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.

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.

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.

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.

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.

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.

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.

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.

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.

Understanding trends in C-H bond activation in heterogeneous catalysis.

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.

Materials for solar fuels and chemicals.

The conversion of sunlight into fuels and chemicals is an attractive prospect for the storage of renewable energy, and photoelectrocatalytic technologies represent a pathway by which solar fuels might be realized. However, there are numerous scientific challenges in developing these technologies. These include finding suitable materials for the absorption of incident photons, developing more efficient catalysts for both water splitting and the production of fuels, and understanding how interfaces between catalysts, photoabsorbers and electrolytes can be designed to minimize losses and resist degradation. In this Review, we highlight recent milestones in these areas and some key scientific challenges remaining between the current state of the art and a technology that can effectively convert sunlight into fuels and chemicals.

Acetaldehyde as an Intermediate in the Electroreduction of Carbon Monoxide to Ethanol on Oxide-Derived Copper.

Oxide-derived copper (OD-Cu) electrodes exhibit unprecedented CO reduction performance towards liquid fuels, producing ethanol and acetate with >50% Faradaic efficiency at -0.3 V (vs. RHE). By using static headspace-gas chromatography for liquid phase analysis, we identify acetaldehyde as a minor product and key intermediate in the electroreduction of CO to ethanol on OD-Cu electrodes. Acetaldehyde is produced with a Faradaic efficiency of ≈5% at -0.33 V (vs. RHE). We show that acetaldehyde forms at low steady-state concentrations, and that free acetaldehyde is difficult to detect in alkaline solutions using NMR spectroscopy, requiring alternative methods for detection and quantification. Our results represent an important step towards understanding the CO reduction mechanism on OD-Cu electrodes.

Theoretical Insights into a CO Dimerization Mechanism in CO2 Electroreduction.

In this work, we present DFT simulations that demonstrate the ability of Cu to catalyze CO dimerization in CO2 and CO electroreduction. We describe a previously unreported CO dimer configuration that is uniquely stabilized by a charged water layer on both Cu(111) and Cu(100). Without this charged water layer at the metal surface, the formation of the CO dimer is prohibitively endergonic. Our calculations also demonstrate that dimerization should have a lower activation barrier on Cu(100) than Cu(111), which, along with a more exergonic adsorption energy and a corresponding higher coverage of *CO, is consistent with experimental observations that Cu(100) has a high activity for C-C coupling at low overpotentials. We also demonstrate that this effect is present with cations other than H(+), a finding that is consistent with the experimentally observed pH independence of C2 formation on Cu.

The Challenge of Electrochemical Ammonia Synthesis: A New Perspective on the Role of Nitrogen Scaling Relations.

The electrochemical production of NH3 under ambient conditions represents an attractive prospect for sustainable agriculture, but electrocatalysts that selectively reduce N2 to NH3 remain elusive. In this work, we present insights from DFT calculations that describe limitations on the low-temperature electrocatalytic production of NH3 from N2 . In particular, we highlight the linear scaling relations of the adsorption energies of intermediates that can be used to model the overpotential requirements in this process. By using a two-variable description of the theoretical overpotential, we identify fundamental limitations on N2 reduction analogous to those present in processes such as oxygen evolution. Using these trends, we propose new strategies for catalyst design that may help guide the search for an electrocatalyst that can achieve selective N2 reduction.

Theoretical evaluation of the surface electrochemistry of perovskites with promising photon absorption properties for solar water splitting.

In this work, we present first-principles calculations describing the catalytic activity for of a set of photoelectrocatalysts identified as candidates for total water splitting in a previous screening study for bulk stability and bandgap. Our Density Functional Theory (DFT) calculations of the intermediate energetics for hydrogen evolution and oxygen evolution suggest that none of the proposed materials has the ideal combination of bandgap and surface chemical properties that should allow for total water splitting in a single material. This result suggests that co-catalysts are necessary to overcome the kinetic limitations of the both reactions, although some materials may catalyze one half-reaction, as has been observed in experiment.