Data Sources

Topology, trajectory, and AiiDA data provenance files from molecular dynamics simulations of biomolecules are stored in a repository specifically designed for their preservation and accessibility.

The CSD is the world's largest curated database of real-world, small molecule organic and metal-organic crystal structures. All data are reviewed and annotated by expert scientists to enable easy retrieval and subsequent reuse in a range of contexts. The CSD can be used to guide molecular and materials design by comparing features of new molecules and materials against what has been experimentally observed to identify areas of risk or strategies for optimisation. The CSD can be searched via the PSDI Cross Data Search service allowing results to be combined with other data sources to generate novel insights to guide the design of new molecules and materials.

The database model for the CDI underpins the knowledge graph of a research community, linking research products (publications, data) and entities (researchers, institutions). It enables data discovery, showcases collaborations, and provides insights into research output. While centred on Published Works, Researchers, Institutions, Research Themes, and Data Objects, it can be extended to support domain-specific data (e.g., chemical entities, materials, analysis methods) tailored to the needs of different communities.

The Chemical Availability Search helps researchers find and compare chemicals from various suppliers for their projects. It saves time and resources by identifying readily available compounds, budget-friendly options, or alternatives. Users can search by drawing a molecule or entering a compound name through the PSDI Cross Data Search service. The tool provides details for over 250,000 chemicals from UK suppliers, including product name, supplier, country, quantity, price, unit price, catalogue number, and a link to the supplier's website.

The Collaborative Computational Project for NMR Crystallography (CCP-NC) database of calculated solid-state NMR data from DFT codes in MAGRES format.

This dataset is derived from the National Institute of Standards and Technology (NIST) publication, "Critical Micelle Concentrations of Aqueous Surfactant Systems" (http://doi.org/10.6028/NBS.NSRDS.36). The data was extracted using the AI-driven Data Revival service (http://www.data-revival.com/), then manually enhanced by adding SMILES strings, and finally reprocessed by Data Revival to be fully converted into a machine-readable format for improved accessibility and interoperability.

Open-access collection of crystal structures of organic, inorganic, metal-organic compounds and minerals, excluding biopolymers

Data-to-Knowledge Collections provide curated datasets specifically designed for use in machine learning or generated through machine learning processes. An example is the Machine Learning Interatomic Potentials (MLIPs) data collection, which includes MLIP XYZ files used for training, the trained MLIP models, and, where available, additional metadata such as AIIDA provenance information. By making these datasets accessible, researchers without the resources to generate such data themselves can leverage them for machine learning. Additionally, the MLIP models can be directly applied in modeling tasks, enabling broader exploration and advancements in research.

The Reproducible XAFS Analyses Zenodo Community provides the published results of X-ray absorption fine structure (XAFS) analyses reproduced using X-ray absorption spectroscopy (XAS) Galaxy tools developed in collaboration with PSDI, and use cases and data from the UK Catalysis Hub. The results shared in the Reproducible XAFS Analyses Zenodo Community are in the form of FAIR Data Objects in RO-Crate format and provide examples relevant to the catalysis domain.

This collection of datasets focuses on solubility-related information, including boiling points, Henry's law constants, LogS, melting points, and mole fractions. The Physical Chemistry Properties Data Collection provides carefully curated and standardized data on key chemical properties, empowering researchers to effectively model, analyze, and drive innovation in the field of physical chemistry.

Propersea predicts a wide range of molecular and physicochemical properties for small molecules, such as melting and boiling points, density, solubility, polarizability, and more. Propersea can also predict IUPAC names using a machine learning model developed by the PSDS team. It employs various algorithms, including RDKit, semi-empirical quantum methods, Bayesian regression trees, and transformer neural networks. Results include predicted values, confidence intervals (for Bayesian models), and comparisons to molecules in the training set. Propersea excels with organic compounds but performs less reliably with inorganics and organometallics, as indicated by lower reliability metrics. These property predictions play a vital role in physical sciences, helping researchers save time and resources while accelerating the development of innovative materials, reactions, treatments, and others.

Description of provider: Automatic FLOW (AFLOW) database for computational materials science. Description of dataset: The AFLOW OPTIMADE endpoint

Description of provider: A collection of databases from the group of Prof Miguel A. L. Marques at Ruhr University Bochum. Description of dataset: A dataset of 2.5m+ stable and metastable materials calculated with the PBE functional

Description of provider: A collection of databases from the group of Prof Miguel A. L. Marques at Ruhr University Bochum. Description of dataset: A new dataset of 415k stable and metastable materials calculated with the PBEsol and SCAN functionals

The Chemotion Repository is an open repository of well-structured and machine-readable chemical data created by the Karlsruhe Institute of Technology. The focus of the repository is synthetic and analytical chemistry data. It includes data pertaining to reactions, molecules and physical samples. Data in the repository is uploaded by the community, and is subject to peer review.

CMR is a collection of materials repositories from different projects such as C2DB, QPOD and many more

Description of provider: The Material Properties Open Database (MPOD) is a web-based, open access repository of quantitative information about the physical properties of crystalline materials. MPOD is oriented to design engineers, scientists, science teachers and students. Properties are generally treated as tensor magnitudes. In MPOD the compact matrix notation is applied. To bring an intuitive view of tensor properties, so-called longitudinal properties surfaces are displayed. 3D printing of properties surfaces is implemented via creation of stl files. A dictionary of properties definitions is included. Eventually, comments are added. Syntax and notation in MPOD files is oriented towards matching IUCr standards and so tries to comply with CIF format. Description of dataset: MPOD is a web-based, open access repository of quantitative information about the physical properties of crystalline materials.

Material-Property-Descriptor Database (MPDD) of atomic structures. Optimized for the high-throughput deployment of material featurizers and ML models. Maintained by Phases Research Lab (phaseslab.org) at The Pennsylvania State University

Description of provider: Databases contributed by the community to the Materials Cloud Archive. Description of dataset: V. Trinquet et al., Optical materials discovery and design with federated databases and machine learning, Materials Cloud Archive 2024.114 (2024) doi: 10.24435/materialscloud:5p-vq

Description of provider: Databases contributed by the community to the Materials Cloud Archive. Description of dataset: Z. Wang et al., Machine learning-accelerated discovery of A2BC2 ternary electrides with diverse anionic electron densities, Materials Cloud Archive 2023.181 (2023) doi: 10.24435/materialscloud:c8-gy

Description of provider: Databases contributed by the community to the Materials Cloud Archive. Description of dataset: L. Kahle et al., High-throughput computational screening for solid-state Li-ion conductors, Materials Cloud Archive 2024.65 (2024) doi: 10.24435/materialscloud:vg-ya

Description of provider: Databases contributed by the community to the Materials Cloud Archive. Description of dataset: Y. He et al., Machine learning on multiple topological materials datasets, Materials Cloud Archive 2025.32 (2025) doi: 10.24435/materialscloud:zk-gc

Description of provider: A platform for Open Science built for seamless sharing of resources in computational materials science. Description of dataset: Quantum spin Hall insulator (QSHI) candidates through DFT band structure calculations.

Description of provider: A platform for Open Science built for seamless sharing of resources in computational materials science. Description of dataset: Tail-corrections in molecular simulations for adsorption of gasses in a diverse set of nanoporous crystalline materials (zeolites, Covalent Organic Framworks (COFs), and Metal Organic Frameworks (MOFs)).

Description of provider: A platform for Open Science built for seamless sharing of resources in computational materials science. Description of dataset: Validation results of an automated protocol for generating maximally-localized Wannier functions in a high-throughput framework.