Data Sources

The Chemical Availability Search (ChASe) data source enables users to search and compare commercially available chemicals using the PSDI Cross Data Search service. Enter product or vendor keywords, CAS number, InChI or SMILES to search pricing and supplier information for over 250,000 unique chemicals from UK suppliers. Search results include product name, supplier name, quantity, purity, prices, CAS number, and catalogue number with a link to the supplier's website. Check the supplier's website for the most up to date product information.

Propersea contains calculated predictions for a wide range of molecular and physicochemical properties for small molecules, such as melting and boiling points, density, solubility, polarizability, and more. It employs various algorithms, including RDKit, semi-empirical quantum methods, Bayesian regression trees, and transformer neural networks. Propersea also contains predicted IUPAC names generated using a machine learning model. Propersea can be searched using the PSDI Cross Data Search service using InChIs, SMILES or by drawing a molecule. Results include predicted values, confidence intervals and reliability scores for the prediction.

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 Catalysis Data Infrastructure (CDI) Data Objects resource provides access to the UK Catalysis Hub (UKCH) published data objects that support research outputs. Through the CDI Portal and the PSDI Cross Data Search, users can search, explore, and reuse these data resources. In the Catalysis Data Infrastructure (CDI) knowledge graph, data objects are linked with researchers, institutions, publications, and research themes, fostering data discovery, highlighting collaborations, and supporting analysis of data-driven research. CDI Data Objects can be extended to include domain-specific data resources such as chemical entities, materials, or analytical methods.

The Catalysis Data Infrastructure (CDI) Publications resource provides access to the UK Catalysis Hub (UKCH) Publications, including peer-reviewed articles, books, and theses. CDI Publications offer a dedicated entry point into the CDI knowledge graph through the CDI Portal and the PSDI Cross Data Search to explore and analyse research publications. Within CDI, publications are linked to researchers (authors), institutions, research themes, and associated data objects. The linking enhances publication discovery, displays research relationships, and supports insight into research activity. CDI Publications can be expanded to include domain-specific indexes about chemicals, materials, and analysis methods.

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. This dataset collection is available for download from a Github repository as zipped csv files.

These 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. These datasets are available via PSDI Cross Data Search (https://data-search.psdi.ac.uk/find-a-substance/molecule?db=psdi.pchprop) and OPTIMADE (https://www.optimade.org/) API endpoints (https://pchprop-optimade.psdi.ac.uk/).

2DMatpedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches

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: 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: Validation results of an automated protocol for generating maximally-localized Wannier functions in a high-throughput framework.

Description of provider: A platform for Open Science built for seamless sharing of resources in computational materials science. Description of dataset: Database of experimentally reported Covalent-Organic Frameworks (COFs), provided with DFT-optimized geometry and DDEC partial charges for molecular simulations.

Description of provider: A platform for Open Science built for seamless sharing of resources in computational materials science. Description of dataset: Structural, dynamic and electronic characterisation of a novel mixed-metal chalcohalide revealing hidden symmetry breaking and spontaneous polarisation.