This page includes links to online resources related to data in materials
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- OpenKIM —
The Open Knowledgebase of Interatomic Models (OpenKIM) project archives
interatomic potentials, develops API standards to make potentials portable between
simulation codes, and develops methods to assess the accuracy and transferability
- CAMS —
The cyberinfrastructure for Atomistic Materials Science (CAMS) serves
as a forum for disseminating new atomistic scale methodologies, and
educating non-experts and the next generation of computational materials
- Part 1 |
Two part blog post by Bryce Meredig (Citrine) on using open source tools and data to get started with machine learning for materials science.
- Data Science Venn Diagram —
A post by Drew Conway on the training needed by data scientists.
- Introduction to Data Science with R —
Course materials for "Introduction to Data Science with R", a video course by RStudio and O'Reilly Media.
- Citrine Newsletter —
Data-driven materials science & chemistry newsletter by Citrine Informatics. Providing a nucleation point and timely updates for the fast-growing materials and chemical data & informatics community.
Materials Data Repositories
- Materials Project —
The Materials Project provides open web-based access to information computed using electronic
structure methods on known and predicted materials as well as powerful analysis tools to inspire
and design novel materials.
- AFLOW —
The Automatic-flow (AFLOW) project is a searchable repository of material properties obtained
using high-throughput ab initio electronic structure computations.
- NoMaD —
The NoMaD (Novel Materials Discovery) Repository hosts, organizes and shares
material data calculated using ab initio electronic-structure methods.
- NanoMine —
NanoMine is an online modular platform of material informatics and simulation tools for predicting
polymer nanocomposite properties.