MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics

Abstract

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 72589 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 density-functional theory (DFT) with automated workflows and curated input protocols. The procedure was repeated for different functionals (and computational protocols), with the latest version (MC3D PBEsol-v2) comprising 32013 unique structures. All versions 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.

18 pages, 10 figures

References in corpus (35)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table
• Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds
• Pseudopotentials for high-throughput DFT calculations
• AFLOW: An automatic framework for high-throughput materials discovery
• Optimization Algorithm for the Generation of ONCV Pseudopotentials
• Precision and efficiency in solid-state pseudopotential calculations
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• Machine learning modeling of superconducting critical temperature
• Materials Cloud, a platform for open computational science
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• A foundation model for atomistic materials chemistry
• Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations
• Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
• Simple Data and Workflow Management with the signac Framework
• Norm-conserving pseudopotentials with chemical accuracy compared to all-electron calculations
• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
• OPTIMADE, an API for exchanging materials data
• Crystal Diffusion Variational Autoencoder for Periodic Material Generation
• $\text{Spglib}$: a software library for crystal symmetry search
• Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
• How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• aflow++: a C++ framework for autonomous materials design
• Projector augmented-wave and all-electron calculations across the periodic table: a comparison of structural and energetic properties
• Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
• Orb-v3: atomistic simulation at scale
• Thermal Conductivity Predictions with Foundation Atomistic Models
• A Foundational Potential Energy Surface Dataset for Materials
• PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
• Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

Cited by in corpus (1)

• Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions