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March 3, 2026Digital Discovery4 citationsOpen Access

MC3D: the materials cloud computational database of experimentally known stoichiometric inorganics

SHSebastiaan P. HuberMMMichail MinotakisMBMarnik Bercx

Key Points

  • The MC3D database provides access to 72,589 unique inorganic crystal structures optimized using density-functional theory.
  • Featuring nearly a million structures imported from various databases, 95% are classified as experimentally known.
  • Analysis involved automated workflows for optimizing geometries and filtering structures based on stoichiometry.
  • Highlights the significance of reproducibility and structured data access through the Materials Cloud portal.

Abstract

Density-functional theory (DFT) is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure Database (MC3D), an online database of computed three-dimensional (3D) inorganic crystal structures. Close to a million experimentally reported structures were imported from the COD, ICSD and MPDS databases; these were parsed and filtered to yield a collection of 72 589 unique and stoichiometric structures, of which 95% are, to date, classified as experimentally known. The geometries of structures with up to 64 atoms were then optimized using DFT with automated workflows and curated input protocols. The procedure was repeated for different functionals and computational protocols, generating three methodology-based MC3D subdatabases: PBE-v1, PBEsol-v1, and PBEsol-v2, with the latest containing 32 013 unique structures. All subdatabases of the MC3D are made available on the Materials Cloud portal, which provides a graphical interface to explore and download the data. The database includes the full provenance graph of all the calculations driven by the automated workflows, thus establishing full reproducibility of the results and more-than-FAIR procedures.

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Cite This Study

Huber et al. (2026) studied this question.

synapsesocial.com/papers/69a768babadf0bb9e87e5bdehttps://doi.org/10.1039/d5dd00415b
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