The space of metastable materials offers promising new design opportunities for next-generation technological materials, such as complex oxides, semiconductors, pharmaceuticals, steels, and beyond. Although metastable phases are ubiquitous in both nature and technology, only a heuristic understanding of their underlying thermodynamics exists. We report a large-scale data-mining study of the Materials Project, a high-throughput database of density functional theory-calculated energetics of Inorganic Crystal Structure Database structures, to explicitly quantify the thermodynamic scale of metastability for 29,902 observed inorganic crystalline phases. We reveal the influence of chemistry and composition on the accessible thermodynamic range of crystalline metastability for polymorphic and phase-separating compounds, yielding new physical insights that can guide the design of novel metastable materials. We further assert that not all low-energy metastable compounds can necessarily be synthesized, and propose a principle of 'remnant metastability'-that observable metastable crystalline phases are generally remnants of thermodynamic conditions where they were once the lowest free-energy phase.

Realizing the growing number of possible or hypothesized metastable crystalline materials is extremely challenging. There is no rigorous metric to identify which compounds can or cannot be synthesized. We present a thermodynamic upper limit on the energy scale, above which the laboratory synthesis of a polymorph is highly unlikely. The limit is defined on the basis of the amorphous state, and we validate its utility by effectively classifying more than 700 polymorphs in 41 common inorganic material systems in the Materials Project for synthesizability. The amorphous limit is highly chemistry-dependent and is found to be in complete agreement with our knowledge of existing polymorphs in these 41 systems, whether made by the nature or in a laboratory. Quantifying the limits of metastability for realizable compounds, the approach is expected to find major applications in materials discovery.

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Footwear, carpet, automotive interiors, and multilayer packaging are examples of products manufactured from several types of polymers whose inextricability poses substantial challenges for recycling at the end of life. Here, we show that chemical circularity in mixed-polymer recycling becomes possible by controlling the rates of depolymerization of polydiketoenamines (PDK) over several orders of magnitude through molecular engineering. Stepwise deconstruction of mixed-PDK composites, laminates, and assemblies is chemospecific, allowing a prescribed subset of monomers, fillers, and additives to be recovered under pristine condition at each stage of the recycling process. We provide a theoretical framework to understand PDK depolymerization via acid-catalyzed hydrolysis and experimentally validate trends predicted for the rate-limiting step. The control achieved by PDK resins in managing chemical and material entropy points to wide-ranging opportunities for pairing circular design with sustainable manufacturing.

Two major challenges hinder the advance of aqueous zinc metal batteries for sustainable stationary storage: (1) achieving predominant Zn-ion (de)intercalation at the oxide cathode by suppressing adventitious proton co-intercalation and dissolution, and (2) simultaneously overcoming Zn dendrite growth at the anode that triggers parasitic electrolyte reactions. Here, we reveal the competition between Zn2+ vs proton intercalation chemistry of a typical oxide cathode using ex-situ/operando techniques, and alleviate side reactions by developing a cost-effective and non-flammable hybrid eutectic electrolyte. A fully hydrated Zn2+ solvation structure facilitates fast charge transfer at the solid/electrolyte interface, enabling dendrite-free Zn plating/stripping with a remarkably high average coulombic efficiency of 99.8% at commercially relevant areal capacities of 4 mAh cm-2 and function up to 1600 h at 8 mAh cm-2. By concurrently stabilizing Zn redox at both electrodes, we achieve a new benchmark in Zn-ion battery performance of 4 mAh cm-2 anode-free cells that retain 85% capacity over 100 cycles at 25 °C. Using this eutectic-design electrolyte, Zn | |Iodine full cells are further realized with 86% capacity retention over 2500 cycles. The approach represents a new avenue for long-duration energy storage.

There is an increasing need for sustainable energy storage solutions as fossil fuels are replaced by renewable energy sources. Multivalent batteries, specifically Mg batteries, are one energy storage technology that researchers continue to develop with hopes to surpass the performance of Li-ion batteries. However, the limited energy density and transport properties of Mg cathodes remain critical challenges preventing the realization of high-performance multivalent batteries. In this work, ABO4 zircon materials (A = Y, Eu and B = V, Cr) are computationally and experimentally evaluated as Mg intercalation cathodes. Remarkably good Mg-ion transport properties were predicted and Mg-ion intercalation was experimentally verified in sol-gel synthesized zircon YVO4, EuVO4, and EuCrO4. Among them, EuVO4 exhibited the best electrochemical performance and demonstrated repeated reversible cycling. While we believe that the one-dimensional diffusion channels and redox-active species tetragonal coordination limit the value of many zircons as high-performance cathodes, their unique structural motif of overlapping polyhedra along the diffusion pathway appears instrumental for promoting good Mg-ion mobility. The motif results in a favorable "6-5-4" change in coordination that avoids unfavorable sites with lower coordination along the diffusion pathway and a structural design metric for future Mg cathode development.

The solvation shell structures of Ca2+ in aqueous and organic solutions probed by calcium L-edge soft X-ray absorption spectroscopy (XAS) and DFT/MD simulations show the coordination number of Ca2+ to be negatively correlated with the electrolyte concentration and the steric hindrance of the solvent molecule. In this work, the calcium L-edge soft XAS demonstrates its sensitivity to the surrounding chemical environment. Additionally, the total electron yield (TEY) mode is surface sensitive because the electron penetration depth is limited to a few nanometers. Thus this study shows its implications for future battery studies, especially for probing the electrolyte/electrode interface for electrochemical reactions under in situ/operando conditions.

The local structure of a transition metal (TM) ion is a function of cation elements and valence states. More than that, in this work, by employing a trove of first-principles data of TM oxides, the local structures of TM cations are statistically analyzed to extract detailed information about cation site preference, bond length, site structural distortion, and cation magnetization. It is found that cation radius alone poorly describes the local structure of a transition metal oxide, while the statistics of coordination number as well as the TMO bond length distribution, especially that of the 3d TMs, can provide comprehensive knowledge for understanding the behavior of TM elements. Based on these statistics, the interplay of site distortion due to the Jahn-Teller effect, cation site similarity, and a new set of ionic radii are all obtained to chart the "persona" of transition metal ions in solids.

Despite the importance of studying the instability of delithiated cathode materials, it remains difficult to underpin the degradation mechanism of lithium-rich cathode materials due to the complication of combined chemical and structural evolutions. Herein, we use state-of-the-art electron microscopy tools, in conjunction with synchrotron X-ray techniques and first-principle calculations to study a 4d-element-containing compound, Li2Ru0.5Mn0.5O3. We find surprisingly, after cycling, ruthenium segregates out as metallic nanoclusters on the reconstructed surface. Our calculations show that the unexpected ruthenium metal segregation is due to its thermodynamic insolubility in the oxygen deprived surface. This insolubility can disrupt the reconstructed surface, which explains the formation of a porous structure in this material. This work reveals the importance of studying the thermodynamic stability of the reconstructed film on the cathode materials and offers a theoretical guidance for choosing manganese substituting elements in lithium-rich as well as stoichiometric layer-layer compounds for stabilizing the cathode surface.

Rechargeable solid-state sodium-ion batteries (SSSBs) hold great promise for safer and more energy-dense energy storage. However, the poor electrochemical stability between current sulfide-based solid electrolytes and high-voltage oxide cathodes has limited their long-term cycling performance and practicality. Here, we report the discovery of the ion conductor Na3-xY1-xZrxCl6 (NYZC) that is both electrochemically stable (up to 3.8 V vs. Na/Na+) and chemically compatible with oxide cathodes. Its high ionic conductivity of 6.6 × 10-5 S cm-1 at ambient temperature, several orders of magnitude higher than oxide coatings, is attributed to abundant Na vacancies and cooperative MCl6 rotation, resulting in an extremely low interfacial impedance. A SSSB comprising a NaCrO2 + NYZC composite cathode, Na3PS4 electrolyte, and Na-Sn anode exhibits an exceptional first-cycle Coulombic efficiency of 97.1% at room temperature and can cycle over 1000 cycles with 89.3% capacity retention at 40 °C. These findings highlight the immense potential of halides for SSSB applications.

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community's COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm and theoretically exfoliated them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data ( http://www.2dmatpedia.org/ ).

Seven human induced pluripotent stem cell (iPSC) lines were generated from fibroblasts from three neonatal individuals using non-integrative reprogramming. Most control iPSCs are derived from adults, so these iPSCs meet the need for control iPSCs from young individuals. Donors were from different ethnicities and these lines provide unique genetic profiles. All iPSCs have normal karyotypes, express stem cell markers, and exhibit pluripotency, as assessed by capacity to differentiate into three germ layers. These lines are valuable to study human development, as age-matched controls for disorder-specific iPSCs, and as platforms for gene editing to control for age and ethnicity.

Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of grain boundaries is about three orders of magnitude lower than that of the bulk. In contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.

The characterization of water-based corrosion, geochemical, environmental and catalytic processes rely on the accurate depiction of stable phases in a water environment. The process is aided by Pourbaix diagrams, which map the equilibrium solid and solution phases under varying conditions of pH and electrochemical potential. Recently, metastable or possibly stable nanometric aqueous clusters have been proposed as intermediate species in non-classical nucleation processes. Herein, we describe a Group Additivity approach to obtain Pourbaix diagrams with full consideration of multimeric cluster speciation from computations. Comparisons with existing titration results from experiments yield excellent agreement. Applying this Group Additivity-Pourbaix approach to Group 13 elements, we arrive at a quantitative evaluation of cluster stability, as a function of pH and concentration, and present compelling support for not only metastable but also thermodynamically stable multimeric clusters in aqueous solutions.

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable "synthesis by design" in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials.

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full 29Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease.

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.