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Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identifies the structure and composition and confirms distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies.

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design.

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.

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

Hydrogen peroxide is a valuable chemical oxidant with a wide range of applications in a variety of industrial processes, especially in water sanitization. Electrochemical synthesis of hydrogen peroxide (H2O2) through a two-electron oxygen reduction reaction (2e-ORR) or a two-electron water oxidation reaction (2e-WOR) has emerged as an appealing process for onsite production of this chemically valuable oxidant. On-site produced H2O2 can be applied for wastewater treatment in remote locations or any applications where H2O2 is needed as an oxidizing agent. This Review studies the theoretical efforts in understanding the challenges in catalysis for electrochemical synthesis of H2O2 as well as providing design principles for more efficient catalyst materials.

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir-H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed.

Photoelectrocatalysts that use sunlight to power the CO2 reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO2 reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO2 photoreduction candidates.

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform.

We investigated the distribution, C:N elemental ratio, and δ¹³C of suspended particulate carbon in the surface and upper 100 m of the water column during three seasons in areas of the Western Tropical North Atlantic influenced by the Amazon River Plume: the Spring high flow period (KN197 Cruise, May-June), the late Summer period of reduced flow (AT2104 cruise, July), and the low flow period in the Fall (MV1110 cruise, Sept-Oct). We used a habitat delineation method to examine spatial and temporal variability in our biogeochemical parameters. We found the highest biomass concentration ([PC]=259.7 µM), high C:N ratio (13.6), and the most negative δ¹³C (-26.8 ‰) in the area proximate to the river mouth during the late summer cruise. We measured elevated [PC] (64.5 µM), C:N ratios (14.1), and δ¹³C (max -15.7 ‰) in the plume core habitats during the peak flow season, reflecting the impact of both the outflow and in situ phytoplankton production. We found that the western margin of the plume had relatively higher biomass, C:N ratios, and organic matter more enriched in 13C than the east margins. In our work area, oceanic waters had the lowest [PC] (1.3 µM), a C:N ratio higher than the Redfield ratio (6.6), and an average δ¹³C of -22 ‰. We explored the relationships between PC and Chla, and δ¹³C and C:N ratio to estimate the contribution of phytoplankton production and terrigenous sources to the suspended particulate carbon pool. We found terrestrial and detrital sources contributing more to the organic matter of the river mouth area. However, the contribution of phytoplankton and living sources dominate most of our study area in all seasons. Our findings emphasize the role of the Amazon River Plume in enhancing biomass and productivity of the WTNA and the biogeochemical dynamic of the carbon cycle. [ABSTRACT FROM AUTHOR]

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.

Electrocatalysis of the oxygen evolution reaction is central to several energy technologies including electrolyzers, solar fuel generators, and air-breathing batteries. Strong acid electrolytes are desirable for many implementations of these technologies, although the deployment of such device designs is often hampered by the lack of non-precious-metal oxygen evolution electrocatalysts, with Ir-based oxides comprising the only known catalysts that exhibit stable activity at low overpotential. During our exploration of the Mn-Sb-O system for precious-metal-free electrocatalysts, we discovered that Mn can be incorporated into the rutile oxide structure at much higher concentrations than previously known, and that these Mn-rich rutile alloys exhibit great catalytic activity with current densities exceeding 50 mA cm-2 at 0.58 V overpotential and catalysis onset at 0.3 V overpotential. While this activity does not surpass that of IrO2, Pourbaix analysis reveals that the Mn-Sb rutile oxide alloys have the same or better thermodynamic stability under operational conditions. By combining combinatorial composition, structure, and activity mapping with synchrotron X-ray absorption measurements and first-principles materials chemistry calculations, we provide a comprehensive understanding of these oxide alloys and identify the critical role of Sb in stabilizing the trivalent Mn octahedra that have been shown to be effective oxygen evolution reaction (OER) catalysts.

As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.

We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility. Built on top of open source Python packages already in use by the materials community such as pymatgen, FireWorks, and custodian, atomate provides well-tested workflow templates to compute various materials properties such as electronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomate also enables the computational characterization of materials by providing workflows that calculate X-ray absorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate is that it provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools. Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations.

