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.

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.

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.

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 (1.2-2.8 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.

Advanced and In Situ Analytical Methods for Solar Fuel Materials.

In situ and operando techniques can play important roles in the development of better performing photoelectrodes, photocatalysts, and electrocatalysts by helping to elucidate crucial intermediates and mechanistic steps. The development of high throughput screening methods has also accelerated the evaluation of relevant photoelectrochemical and electrochemical properties for new solar fuel materials. In this chapter, several in situ and high throughput characterization tools are discussed in detail along with their impact on our understanding of solar fuel materials.

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.

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.

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. A total of 72 hours of automated operation was realized with actions including liquid management, cell cleaning, aliquoting, PA injection, and bubble detection and removal, demonstrating further advancements in automated stability testing for electrocatalysts.

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.

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.

High throughput X-ray diffraction analysis of combinatorial polycrystalline thin film libraries.

With the increasing demand for new materials, analytical techniques which are able to rapidly characterize a large number of samples are becoming indispensable. Thin film technology has the potential to improve the amount of information contained on as-deposited samples by creating compositionally graded libraries. Conventionally, raster scan methods are used to interrogate such libraries but, in this paper, a different approach is presented to provide a method of high-throughput data collection and analysis using an X-ray diffraction (XRD) probe. An extended X-ray beam was used to illuminate the libraries, and a large area detector was used to collect the data. A new algorithm "Bandit" has been employed to analyze the collected data and extract the crystallographic information. The results of the technique have been compared with the raster scans showing that the algorithm provides reliable data at a significantly increased data acquisition speed.

Cosputtered composition-spread reproducibility established by high-throughput x-ray fluorescence.

We describe the characterization of sputtered yttria-zirconia composition spread thin films by x-ray fluorescence (XRF). We also discuss our automated analysis of the XRF data, which was collected in a high throughput experiment at the Cornell High Energy Synchrotron Source. The results indicate that both the composition reproducibility of the library deposition and the composition measurements have a precision of better than 1 atomic percent.

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.

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.

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.

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.