Unraveling the Growth Dynamics of Rutile SnGeO Using Theory and Experiment.

Rutile GeO and related materials are attracting interest due to their ultrawide band gaps and potential for ambipolar doping in high-power electronic applications. This study examines the growth of rutile SnGeO films through oxygen-plasma-assisted hybrid molecular beam epitaxy (hMBE). The film composition and thickness are evaluated across a range of growth conditions, with the outcomes rationalized by using density functional theory calculations.

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis.

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants.

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials.

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology.

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO.

We used data-driven methods to understand the formation of impurity phases in BiFeO thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature.

Selective formation of metastable polymorphs in solid-state synthesis.

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions.

Selective Synthesis of Defect-Rich LaMnO by Low-Temperature Anion Cometathesis.

The synthesis of complex oxides at low temperatures brings forward aspects of chemistry not typically considered. This study focuses on perovskite LaMnO, which is of interest for its correlated electronic behavior tied to the oxidation state and thus the spin configuration of manganese. Traditional equilibrium synthesis of these materials typically requires synthesis reaction temperatures in excess of 1000 °C, followed by subsequent annealing steps at lower temperatures and different (O) conditions to manipulate the oxygen content postsynthesis (e.

An autonomous laboratory for the accelerated synthesis of novel materials.

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.

Autonomous and dynamic precursor selection for solid-state materials synthesis.

Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS) designed to automate the selection of optimal precursors for solid-state materials synthesis.

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material.

Testing the rSCAN Density Functional for the Thermodynamic Stability of Solids with and without a van der Waals Correction.

A central aim of materials discovery is an accurate and numerically reliable description of thermodynamic properties, such as the enthalpies of formation and decomposition. The rSCAN revision of the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) balances numerical stability with high general accuracy. To assess the rSCAN description of solid-state thermodynamics, we evaluate the formation and decomposition enthalpies, equilibrium volumes, and fundamental band gaps of more than 1000 solids using rSCAN, SCAN, and PBE, as well as two dispersion-corrected variants, SCAN+rVV10 and rSCAN+rVV10.

Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.

There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis data sets text-mined from scientific journal articles.

High-Throughput Selection and Experimental Realization of Two New Ce-Based Nitride Perovskites: CeMoN and CeWN.

Nitride perovskites have only been experimentally realized in very few cases despite the widespread existence and commercial importance of perovskite materials. From oxide perovskites used in ultrasonics to halide perovskites that have revolutionized the photovoltaics industry, the discovery of new perovskite materials has historically impacted a wide number of fields. Here, we add two new perovskites, CeWN and CeMoN, to the list of experimentally realized perovskite nitrides using high-throughput computational screening and subsequent high-throughput thin film growth techniques.

Toward autonomous design and synthesis of novel inorganic materials.

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition.

Data-centric approach to improve machine learning models for inorganic materials.

Pandey et al. (2021) demonstrate the importance of diversifying training data to make balanced predictions of thermodynamic properties for inorganic crystals.

Synthetic accessibility and stability rules of NASICONs.

In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility.

Expanding the Ambient-Pressure Phase Space of CaFeO-Type Sodium Postspinel Host-Guest Compounds.

CaFeO-type sodium postspinels (Na-CFs), with Na occupying tunnel sites, are of interest as prospective battery electrodes. While many compounds of this structure type require high-pressure synthesis, several compounds are known to form at ambient pressure. Here we report a large expansion of the known Na-CF phase space at ambient pressure, having successfully synthesized NaCrTiO, NaRhTiO, NaCrSnO, NaInSnO, NaMgTiO, NaFeTiO, NaMgSnO, NaMnSnO, NaFeSnO, NaCoSnO, NaNiSnO, NaCuSnO, NaZnSnO, NaCdSnO, NaScSbO, NaInSbO, and several solid solutions.

Operando X-ray Diffraction Studies of the Mg-Ion Migration Mechanisms in Spinel Cathodes for Rechargeable Mg-Ion Batteries.

A promising high-voltage spinel oxide cathode material MgCrMnO with 18% Mg/Mn inversion was synthesized successfully. A new custom operando battery device was designed to study the cation migration mechanisms of the MgCrMnO cathode using 0.1 M Mg(TPFA) electrolyte dissolved in triglyme and activated carbon as the anode.

First-principles study of CaBH as a potential solid-state conductor for Ca.

Calcium dodecahydro-closo-dodecaborate, CaB12H12, was calculated to have a percolating Ca migration path with low activation barrier (650 meV). The formation of Ca vacancies required for diffusion was calculated to be thermodynamically feasible by substitution of Ca with Al, Bi, or a number of trivalent rare-earth cations.

Inorganic Halide Double Perovskites with Optoelectronic Properties Modulated by Sublattice Mixing.

All-inorganic halide double perovskites have emerged as a promising class of materials that are potentially more stable and less toxic than lead-containing hybrid organic-inorganic perovskite optoelectronic materials. In this work, 311 cesium chloride double perovskites (Cs'Cl) were selected from a set of 903 compounds as likely being stable on the basis of a statistically learned tolerance factor (τ) for perovskite stability. First-principles calculations on these 311 double perovskites were then performed to assess their stability and identify candidates with band gaps appropriate for optoelectronic applications.

Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteries.

Seven MgLn2X4 (Ln = lanthanoid, X = S, Se) spinels are calculated with density functional theory to have low barriers for Mg migration (<380 meV) and are stable or nearly stable (within 50 meV per atom of stability with respect to competing structures and compositions). As the size of the Ln increases, Mg mobility is found to increase, but stability in the spinel structure is found to decrease.

A map of the inorganic ternary metal nitrides.

Exploratory synthesis in new chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials.

High-Throughput Equilibrium Analysis of Active Materials for Solar Thermochemical Ammonia Synthesis.

Solar thermochemical ammonia (NH) synthesis (STAS) is a potential route to produce NH from air, water, and concentrated sunlight. This process involves the chemical looping of an active redox pair that cycles between a metal nitride and its complementary metal oxide to yield NH. To identify promising candidates for STAS cycles, we performed a high-throughput thermodynamic screening of 1,148 metal nitride/metal oxide pairs.