A representation-independent electronic charge density database for crystalline materials.

In addition to being the core quantity in density-functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With the increasing prevalence of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem-classifying reactions into distinct families-and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets, as well as those based on reaction fingerprints derived from masked language modelling. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space.

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally "innocent" Na2CO3 precursor to enable the metathesis synthesis of single-phase Y2Mn2O7: an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li2CO3 and K2CO3 under identical conditions. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y2Mn2O7, thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. This approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

Quantum chemical calculations of lithium-ion battery electrolyte and interphase species.

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

A framework for quantifying uncertainty in DFT energy corrections.

In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation.

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.

Database of ab initio L-edge X-ray absorption near edge structure.

The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.

A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis.

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.

Defect-Accommodating Intermediates Yield Selective Low-Temperature Synthesis of YMnO3 Polymorphs.

In the synthesis of complex oxides, solid-state metathesis provides low-temperature reactions where product selectivity can be achieved through simple changes in precursor composition. The influence of precursor structure, however, is less understood in solid-state synthesis. Here we present the ternary metathesis reaction (LiMnO2 + YOCl → YMnO3 + LiCl) to target two yttrium manganese oxide products, hexagonal and orthorhombic YMnO3, when starting from three different LiMnO2 precursors. Using temperature-dependent synchrotron X-ray and neutron diffraction, we identify the relevant intermediates and temperature regimes of reactions along the pathway to YMnO3. Manganese-containing intermediates undergo a charge disproportionation into a reduced Mn(II,III) tetragonal spinel and oxidized Mn(III,IV) cubic spinel, which lead to hexagonal and orthorhombic YMnO3, respectively. Density functional theory calculations confirm that the presence of Mn(IV) caused by a small concentration of cation vacancies (∼2.2%) in YMnO3 stabilizes the orthorhombic polymorph over the hexagonal. Reactions over the course of 2 weeks yield o-YMnO3 as the majority product at temperatures below 600 °C, which supports an equilibration of cation defects over time. Controlling the composition and structure of these defect-accommodating intermediates provides new strategies for selective synthesis of complex oxides at low temperatures.

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.

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.

High-throughput computation and evaluation of raman spectra.

Raman spectroscopy is used ubiquitously in the characterization of condensed materials, spanning from biomaterials, minerals to polymers, as it provides a unique fingerprint of local bonding and environment. In this work, we design and demonstrate a robust, automatic computational workflow for Raman spectra that employs first-principle calculations based on density functional perturbation theory. A set of computational results are compared to Raman spectra obtained from established experimental databases to estimate the accuracy of the calculated properties across chemical systems and structures. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data record.

The existence and impact of persistent ferroelectric domains in MAPbI3.

Methylammonium lead iodide (MAPbI3) exhibits exceptional photovoltaic performance, but there remains substantial controversy over the existence and impact of ferroelectricity on the photovoltaic response. We confirm ferroelectricity in MAPbI3 single crystals and demonstrate mediation of the electronic response by ferroelectric domain engineering. The ferroelectric response sharply declines above 57°C, consistent with the tetragonal-to-cubic phase transition. Concurrent band excitation piezoresponse force microscopy-contact Kelvin probe force microscopy shows that the measured response is not dominated by spurious electrostatic interactions. Large signal poling (>16 V/cm) orients the permanent polarization into large domains, which show stabilization over weeks. X-ray photoemission spectroscopy results indicate a shift of 400 meV in the binding energy of the iodine core level peaks upon poling, which is reflected in the carrier concentration results from scanning microwave impedance microscopy. The ability to control the ferroelectric response provides routes to increase device stability and photovoltaic performance through domain engineering.

Thermodynamic limit for synthesis of metastable inorganic materials.

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.

Theory-Guided Synthesis of a Metastable Lead-Free Piezoelectric Polymorph.

Many technologically critical materials are metastable under ambient conditions, yet the understanding of how to rationally design and guide the synthesis of these materials is limited. This work presents an integrated approach that targets a metastable lead-free piezoelectric polymorph of SrHfO3 . First-principles calculations predict that the previous experimentally unrealized, metastable P4mm phase of SrHfO3 should exhibit a direct piezoelectric response (d33 ) of 36.9 pC N-1 (compared to d33 = 0 for the ground state). Combining computationally optimized substrate selection and synthesis conditions lead to the epitaxial stabilization of the polar P4mm phase of SrHfO3 on SrTiO3 . The films are structurally consistent with the theory predictions. A ferroelectric-induced large signal effective converse piezoelectric response of 5.2 pm V-1 for a 35 nm film is observed, indicating the ability to predict and target multifunctionality. This illustrates a coupled theory-experimental approach to the discovery and realization of new multifunctional polymorphs.

High-throughput density-functional perturbation theory phonons for inorganic materials.

The knowledge of the vibrational properties of a material is of key importance to understand physical phenomena such as thermal conductivity, superconductivity, and ferroelectricity among others. However, detailed experimental phonon spectra are available only for a limited number of materials, which hinders the large-scale analysis of vibrational properties and their derived quantities. In this work, we perform ab initio calculations of the full phonon dispersion and vibrational density of states for 1521 semiconductor compounds in the harmonic approximation based on density functional perturbation theory. The data is collected along with derived dielectric and thermodynamic properties. We present the procedure used to obtain the results, the details of the provided database and a validation based on the comparison with experimental data.

Computational Approach for Epitaxial Polymorph Stabilization through Substrate Selection.

With the ultimate goal of finding new polymorphs through targeted synthesis conditions and techniques, we outline a computational framework to select optimal substrates for epitaxial growth using first principle calculations of formation energies, elastic strain energy, and topological information. To demonstrate the approach, we study the stabilization of metastable VO2 compounds which provides a rich chemical and structural polymorph space. We find that common polymorph statistics, lattice matching, and energy above hull considerations recommends homostructural growth on TiO2 substrates, where the VO2 brookite phase would be preferentially grown on the a-c TiO2 brookite plane while the columbite and anatase structures favor the a-b plane on the respective TiO2 phases. Overall, we find that a model which incorporates a geometric unit cell area matching between the substrate and the target film as well as the resulting strain energy density of the film provide qualitative agreement with experimental observations for the heterostructural growth of known VO2 polymorphs: rutile, A and B phases. The minimal interfacial geometry matching and estimated strain energy criteria provide several suggestions for substrates and substrate-film orientations for the heterostructural growth of the hitherto hypothetical anatase, brookite, and columbite polymorphs. These criteria serve as a preliminary guidance for the experimental efforts stabilizing new materials and/or polymorphs through epitaxy. The current screening algorithm is being integrated within the Materials Project online framework and data and hence publicly available.