standard instrumentation.

Statistical analysis and interpolation of compositional data in materials science.

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space.

High-throughput bubble screening method for combinatorial discovery of electrocatalysts for water splitting.

Combinatorial synthesis and screening for discovery of electrocatalysts has received increasing attention, particularly for energy-related technologies. High-throughput discovery strategies typically employ a fast, reliable initial screening technique that is able to identify active catalyst composition regions. Traditional electrochemical characterization via current-voltage measurements is inherently throughput-limited, as such measurements are most readily performed by serial screening.

Data mining for isotope discrimination in atom probe tomography.

Ions with similar time-of-flights (TOF) can be discriminated by mapping their kinetic energy. While current generation position-sensitive detectors have been considered insufficient for capturing the isotope kinetic energy, we demonstrate in this paper that statistical learning methodologies can be used to capture the kinetic energy from all of the parameters currently measured by mathematically transforming the signal. This approach works because the kinetic energy is sufficiently described by the descriptors on the potential, the material, and the evaporation process within atom probe tomography (APT).

Calibration of reconstruction parameters in atom probe tomography using a single crystallographic orientation.

The purpose of this work is to develop a methodology to estimate the APT reconstruction parameters when limited crystallographic information is available. Reliable spatial scaling of APT data currently requires identification of multiple crystallographic poles from the field desorption image for estimating the reconstruction parameters. This requirement limits the capacity of accurately reconstructing APT data for certain complex systems, such as highly alloyed systems and nanostructured materials wherein more than one pole is usually not observed within one grain.

Interactive visualization of APT data at full fidelity.

Understanding the impact of noise and incomplete data is a critical need for using atom probe tomography effectively. Although many tools and techniques have been developed to address this problem, visualization of the raw data remains an important part of this process. In this paper, we present two contributions to the visualization of data acquired through atom probe tomography.

Refining spatial distribution maps for atom probe tomography via data dimensionality reduction methods.

A mathematical framework based on singular value decomposition is used to analyze the covariance among interatomic frequency distributions in spatial distribution maps (SDMs). Using this approach, singular vectors that capture the covariance within the SDM data are obtained. The structurally relevant singular vectors (SRSVs) are identified.