Electrode Surface Heating with Organic Films Improves CO2 Reduction Kinetics on Copper.

Management of the electrode surface temperature is an understudied aspect of (photo)electrode reactor design for complex reactions, such as CO2 reduction. In this work, we study the impact of local electrode heating on electrochemical reduction of CO2 reduction. Using the ferri/ferrocyanide open circuit voltage as a reporter of the effective reaction temperature, we reveal how the interplay of surface heating and convective cooling presents an opportunity for cooptimizing mass transport and thermal assistance of electrochemical reactions, where we focus on reduction of CO2 to carbon-coupled (C2+) products. The introduction of an organic coating on the electrode surface facilitates well-behaved electrode kinetics with near-ambient bulk electrolyte temperature. This approach helps to probe the fundamentals of thermal effects in electrochemical reactions, as demonstrated through Bayesian inference of Tafel kinetic parameters from a suite of high throughput experiments, which reveal a decrease in overpotential for C2+ products by 0.1 V on polycrystalline copper via 60 °C surface heating.

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. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

Zinc Titanium Nitride Semiconductor toward Durable Photoelectrochemical Applications.

Photoelectrochemical fuel generation is a promising route to sustainable liquid fuels produced from water and captured carbon dioxide with sunlight as the energy input. Development of these technologies requires photoelectrode materials that are both photocatalytically active and operationally stable in harsh oxidative and/or reductive electrochemical environments. Such photocatalysts can be discovered based on co-design principles, wherein design for stability is based on the propensity for the photocatalyst to self-passivate under operating conditions and design for photoactivity is based on the ability to integrate the photocatalyst with established semiconductor substrates. Here, we report on the synthesis and characterization of zinc titanium nitride (ZnTiN2) that follows these design rules by having a wurtzite-derived crystal structure and showing self-passivating surface oxides created by electrochemical polarization. The sputtered ZnTiN2 thin films have optical absorption onsets below 2 eV and n-type electrical conduction of 3 S/cm. The band gap of this material is reduced from the 3.36 eV theoretical value by cation-site disorder, and the impact of cation antisites on the band structure of ZnTiN2 is explored using density functional theory. Under electrochemical polarization, the ZnTiN2 surfaces have TiO2- or ZnO-like character, consistent with Materials Project Pourbaix calculations predicting the formation of stable solid phases under near-neutral pH. These results show that ZnTiN2 is a promising candidate for photoelectrochemical liquid fuel generation and demonstrate a new materials design approach to other photoelectrodes with self-passivating native operational surface chemistry.

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings.

Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

Designing a Zn-Ag Catalyst Matrix and Electrolyzer System for CO2 Conversion to CO and Beyond.

CO2 emissions can be transformed into high-added-value commodities through CO2 electrocatalysis; however, efficient low-cost electrocatalysts are needed for global scale-up. Inspired by other emerging technologies, the authors report the development of a gas diffusion electrode containing highly dispersed Ag sites in a low-cost Zn matrix. This catalyst shows unprecedented Ag mass activity for CO production: -614 mA cm-2 at 0.17 mg of Ag. Subsequent electrolyte engineering demonstrates that halide anions can further improve stability and activity of the Zn-Ag catalyst, outperforming pure Ag and Au. Membrane electrode assemblies are constructed and coupled to a microbial process that converts the CO to acetate and ethanol. Combined, these concepts present pathways to design catalysts and systems for CO2 conversion toward sought-after products.

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA's performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.

Breaking Scaling Relationships in CO2 Reduction on Copper Alloys with Organic Additives.

Boundary conditions for catalyst performance in the conversion of common precursors such as N2, O2, H2O, and CO2 are governed by linear free energy and scaling relationships. Knowledge of these limits offers an impetus for designing strategies to alter reaction mechanisms to improve performance. Typically, experimental demonstrations of linear trends and deviations from them are composed of a small number of data points constrained by inherent experimental limitations. Herein, high-throughput experimentation on 14 bulk copper bimetallic alloys allowed for data-driven identification of a scaling relationship between the partial current densities of methane and C2+ products. This strict dependence represents an intrinsic limit to the Faradaic efficiency for C-C coupling. We have furthermore demonstrated that coating the electrodes with a molecular film breaks the scaling relationship to promote C2+ product formation.

Discovery of complex oxides via automated experiments and data science.

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Optical Identification of Materials Transformations in Oxide Thin Films.

Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization methods are limited by speed, cost, availability, and/or resolution. To make efficient use of these methods, there is value in developing approaches for identifying critical compositions and conditions to be used as a priori knowledge for follow-up characterization with high-precision techniques, such as micrometer-scale synchrotron-based X-ray diffraction (XRD). Here, we demonstrate the use of optical microscopy and reflectance spectroscopy to identify likely phase-change boundaries in thin film libraries. These methods are used to delineate possible metastable phase boundaries following lateral-gradient laser spike annealing (lg-LSA) of oxide materials. The set of boundaries are then compared with definitive determinations of structural transformations obtained using high-resolution XRD. We demonstrate that the optical methods detect more than 95% of the structural transformations in a composition-gradient La-Mn-O library and a Ga2O3 sample, both subject to an extensive set of lg-LSA anneals. Our results provide quantitative support for the value of optically detected transformations as a priori data to guide subsequent structural characterization, ultimately accelerating and enhancing the efficient implementation of micrometer-resolution XRD experiments.

Bi Alloying into Rare Earth Double Perovskites Enhances Synthesizability and Visible Light Absorption.

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm2MnNiO6 (double perovskite structure), of the form (Bi,Sm)2MnNiO6. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

Combinatorial Synthesis of Oxysulfides in the Lanthanum-Bismuth-Copper System.

Establishing synthesis methods for a target material constitutes a grand challenge in materials research, which is compounded with use-inspired specifications on the format of the material. Solar photochemistry using thin film materials is a promising technology for which many complex materials are being proposed, and the present work describes application of combinatorial methods to explore the synthesis of predicted La-Bi-Cu oxysulfide photocathodes, in particular alloys of LaCuOS and BiCuOS. The variation in concentration of three cations and two anions in thin film materials, and crystallization thereof, is achieved by a combination of reactive sputtering and thermal processes including reactive annealing and rapid thermal processing. Composition and structural characterization establish composition-processing-structure relationships that highlight the breadth of processing conditions required for synthesis of LaCuOS and BiCuOS. The relative irreducibility of La oxides and limited diffusion indicate the need for high temperature processing, which conflicts with the temperature limits for mitigating evaporation of Bi and S. Collectively the results indicate that alloys of these phases will require reactive annealing protocols that are uniquely tailored to each composition, motivating advancement of dynamic processing capabilities to further automate discovery of synthesis routes.

High-throughput, combinatorial synthesis of multimetallic nanoclusters.

Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space necessitates an accelerated discovery pipeline that supersedes traditional "trial-and-error" experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications.

Benchmarking the acceleration of materials discovery by sequential learning.

Sequential learning (SL) strategies, i.e. 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. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.

Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments.

With the increasing energy demand, developing renewable fuel production strategies such as photoelectrocatalytic hydrogen production is critical to mitigating the global climate change. In this work, we experimentally validate a new stable and photoactive material, Mg2MnO4, from the exhaustive theoretical exploration of the chemical space of X (=Mg and Ca), Mn and O.

Scanning Electrochemical Flow Cell with Online Mass Spectroscopy for Accelerated Screening of Carbon Dioxide Reduction Electrocatalysts.

Electrochemical conversion of carbon dioxide into valuable chemicals or fuels is an increasingly important strategy for achieving carbon neutral technologies. The lack of a sufficiently active and selective electrocatalyst, particularly for synthesizing highly reduced products, motivates accelerated screening to evaluate new catalyst spaces. Traditional techniques, which couple electrocatalyst operation with analytical techniques to measure product distributions, enable screening throughput at 1-10 catalysts per day. In this paper, a combinatorial screening instrument is designed for MS detection of hydrogen, methane, and ethylene in quasi-real-time during catalyst operation experiments in an electrochemical flow cell. Coupled with experiment modeling, product detection during cyclic voltammetry (CV) enables modeling of the voltage-dependent partial current density for each detected product. We demonstrate the technique by using the well-established thin film Cu catalysts and by screening a Pd-Zn composition library in carbonate-buffered aqueous electrolyte. The rapid product distribution characterization over a large range of overpotential makes the instrument uniquely suited for accelerating screening of electrocatalysts for the carbon dioxide reduction reaction.

Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides.

Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties.

Machine learning of optical properties of materials - predicting spectra from images and images from spectra.

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 unique quinary oxide and 67 000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 180 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena yielding the type of complex data relationships that merit and benefit from neural network-type modelling.

Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery.

The photocatalytic conversion of the greenhouse gas CO2 to chemical fuels such as hydrocarbons and alcohols continues to be a promising technology for renewable generation of energy. Major advancements have been made in improving the efficiencies and product selectiveness of currently known CO2 reduction electrocatalysts, nonetheless, materials discovery is needed to enable economically viable, industrial-scale CO2 reduction. We report here the largest CO2 photocathode search to date, starting with 68860 candidate materials, using a rational first-principles computation-based screening strategy to evaluate synthesizability, corrosion resistance, visible-light absorption, and compatibility of the electronic structure with fuel synthesis. The results confirm the observation of the literature that few materials meet the stringent CO2 photocathode requirements, with only 52 materials meeting all requirements. The results are well validated with respect to the literature, with 9 of these materials having been studied for CO2 reduction, and the remaining 43 materials are discoveries from our pipeline that merit further investigation.

Progress and prospects for accelerating materials science with automated and autonomous workflows.

Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery.

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. The photoactivity increases upon mixture with 10-60% Ni6MnO8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation.