How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.

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.

Why understanding food choice is crucial to transform food systems for human and planetary health.

What, how and why people eat has long been understood to be important for human health, but until recently, has not been recognised as an essential facet of climate change and its effects on planetary health. The global climate change and diet-related health crises occurring are connected to food systems, food environments and consumer food choices. Calls to transform food systems for human and planetary health highlight the importance of understanding individual food choice. Understanding what, how and why people eat the way they do is crucial to successful food systems transformations that achieve both human and planetary health goals. Little is known about how food choice relates to climate. To clarify potential paths for action, we propose that individual food choice relates to climate change through three key mechanisms. First, the sum of individual food choices influences the supply and demand of foods produced and sold in the marketplace. Second, individual food decisions affect type and quantity of food waste at the retail and household level. Third, individual food choices serve as a symbolic expression of concern for human and planetary health, which can individually and collectively stimulate social movements and behaviour change. To meet the dietary needs of the 2050 global population projection of 10 billion, food systems must transform. Understanding what, how and why people eat the way they do, as well as the mechanisms by which these choices affect climate change, is essential for designing actions conducive to the protection of both human and planetary health.

Novel inorganic crystal structures predicted using autonomous simulation agents.

We report a dataset of 96640 crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery). Of these, 894 are within 1 meV/atom of the convex hull and 26826 are within 200 meV/atom of the convex hull. The dataset contains DFT-optimized pymatgen crystal structure objects, DFT-computed formation energies and phase stability calculations from the convex hull. It contains a variety of spacegroups and symmetries derived from crystal prototypes derived from known experimental compounds, and was generated from active learning campaigns of various chemical systems. This dataset can be used to benchmark future active-learning or generative efforts for structure prediction, to seed new efforts of experimental crystal structure discovery, or to construct new models of structure-property relationships.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries.

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

Agents for sequential learning using multiple-fidelity data.

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values. The fidelities of data come from the results of DFT calculations as low fidelity and experimental results as high fidelity. We demonstrate performance gains of agents which incorporate multi-fidelity data in two contexts: either using a large body of low fidelity data as a prior knowledge base or acquiring low fidelity data in-tandem with experimental data. This advance provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. This may also serve as a reference point for those who are interested in practical strategies that can be used when multiple data sources are available for active or sequential learning campaigns.

Machine learning-accelerated design and synthesis of polyelemental heterostructures.

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning­driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

Small pigmented eukaryote assemblages of the western tropical North Atlantic around the Amazon River plume during spring discharge.

Small pigmented eukaryotes (⩽ 5 µm) are an important, but overlooked component of global marine phytoplankton. The Amazon River plume delivers nutrients into the oligotrophic western tropical North Atlantic, shades the deeper waters, and drives the structure of microphytoplankton (> 20 µm) communities. For small pigmented eukaryotes, however, diversity and distribution in the region remain unknown, despite their significant contribution to open ocean primary production and other biogeochemical processes. To investigate how habitats created by the Amazon river plume shape small pigmented eukaryote communities, we used high-throughput sequencing of the 18S ribosomal RNA genes from up to five distinct small pigmented eukaryote cell populations, identified and sorted by flow cytometry. Small pigmented eukaryotes dominated small phytoplankton biomass across all habitat types, but the population abundances varied among stations resulting in a random distribution. Small pigmented eukaryote communities were consistently dominated by Chloropicophyceae (0.8-2 µm) and Bacillariophyceae (0.8-3.5 µm), accompanied by MOCH-5 at the surface or by Dinophyceae at the chlorophyll maximum. Taxonomic composition only displayed differences in the old plume core and at one of the plume margin stations. Such results reflect the dynamic interactions of the plume and offshore oceanic waters and suggest that the resident small pigmented eukaryote diversity was not strongly affected by habitat types at this time of the year.

Automated Adsorption Workflow for Semiconductor Surfaces and the Application to Zinc Telluride.

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.

Rational Solid-State Synthesis Routes for Inorganic Materials.

The rational solid-state synthesis of inorganic compounds is formulated as catalytic nucleation on crystalline reactants, where contributions of reaction and interfacial energies to the nucleation barriers are approximated from high-throughput thermochemical data and structural and interfacial features of crystals, respectively. Favorable synthesis reactions are then identified by a Pareto analysis of relative nucleation barriers and phase selectivities of reactions leading to the target. We demonstrate the application of this approach in reaction planning for the solid-state synthesis of a range of compounds, including the widely studied oxides LiCoO2, BaTiO3, and YBa2Cu3O7, as well as other metal oxide, oxyfluoride, phosphate, and nitride targets. Pathways for enabling the retrosynthesis of inorganics are also discussed.

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

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.

Mapping spatial and temporal variation of seafloor organic matter Δ14C and δ13C in the Northern Gulf of Mexico following the Deepwater Horizon Oil Spill.

Following the Deepwater Horizon oil spill of 2010, large amounts of biodegraded oil (petrocarbon) sank to the seafloor. Our objectives were to 1) determine post-spill isotopic values as the sediments approached a new baseline and 2) track the recovery of affected sediments. Sediment organic carbon δ13C and Δ14C reached a post-spill baseline averaging -21.2 ± 0.9‰ (n = 129) and -220 ± 66‰ (n = 95). Spatial variations in seafloor organic carbon baseline isotopic values, 13C and 14C, were influenced by river discharge and hydrocarbon seepage, respectively. Inverse Distance Weighting of surface sediment Δ14C values away from seep sites showed a 50% decrease in the total mass of petrocarbon, from 2010 to 2014. We estimated a rate of loss of -2 × 109 g of petrocarbon-C/year, 2-11% of the degradation rates in surface slicks. Despite the observed recovery in sediments, lingering residual material in the surface sediments was evident seven years following the blowout.

Autonomous intelligent agents for accelerated materials discovery.

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.

Efficient Pourbaix diagrams of many-element compounds.

Pourbaix diagrams have been used extensively to evaluate stability regions of materials subject to varying potential and pH conditions in aqueous environments. However, both recent advances in high-throughput material exploration and increasing complexity of materials of interest for electrochemical applications pose challenges for performing Pourbaix analysis on multidimensional systems. Specifically, current Pourbaix construction algorithms incur significant computational costs for systems consisting of four or more elemental components. Herein, we propose an alternative Pourbaix construction method that filters all potential combinations of species in a system to only those present on a compositional convex hull. By including axes representing the quantities of H+ and e- required to form a given phase, one can ensure every stable phase mixture is included in the Pourbaix diagram and reduce the computational time required to construct the resultant Pourbaix diagram by several orders of magnitude. This new Pourbaix algorithm has been incorporated into the pymatgen code and the Materials Project website, and it extends the ability to evaluate the Pourbaix stability of complex multicomponent systems.

The great Atlantic Sargassum belt.

Pelagic Sargassum is abundant in the Sargasso Sea, but a recurrent great Atlantic Sargassum belt (GASB) has been observed in satellite imagery since 2011, often extending from West Africa to the Gulf of Mexico. In June 2018, the 8850-kilometer GASB contained >20 million metric tons of Sargassum biomass. The spatial distribution of the GASB is mostly driven by ocean circulation. The bloom of 2011 might be a result of Amazon River discharge in previous years, but recent increases and interannual variability after 2011 appear to be driven by upwelling off West Africa during boreal winter and by Amazon River discharge during spring and summer, indicating a possible regime shift and raising the possibility that recurrent blooms in the tropical Atlantic and Caribbean Sea may become the new norm.

Petrocarbon evolution: Ramped pyrolysis/oxidation and isotopic studies of contaminated oil sediments from the Deepwater Horizon oil spill in the Gulf of Mexico.

Hydrocarbons released during the Deepwater Horizon (DWH) oil spill weathered due to exposure to oxygen, light, and microbes. During weathering, the hydrocarbons' reactivity and lability was altered, but it remained identifiable as "petrocarbon" due to its retention of the distinctive isotope signatures (14C and 13C) of petroleum. Relative to the initial estimates of the quantity of oil-residue deposited in Gulf sediments based on 2010-2011 data, the overall coverage and quantity of the fossil carbon on the seafloor has been attenuated. To analyze recovery of oil contaminated deep-sea sediments in the northern Gulf of Mexico we tracked the carbon isotopic composition (13C and 14C, radiocarbon) of bulk sedimentary organic carbon through time at 4 sites. Using ramped pyrolysis/oxidation, we determined the thermochemical stability of sediment organic matter at 5 sites, two of these in time series. There were clear differences between crude oil (which decomposed at a lower temperature during ramped oxidation), natural hydrocarbon seep sediment (decomposing at a higher temperature; Δ14C = -912‰) and our control site (decomposing at a moderate temperature; Δ14C = -189‰), in both the stability (ability to withstand ramped temperatures in oxic conditions) and carbon isotope signatures. We observed recovery toward our control site bulk Δ14C composition at sites further from the wellhead in ~4 years, whereas sites in closer proximity had longer recovery times. The thermographs also indicated temporal changes in the composition of contaminated sediment, with shifts towards higher temperature CO2 evolution over time at a site near the wellhead, and loss of higher temperature CO2 peaks at a more distant site.

