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 materials1,2, 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. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

Selective Synthesis of Defect-Rich LaMnO3 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 LaMnO3, 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 p(O2) conditions to manipulate the oxygen content postsynthesis (e.g., LaMnO3+x). Double-ion exchange (metathesis) reactions have recently been shown to react at much lower temperatures (500-800 °C), highlighting a fundamental knowledge gap for how solids react at lower temperatures. Here, we revisit the metathesis reaction, LiMnO2 + LaOX, where X is a halide or mixture of halides, using in situ synchrotron X-ray diffraction. These experiments reveal low reaction onset temperatures (ca. 450-480 °C). The lowest reaction temperatures are achieved by a mixture of lanthanum oxyhalide precursors: 2 LiMnO2 + LaOCl + LaOBr. In all cases, the resulting products are the expected alkali halide salt and defective La1-ϵMn1-ϵO3, where ϵ = x/(3 + x). We observe a systematic variation in defect concentration, consistent with a rapid stoichiometric local equilibration of the precursors and the subsequent global thermodynamic equilibration with O2 (g), as revealed by computational thermodynamics. Together, these results reveal how the inclusion of additional elements (e.g., Li and a halide) leads to the local equilibrium, particularly at low reaction temperatures for solid-state chemistry.

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 MgCrMnO4 with 18% Mg/Mn inversion was synthesized successfully. A new custom operando battery device was designed to study the cation migration mechanisms of the MgCrMnO4 cathode using 0.1 M Mg(TPFA)2 electrolyte dissolved in triglyme and activated carbon as the anode. For the first time in multivalent batteries, high-quality operando diffraction data enabled the accurate quantification of cation contents in the host structure. Besides the exceptional reversibility of 12% Mg2+ insertion in Mg1-xCrMnO4 (x ≤ 1), a partially reversible insertion of excess Mg2+ during overdischarging was also observed. Moreover, the insertion/extraction reaction was experimentally shown to be accompanied by a series of cation redistributions in the spinel framework, which were further supported by density functional theory calculations. The inverted Mn is believed to be directly involved in the cation migrations, which would cause voltage hysteresis and irreversible structural evolution after overdischarging. Tuning the Mg/Mn inversion rate could provide a direct path to further optimize spinel oxide cathodes for Mg-ion batteries, and more generally, the operando techniques developed in this work should play a key role in understanding the complex mechanisms involved in multivalent ion insertion systems.

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 (Cs2BB'Cl6) 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. We predict that 261 of the 311 Cs2BB'Cl6 compounds are likely synthesizable on the basis of a thermodynamic analysis of their decomposition to competing compounds (decomposition enthalpy <0.05 eV/atom). Of these 261 likely synthesizable compounds, 47 contain no toxic elements and have direct or nearly direct (within 100 meV) band gaps between 1 and 3 eV, as computed with hybrid density functional theory (HSE06). Within this set, we identify the triple-alkali perovskites Cs2[Alk]+[TM]3+Cl6, where Alk is a group 1 alkali cation and TM is a transition-metal cation, as a class of Cs2BB'Cl6 double perovskites with remarkable optical properties, including large and tunable exciton binding energies as computed by the GW-Bethe-Salpeter equation (GW-BSE) method. We attribute the unusual electronic structure of these compounds to the mixing of the Alk-Cl and TM-Cl sublattices, leading to materials with small band gaps, large exciton binding energies, and absorption spectra that are strongly influenced by the identity of the transition metal. The role of the double-perovskite structure in enabling these unique properties is probed through an analysis of the electronic structures and chemical bonding of these compounds in comparison with other transition-metal and alkali transition-metal halides.

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. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation-from which we experimentally realized seven new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity and covalency of solid-state bonding from the density functional theory (DFT)-computed electron density, we reveal the complex interplay between chemistry, composition and electronic structure in governing large-scale stability trends in ternary nitride materials.

First-principles study of CaB12H12 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.

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. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.

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. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO4, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation.

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3.

We used data-driven methods to understand the formation of impurity phases in BiFeO3 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. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of "missingness". We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.

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. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli-electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

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 (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material's formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

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. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.

Testing the r2SCAN 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 r2SCAN 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 r2SCAN description of solid-state thermodynamics, we evaluate the formation and decomposition enthalpies, equilibrium volumes, and fundamental band gaps of more than 1000 solids using r2SCAN, SCAN, and PBE, as well as two dispersion-corrected variants, SCAN+rVV10 and r2SCAN+rVV10. We show that r2SCAN achieves accuracy comparable to SCAN and often improves upon SCAN's already excellent accuracy. Although SCAN+rVV10 is often observed to worsen the formation enthalpies of SCAN and makes no substantial correction to SCAN's cell volume predictions, r2SCAN+rVV10 predicts marginally less accurate formation enthalpies than r2SCAN, and slightly more accurate cell volumes than r2SCAN. The average absolute errors in predicted formation enthalpies are found to decrease by a factor of 1.5 to 2.5 from the GGA level to the meta-GGA level. Smaller decreases in error are observed for decomposition enthalpies. For formation enthalpies r2SCAN improves over SCAN for intermetallic systems. For a few classes of systems-transition metals, intermetallics, weakly bound solids, and enthalpies of decomposition into compounds-GGAs are comparable to meta-GGAs. In total, r2SCAN and r2SCAN+rVV10 can be recommended as stable, general-purpose meta-GGAs for materials discovery.

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

Expanding the Ambient-Pressure Phase Space of CaFe2O4-Type Sodium Postspinel Host-Guest Compounds.

CaFe2O4-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 NaCrTiO4, NaRhTiO4, NaCrSnO4, NaInSnO4, NaMg0.5Ti1.5O4, NaFe0.5Ti1.5O4, NaMg0.5Sn1.5O4, NaMn0.5Sn1.5O4, NaFe0.5Sn1.5O4, NaCo0.5Sn1.5O4, NaNi0.5Sn1.5O4, NaCu0.5Sn1.5O4, NaZn0.5Sn1.5O4, NaCd0.5Sn1.5O4, NaSc1.5Sb0.5O4, Na1.16In1.18Sb0.66O4, and several solid solutions. In contrast to earlier reports, even cations that are strongly Jahn-Teller active (e.g., Mn3+ and Cu2+) can form Na-CFs at ambient pressure when combined with Sn4+ rather than with the smaller Ti4+. Order and disorder are probed at the average and local length-scales with synchrotron powder X-ray diffraction and solid-state NMR spectroscopy. Strong ordering of framework cations between the two framework sites is not observed, except in the case of Na1.16In1.18Sb0.66O4. This compound is the first example of an Na-CF that contains Na+ in both the tunnel and framework sites, reminiscent of Li-rich spinels. Trends in the thermodynamic stability of the new compounds are explained on the basis of crystal-chemistry and density functional theory (DFT). Further DFT calculations examine the relative stability of the CF versus spinel structures at various degrees of sodium extraction in the context of electrochemical battery reactions.

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. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies (ΔG f , ΔH f ). In contrast, features derived from the thermodynamics of synthesis-related reactions did not directly correlate to the chosen heating temperatures. This correlation between optimal solid-state heating temperature and precursor stability extends Tamman's rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in determining synthesis conditions. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of human bias in the data set. Using these predictive features, we constructed machine-learning models with good performance and general applicability to predict the conditions required to synthesize diverse chemical systems.

High-Throughput Selection and Experimental Realization of Two New Ce-Based Nitride Perovskites: CeMoN3 and CeWN3.

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, CeWN3 and CeMoN3, to the list of experimentally realized perovskite nitrides using high-throughput computational screening and subsequent high-throughput thin film growth techniques. Candidate compositions are first down-selected using a tolerance factor and then thermochemical stability. A novel competing fluorite-family phase is identified for both material systems, which we hypothesize is a transient intermediate phase that crystallizes during the evolution from an amorphous material to a stable perovskite. Different processing routes to overcome the competing fluorite phase and obtain phase-pure nitride perovskites are demonstrated for the CeMoN3-x and CeWN3-x material systems, which provide a starting point for the development of future nitride perovskites. Additionally, we find that these new perovskite phases have interesting low-temperature magnetic behavior: CeMoN3-x orders antiferromagnetically below T N ≈ 8 K with indications of strong magnetic frustration, while CeWN3-x exhibits no long-range order down to T = 2 K but has strong antiferromagnetic correlations. This work demonstrates the importance and effectiveness of using high-throughput techniques, both computational and experimental: they are integral to optimize the process of realizing two entirely novel nitride perovskites.

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.

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. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.