Exabyte-io/api-examples

Example usage of Exabyte.io platform through its RESTful API: programmatically create materials and modeling workflows, execute simulations on the cloud, analyze data and build machine learning models

40
/ 100
Emerging

This helps computational materials scientists or researchers automate their materials simulations and data analysis. You can input material structures (e.g., from POSCAR files) and define simulation workflows (like DFT calculations), then the system executes these on the cloud and provides simulation outputs and derived material properties. This is for professionals who regularly run complex materials simulations and want to integrate them into automated workflows or generate large datasets for machine learning.

Use this if you need to programmatically manage and execute materials science simulations, analyze results, and build machine learning models based on material properties.

Not ideal if you prefer purely manual interaction with simulation software or only need to run a few isolated calculations without automation.

materials-science computational-chemistry quantum-espresso materials-informatics density-functional-theory
No License No Package No Dependents
Maintenance 13 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 15 / 25

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Jupyter Notebook

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Last pushed

Mar 14, 2026

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