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The wavelengths of visible light ultimately limit the maximum theoretical resolution of images created by light microscopes. Most light microscopes can only magnify 1000X, and a few can magnify up to 1500X. Electrons, like electromagnetic radiation, can behave like waves, but with wavelengths of 0.005 nm, they produce significantly greater resolution up to 0.05 nm as compared to 500 nm for visible light. An electron microscope (EM) can create a sharp image that is magnified up to 2,000,000X.
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Materials Cloud, a platform for open computational science.

Leopold Talirz1,2,3, Snehal Kumbhar4,5, Elsa Passaro4,5,6

  • 1National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland. leopold.talirz@gmail.com.

Scientific Data
|September 9, 2020
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Summary
This summary is machine-generated.

Materials Cloud offers open resource sharing for computational science, featuring citable data and reproducible simulation pipelines. This platform enhances collaboration and accelerates research by providing access to data, models, and tools.

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Area of Science:

  • Computational Materials Science
  • Data Science
  • Scientific Computing

Background:

  • Computational science research generates vast amounts of data and complex simulation workflows.
  • Sharing these resources effectively is crucial for reproducibility and collaboration.
  • Existing platforms often lack comprehensive support for data provenance and simulation pipeline management.

Purpose of the Study:

  • To introduce Materials Cloud, a platform for open and seamless resource sharing in computational science.
  • To enable persistent archiving and dissemination of data with provenance.
  • To support the entire simulation pipeline, from data generation to analysis and education.

Main Methods:

  • Development of a platform integrating data archival, modeling services, and analytical tools.
  • Implementation of a provenance graph to track and reproduce simulation results.
  • Support for the AiiDA (Advanced Information and Intelligence for All) database for simulation management.

Main Results:

  • Materials Cloud provides persistent, citable data with a detailed provenance graph.
  • Users can browse, download, and utilize shared simulation data and workflows.
  • The platform facilitates retracing and reproducing computational results, fostering trust and collaboration.

Conclusions:

  • Materials Cloud enables open and seamless sharing of computational science resources.
  • The platform enhances research reproducibility and collaboration through comprehensive simulation pipeline management.
  • Its agnostic infrastructure supports diverse applications beyond materials modeling.