Phonons play an essential role in condensed matter physics, influencing key phenomena such as vibrational entropy, thermal conductivity, superconductivity, ferroelectricity, and photoluminescence spectra. Here, we present a comprehensive database of harmonic phonon properties, constructed using automated high-throughput (HT) density functional theory (DFT) calculations for 26,413 compounds. The database covers materials with all seven crystal systems and includes primitive cells with up to 60 atoms. In this work, phonons were computed using DFT-based (PBEsol level of theory) second-order interatomic force constants (IFCs), obtained from perturbed supercell calculations and fitted using either least-squares or LASSO-based regression depending on the number of required finite displacements. The phonon workflow is implemented in the HT software Atomate2, incorporating Pheasy, a compressive sensing lattice dynamics code. Within this framework, space group and point group symmetries, as well as the acoustic sum rule and rotational invariance constraints, are applied to the force constants to ensure physical accuracy and reduce numerical errors. This approach offers a substantial computational speedup compared to both the traditional finitedisplacement method and density functional perturbation theory, while maintaining accuracy comparable to both. The resulting phonon database includes phonon dispersions, phonon density of states (DOS), and derived thermodynamic properties such as Helmholtz free energy ( F ), entropy ( S ), and constant-volume heat capacity ( C V ). Our work not only establishes an HT methodology for phonon calculations, but also delivers a large-scale phonon database accessible to Materials Project (MP) users for a range of applications, including materials screening and follow-up computational studies.

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a unified treatment of elasticity tensors for all seven crystal systems across diverse chemical spaces, without the need to deal with each separately.. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus.

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials.

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a unified treatment of elasticity tensors for all seven crystal systems across diverse chemical spaces, without the need to deal with each separately.. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus.

# Elasticity tensors of 10276 crystals from density functional theory (DFT) computations This dataset consists of three data files in the json format. Each file is explained below. ## crystal_elasticity_tensor.json DFT computed elastic tensors of 10276 crystals used for developing the MatTen model. structure: crystal structure of the materialformula_pretty: chemical formulacrystal_system: crystal systemelastic_tensor: full fourth-rank elastic tensorelastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensorsplit: split of the data into train, validation, and test subsets for model development ## max_directional_E.json New crystals with large maximum directional Young's modulus. material_id: Materials Project identifierformula_pretty: chemical formula structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original` structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`max_directional_E: DFT maximum directional Young's modulus using `structure`elastic_tensor_matten: MatTen predicted elastic tensor using `structure`max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure` ## elemental_cubic_metal_max_E_along_100_direction.json New crystals with its maximum directional Young's modulus along the [100] direction. material_id: Materials Project identifierformula_pretty: chemical formula structure_original: crystal structure from the Materials Project databaseelastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten` structure: further DFT optimized structure with a tigher criterionelastic_tensor: DFT elastic tensor corresponding to `structure`Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`

Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to the scientific community, such datasets should follow FAIR principles (i.e. be findable, accessible, interoperable, and reusable). In this work, we present a FAIR expansion of the Materials Project database ("MPcules") that adds more than 170,000 molecules studied using density functional theory (DFT) to the existing data, which comprises crystalline solids. MPcules is a diverse collection of DFT calculated molecular properties, with an emphasis on reactive, open-shell, and charged species - relevant for studying reaction pathways - and a wide array of structural, electronic, vibrational, and thermodynamic properties. This database can be queried through an OpenAPI-compliant application programming interface and a featureful web application. We continue to expand the data available on MPcules and encourage contributions from the community.

Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way. It is intended to help beginners rapidly develop web-based apps to explore their own data or to help developers make their research algorithms accessible to a broader audience of scientists who might not have any training in computer programming and who would benefit from graphical interfaces. Crystal Toolkit comes with a library of ready-made components that can be assembled to make complex web apps: simulation of powder and single crystalline diffraction patterns, convex hull phase diagrams, Pourbaix diagrams, electronic band structures, analysis of local chemical environments and symmetry, and more. Crystal Toolkit is now powering the Materials Project website frontend, providing user-friendly access to its database of computed materials properties. In the future, it is hoped that new visualizations might be prototyped using Crystal Toolkit to help explore new forms of data being generated by the materials science community, and that this in turn can help new materials scientists develop intuition for how their data behaves and the insights that might be found within. Crystal Toolkit will remain a work-in-progress and is open to contributions from the community.

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.

With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for (opto)electronic and energy storage applications, we present a new dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the new dataset of computed MOF properties to train machine learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

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.

Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors.These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps.Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment.In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows.Science Capsule captures, manages and represents both computational and human elements of a workflow.It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts.Science Capsule also allows users to create 'workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users.Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for obtaining precise information about the local bonding of materials, but difficult to interpret without a well-vetted dataset of reference spectra. The ability to predict NMR parameters and connect them to three-dimensional local environments is critical for understanding more complex, long-range interactions. New computational methods have revealed structural information available from 29 Si solid-state NMR by generating computed reference spectra for solids. Such predictions are useful for the identification of new silicon-containing compounds, and serve as a starting point for determination of the local environments present in amorphous structures. In this study, we have used 42 silicon sites as a benchmarking set to compare experimentally reported 29 Si solid-state NMR spectra with those computed by CASTEP-NMR and Vienna Ab Initio Simulation Program (VASP). Data-driven approaches enable us to identify the source of discrepancies across a range of experimental and computational results. The information from NMR (in the form of an NMR tensor) has been validated, and in some cases corrected, in an effort to catalog these for the local spectroscopy database infrastructure (LSDI), where over 10,000 29 Si NMR tensors for crystalline materials have been computed. Knowledge of specific tensor values can serve as the basis for executing NMR experiments with precision, optimizing conditions to capture the elements accurately. The ability to predict and compare experimental observables from a wide range of structures can aid researchers in their chemical assignments and structure determination, since the computed values enables the extension beyond tables of typical chemical shift (or shielding) ranges.

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/ ).

Perovskite solid solutions are screened both experimentally and through DFT to determine their redox properties for thermochemical applications.