MRS Computational Materials Science Tutorial

By Panayotis Thalis Manganaris1; Saaketh Desai2; Arun Kumar Mannodi Kanakkithodi1

1. Purdue University 2. Sandia National Laboratory

Hands-on guide to the development of statistical models useful for materials design using python, sklearn, tensorflow, and intel extensions.

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Version 1.0 - published on 06 May 2022

doi:10.21981/D1J2-AR65 cite this

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Abstract

These notebooks are the first in a series of tutorials planned for recurring workshops hosted at the MRS spring and fall meetings. It aims to introduces newcomers to an example of rigorous model engineering. This is done by interactively guiding users through the task of creating models of semiconductor band gaps using a subset of the Mannodi Research Group's computational cubic Perovskites dataset.

References

Mannodi-Kanakkithodi, A., & Chan, M. K. Y. (2021). Data-driven design
of novel halide perovskite alloys. Energy and Environmental Science,
(), . http://dx.doi.org/10.1039/D1EE02971A

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  • Panayotis Thalis Manganaris, Saaketh Desai, Arun Kumar Mannodi Kanakkithodi (2022), "MRS Computational Materials Science Tutorial," https://nanohub.org/resources/mrsicmsnotes. (DOI: 10.21981/D1J2-AR65).

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Tags

  1. data analysis
  2. halides
  3. machine learning
  4. materials informatics
  5. perovskites
  6. python
  7. tensorflow