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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.

Guided by computational Pourbaix screening and high-throughput experiments aimed at the development of precious-metal-free fuel cells, we investigate rutile CoSb₂O₆ as an electrocatalyst for oxygen reduction in 1 M sulfuric acid. Following 4 h of catalyst conditioning at 0.7 V vs RHE, operation at this potential for 20 h yielded an average current density of −0.17 mA cm⁻² with corrosion at a rate of 0.04 nm hour⁻¹ that is stoichiometric with catalyst composition. Surface Pourbaix analysis of the (111) surface identified partial H coverage under operating conditions. The Sb active site has an HO* binding free energy of 0.49 eV, which is near the peak of the kinetic 4e⁻ ORR volcano for transition-metal oxides in acidic conditions. The experimental demonstration of operational stability and computational identification of a reaction pathway with favorable energetics place rutile CoSb₂O₆ among the most promising precious-metal-free electrocatalysts for oxygen reduction in acidic media.

The oxygen evolution reaction (OER) is central to several sustainable energy technologies. Catalyst development has largely focused on lowering the overpotential and eliminating reliance on precious metals, revealing stark differences in alkaline and acidic OER. In alkaline electrolyte, precious metal-free catalysts have approached the limiting overpotential from established free energy scaling relationships, and our survey of complex metal oxides shows that this limit can be approached with a broad range of catalysts. In acidic electrolyte, electrochemical instabilities create a dual challenge of a dearth of nonprecious metal OER catalysts with overpotential below 0.5 V and a high dissolved metals concentration for most precious metal-free catalysts. On device-relevant time scales, the high dissolved metals concentrations compromise device stability, for example, through a decrease of performance and due to metal exchange between anode and cathode catalysts due to finite permeability of ion exchange membranes. These considerations motivate a substantial increase in monitoring and reporting of dissolved metals concentrations in OER experiments. To facilitate durability-based screening in continued catalyst discovery campaigns, we introduce a durability descriptor based on the d-electron count of each metal element compared to that of its Pourbaix-stable oxidation state, which enables rapid down-selection of candidate metal oxide catalysts. We discuss the importance of a codesign approach to catalyst development, where a device architecture can set specific requirements for dissolved metals concentrations and/or cathode and anode catalysts can be designed to tolerate cross-contamination. This device-level guidance of basic science will facilitate deployment of new catalysts to meet the societal needs for accelerated sustainable technology development.

In this perspective, we highlight results of a research consortium devoted to advancing understanding of oxygen reduction reaction (ORR) catalysis as a means to inform fuel cell science.

The oxygen reduction electrocatalysis is a key reaction for energy technologies such as fuel cells and air breathing batteries for which development of stable and efficient electrocatalysts remains a substantial challenge, especially when excluding precious metals. Multi-component metal oxides are complex materials that offer opportunities for catalyst development and challenges for prediction of optimal compositions. We use high throughput synthesis and electrochemistry to explore metal oxide electrocatalysts containing 1 to 4 of the elements Mg, Ca, Mn, Fe, Ni, Y, In, and La. Mining the resulting dataset of 7798 unique metal oxide compositions guides additional activity and stability experiments that reveal multiple avenues for further catalyst development based on (i) stabilization of Mn through incorporation in Mn-Ni–Fe oxides, (ii) discovery of Ni–Fe-La oxide electrocatalysts with excellent stability, and (iii) discovery of Mn-Ni–Fe-La oxide electrocatalysts that optimize activity while retaining excellent operational stability.

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 experimentation on the reduction of CO2 on copper alloys led to the identification of a scaling relationship between CH4 and C2+ products that can be broken with an organic additive.

Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal phases, or mixtures thereof, in X-ray diffraction measurements of synthesized materials. Phase mapping algorithms have been developed that excel at solving systems with up to several unique phase mixtures, where each phase has a readily distinguishable diffraction pattern. However, phase mapping is often beyond materials scientists’ capabilities and also poses challenges to state-of-the-art algorithms due to complexities such as the existence of dozens of phase mixtures, alloy-dependent variation in the diffraction patterns and multiple compositional degrees of freedom, creating a major bottleneck in high-throughput materials discovery. Here we show how to automate crystal-structure phase mapping. We formulate phase mapping as an unsupervised pattern demixing problem and describe how to solve it using deep reasoning networks (DRNets). DRNets combine deep learning with constraint reasoning for incorporating prior scientific knowledge and consequently require only a modest amount of (unlabelled) data. DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals. DRNets are designed with an interpretable latent space for encoding prior-knowledge domain constraints and seamlessly integrate constraint reasoning into neural network optimization. DRNets surpass previous approaches on crystal-structure phase mapping, unravelling the Bi–Cu–V oxide phase diagram and aiding the discovery of solar fuels materials. Incorporating prior knowledge in deep learning models can overcome the difficulties of supervised learning, including the need for large amounts of annotated data. An approach in this area called deep reasoning networks is applied to the complex task of mapping crystal structures from X-ray diffraction data for multi-element oxide structures, and identified 13 phases from 307 X-ray diffraction patterns in the previously unsolved Bi-Cu-V oxide system.

In the search for photoanode materials with band gaps suitable for utilization in solar fuel generation, approximately 1.2–2.8 eV, theory-guided experiments have identified a variety of materials that meet the band gap requirements and exhibit operational stability in harsh photoelectrochemical environments. In particular, M-V-O compounds (M is a transition metal or main group element) with VO₄ structural motifs were predicted to show a remarkably wide range of band energetics (>3 eV variation in the energy of valence band maximum) and characteristics, depending on the M and crystal structure, which is beyond the extent of electronic structured tuning observed in previously studied families of metal oxide photoanodes. While this finding guided experimental discovery of new photoanode materials, explicit experimental verification of the theoretical prediction of the tunable electronic structure of these materials has been lacking to date. In this study, we use X-ray photoelectron spectroscopy and Kelvin probe microscopy to experimentally investigate the electronic structure of M-V-O photoanodes, enabling comparison to theory on a common absolute energy scale. The results confirm the prediction that band edge energies of ternary vanadates vary significantly with metal cations. The valence band variation of approximately 1 eV observed here is larger than that reported in any analogous class of metal oxide semiconductors and demonstrates the promise of tuning the metal oxide electronic structure to enable efficient photoelectrocatalysis of the oxygen evolution reaction and beyond. Because midgap states can hamper realization of the high photovoltage sought by band edge tuning, we analyze the electronic contributions of oxygen vacancies for the representative photoanode V₄Cr₂O₁₃ to guide future research on the development of high-efficiency metal oxide photoanodes for solar fuel technology.

Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

Summary We report an active zinc-copper (Zn-Cu) bimetallic electrocatalyst for CO2 reduction to CO, prepared by a facile galvanic procedure. Under moderate overpotentials, Zn-Cu catalysts that are Zn rich exhibit intrinsic activity for CO formation superior to that of pure Zn, Cu, and Ag, the last of which is the state-of-the-art catalyst in CO2 electrolyzers. Combinatorial experiments involving catalysts prepared by physical vapor deposition reveal trends across the Zn-Cu system, corroborating the high CO selectivity unrivaled by other alloys and intermetallics. Physical and electrochemical characterization and first principles theory reveal that the origin of this synergy in intrinsic activity is an electronic effect from bimetallic Zn-Cu sites that stabilizes the carboxyl intermediate during CO2 reduction to CO. Furthermore, by integrating Zn-Cu into gas-diffusion electrodes, we demonstrate that bimetallic effects lead to improved electrocatalytic performance at industrially relevant currents. These insights provide catalyst design principles that can guide future development of efficient and earth-abundant CO-producing electrocatalysts.

Renewable generation of fuels using solar energy is a promising technology whose deployment hinges on the discovery of materials with a combination of durability and solar-to-chemical conversion efficiency that has yet to be demonstrated. Stable operation of photoanodes has been demonstrated with wide-gap semiconductors, as well as protected visible gap semiconductors. Visible photoresponse from electrochemically stable materials is quite rare. In this paper, we report the high-throughput discovery of an amorphous Ni-Sb (1:1) oxide photoanode that meets the requirements of operational stability, visible photoresponse, and appreciable photovoltage. X-ray absorption characterization of Ni and Sb establishes a structural connection to rutile NiSb₂O₆, guiding electronic structure characterization via X-ray photoelectron experiments and density functional theory. This amorphous photoanode opens avenues for photoelectrode development due to the lack of crystal anisotropy combined with its operational stability, which mitigates the formation of an interphase that disrupts the semiconductor-electrolyte junction.

Computational sustainability harnesses computing and artificial intelligence for human well-being and the protection of our planet. Materials science is central to many sustainability challenges. Exploiting synergies between computational sustainability and materials science advances both fields, furthering the ultimate goal of establishing a sustainable future.

Summary Copper vanadates are a promising class of solar fuel photoanodes with broad spectral response and excellent operational stability. The present performance limitations of these photoanodes are most evident in the rapid decrease in photoactivity with decrease in either photon energy or applied bias, the former limiting efficient utilization of the solar spectrum and the latter limiting photovoltage. We designed high-throughput photoelectrochemical screening to characterize these two aspects of improving Cu-V-based photoanodes in quaternary oxide composition spaces of the form Cu-V-X-O, where X = Mg, Ca, Sr, Fe. The results reveal that alloying of 2+ cations onto the Cu site of copper vanadates can improve photoelectrochemical properties, and Sr-alloyed Cu5V2O10 emerges as the most promising photoanode providing the best combination of photovoltage and spectral response. Six quaternary oxide phases are discovered as photoanodes with visible light activity below 1.23 V versus reversible hydrogen electrode, highlighting the high-throughput photoanode discovery.

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.

The importance of metal oxide photoanodes in solar fuels technology has garnered concerted efforts in photoanode discovery in recent decades, which complement parallel efforts in development of analytical techniques and optimization strategies using standard photoanodes such as TiO₂, Fe2O₃ and BiVO₄. Theoretical guidance of high throughput experiments has been particularly effective in dramatically increasing the portfolio of metal oxide photoanodes, motivating a new era of photoanode development where the characterization and optimization techniques developed on traditional materials are applied to nascent photoanodes that exhibit visible light photoresponse. The compendium of metal oxide photoanodes presented in the present work can also serve as the basis for further technique development, with a primary goal to establish workflows for discovery of materials that perform better against the critical criteria of operational stability, visible light photoresponse, and photovoltage suitable for tandem absorber architectures.

Significance Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials., 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.

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 such 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 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 0.1 S/cm. The band gap of this material is reduced from the 3.5 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.