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.

Trends in adsorption of electrocatalytic water splitting intermediates on cubic ABO3 oxides.

The reactivity of solid oxide surfaces towards adsorption of oxygen and hydrogen is a key metric for the design of new catalysts for electrochemical water splitting. In this paper, we report on trends in the adsorption energy of different adsorbed intermediates derived from the oxidation and reduction of water for ternary ABO3 oxides in the cubic perovskite structure. Our findings support a previously reported trend that rationalizes the observed lower bound in oxygen evolution (OER) overpotentials from correlations in OH* and OOH* adsorption energies. In addition, we report hydrogen adsorption energies that may be used to estimate hydrogen evolution (HER) overpotentials along with potential metrics for electrochemical metastability in reducing environments. We also report and discuss trends between atom-projected density of states and adsorption energies, which may enable a design criteria from the local electronic structure of the active site.

Computational prediction of new auxetic materials.

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.

Electrochemical CO2 reduction on Au surfaces: mechanistic aspects regarding the formation of major and minor products.

In the future, industrial CO2 electroreduction using renewable energy sources could be a sustainable means to convert CO2 and water into commodity chemicals at room temperature and atmospheric pressure. This study focuses on the electrocatalytic reduction of CO2 on polycrystalline Au surfaces, which have high activity and selectivity for CO evolution. We explore the catalytic behavior of polycrystalline Au surfaces by coupling potentiostatic CO2 electrolysis experiments in an aqueous bicarbonate solution with high sensitivity product detection methods. We observed the production of methanol, in addition to detecting the known products of CO2 electroreduction on Au: CO, H2 and formate. We suggest a mechanism that explains Au's evolution of methanol. Specifically, the Au surface does not favor C-O scission, and thus is more selective towards methanol than methane. These insights could aid in the design of electrocatalysts that are selective for CO2 electroreduction to oxygenates over hydrocarbons.

Piecewise Structural Equation Model (SEM) Disentangles the Environmental Conditions Favoring Diatom Diazotroph Associations (DDAs) in the Western Tropical North Atlantic (WTNA).

Diatom diazotroph associations (DDAs) are important components in the world's oceans, especially in the western tropical north Atlantic (WTNA), where blooms have a significant impact on carbon and nitrogen cycling. However, drivers of their abundances and distribution patterns remain unknown. Here, we examined abundance and distribution patterns for two DDA populations in relation to the Amazon River (AR) plume in the WTNA. Quantitative PCR assays, targeting two DDAs (het-1 and het-2) by their symbiont's nifH gene, served as input in a piecewise structural equation model (SEM). Collections were made during high (spring 2010) and low (fall 2011) flow discharges of the AR. The distributions of dissolved nutrients, chlorophyll-a, and DDAs showed coherent patterns indicative of areas influenced by the AR. A symbiotic Hemiaulus hauckii-Richelia (het-2) bloom (>106 cells L-1) occurred during higher discharge of the AR and was coincident with mesohaline to oceanic (30-35) sea surface salinities (SSS), and regions devoid of dissolved inorganic nitrogen (DIN), low concentrations of both DIP (>0.1 µmol L-1) and Si (>1.0 µmol L-1). The Richelia (het-1) associated with Rhizosolenia was only present in 2010 and at lower densities (10-1.76 × 105nifH copies L-1) than het-2 and limited to regions of oceanic SSS (>36). The het-2 symbiont detected in 2011 was associated with H. membranaceus (>103nifH copies L-1) and were restricted to regions with mesohaline SSS (31.8-34.3), immeasurable DIN, moderate DIP (0.1-0.60 µmol L-1) and higher Si (4.19-22.1 µmol L-1). The piecewise SEM identified a profound direct negative effect of turbidity on the het-2 abundance in spring 2010, while DIP and water turbidity had a more positive influence in fall 2011, corroborating our observations of DDAs at subsurface maximas. We also found a striking difference in the influence of salinity on DDA symbionts suggesting a niche differentiation and preferences in oceanic and mesohaline salinities by het-1 and het-2, respectively. The use of the piecewise SEM to disentangle the complex and concomitant hydrography of the WTNA acting on two biogeochemically relevant populations was novel and underscores its use to predict conditions favoring abundance and distributions of microbial populations.