MatMiner is an open-source Python library for performing data mining and analysis in the field of Materials Science. It is meant to make accessible the application of state-of-the-art statistical and machine learning algorithms to materials science data with just a few lines of code. It is currently in development, however it is a working code.
We are currently in the process of revising the first draft of a paper on MatMiner, so be on the lookout for a paper with the following cite structure:
Bajaj, S.; Jain, A.; [MatMiner]
It includes tools and utilities that make it easier to,
- Retrieve data from the biggest materials databases, such as the Materials Project and Citrine's databases, in a Pandas dataframe format
- Decorate the dataframe with composition, structural, and/or band structure descriptors/features
- Solve for and add thermal and mechanical properties to the dataframe
To cite MatMiner, you can cite the following two papers:
(1) Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. A. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater., 2013, 1, 011002, doi:10.1063/1.4812323. (2) Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. a.; Ceder, G. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis, Comput. Mater. Sci., 2013, 68, 314–319, doi:10.1016/j.commatsci.2012.10.028.
Example notebooks
A few examples demonstrating the features of the code in this repository have been added in the form of ipython notebooks. You can also use the Binder service (in beta) to launch an interactive notebook upon a click. Click the button below to open the tree structure of this repository and navigate to matminer/data_retrieval/example_notebooks to use/edit the notebook right away!