Auxetics comprise a rare family of materials that manifest negative Poisson's ratio, which causes an expansion instead of contraction under tension. Most known homogeneously auxetic materials are porous foams or artificial macrostructures and there are few examples of inorganic materials that exhibit this behavior as polycrystalline solids. It is now possible to accelerate the discovery of materials with target properties, such as auxetics, using high-throughput computations, open databases, and efficient search algorithms. Candidates exhibiting features correlating with auxetic behavior were chosen from the set of more than 67 000 materials in the Materials Project database. Poisson's ratios were derived from the calculated elastic tensor of each material in this reduced set of compounds. We report that this strategy results in the prediction of three previously unidentified homogeneously auxetic materials as well as a number of compounds with a near-zero homogeneous Poisson's ratio, which are here denoted "anepirretic materials".There are very few inorganic materials with auxetic homogenous Poisson's ratio in polycrystalline form. Here authors develop an approach to screening materials databases for target properties such as negative Poisson's ratio by using stability and structural motifs to predict new instances of homogenous auxetic behavior as well as a number of materials with near-zero Poisson's ratio.

The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API. [ABSTRACT FROM AUTHOR]

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

The Amazon River plume (ARP) has been shown to support high rates of nitrogen fixation and primary production. However, nitrogen fixation alone cannot account for total primary production determined in the region, hinting that other nitrogen uptake processes might play a role. For the first time, we measured nitrate uptake rates in the ARP during three cruises in May 2018, June 2019 and April/May 2021, along with primary production rates and an analysis of phytoplankton community composition via high performance liquid chromatography. Based on a classification according to the salt content the region was divided into estuarine (ES), mesohaline (MH) and oceanic (OC) stations. Primary production was light limited near the river mouth at ES stations and was maximal off the coasts of French Guiana and Suriname, where also nitrate uptake was highest with rates of 11.4 mmol m−2 d−1. The role of eddies pinching off a deflecting plume are discussed as possible reason for higher nutrient concentrations at the MH stations. Surprisingly, at most MH stations north of 5°N, nitrate uptake rates were low despite the presence of sufficient substrate concentration (up to 1.44 μM nitrate). Diatoms, dinoflagellates or Synechococcus sp. dominated phytoplankton communities. OC stations showed lowest productivity rates in accordance with oligotrophic conditions. However, rates seem to be sufficient to completely deplete the remaining riverine nitrate, preventing its export to the open ocean. Plain Language Summary: The Amazon River is thought to deliver important nutrients to the Western Tropical North Atlantic, which enable high phytoplankton growth and especially favor nitrogen fixers. Previous studies have focused on nitrogen fixation, but we propose that other nitrogen sources could also be important in the river plume to support the high phytoplankton growth there. In this study, we measured the uptake of nitrate by phytoplankton in the plume for the first time. We found that nitrate uptake is of variable importance for phytoplankton growth in the Amazon River plume; it seems to be more important close to the river mouth, where diatoms and dinoflagellates dominate, compared to the northern plume, where nitrogen fixers are dominant. Investigations like this will help us to better understand, which nitrogen sources are important for the growth of different phytoplankton species in the plume. With this information, we can improve predictions of competition of phytoplankton species for nitrogen and their general growth in the future. Some phytoplankton groups have a higher potential to contribute to oceanic CO2 storage than others, so understanding the future of these phytoplankton groups is critical to any assessment of the amount of CO2 that can be stored by the ocean. Key Points: Nitrate uptake rates were measured in the Amazon River plume for the first time to resolve its role for overall productivity in the plumeNitrate uptake displayed a pronounced gradient and was not only regulated by concentrationsNitrate from the Amazon River is completely consumed on the shelf and not exported to the open ocean [ABSTRACT FROM AUTHOR]

Hydrographic data, nutrient data and bulk rates of nitrate uptake and primary production were determined in the Amazon River plume (ARP) in the Western Tropical North Atlantic (WTNA) during three cruises in May 2018, June/July 2019, with RV Endeavor and April/May 2021 with RV Meteor. Using daily quasi‐geostrophic surface velocity data from satellite observations, the geographical positions of the stations of observations were transformed onto Lagrangian coordinates to obtain a dynamically coherent and consistent spatial distribution. After the transformation, the observed surface salinity and temperature fields were consistent with the flow fields, the ARP formed a coherent structure and the retroflection of the North Brazil Current became visible. By transforming other surface variables such as nitrate concentration, photosynthetically available radiation, turbidity, bulk rates of nitrate uptake, and primary production onto Lagrangian coordinates, patterns became consistent with the physical variables at the surface. The use of "synchronous" fields as done here by transformation onto Lagrangian coordinates is essential for spatially structured analyses of data collected over tens of days in a highly dynamic region characterized by complex flow fields with low persistence such as the WTNA. Therefore, the use of the Lagrangian method provides a powerful tool for exploring spatial distributions of biologically relevant factors in regions with complex and dynamic flow patterns. These spatial distributions are qualitatively in agreement with satellite images of daily sea surface temperature and composites of monthly mean Chlorophyll a distributions. [ABSTRACT FROM AUTHOR]