Matminer: An open source toolkit for materials data mining

By Logan T. Ward, Alex Dunn, Alireza Faghaninia, Nils E. R. Zimmermann, Saurabh Bajaj, Qi Wang, Joseph H. Montoya, Jiming Chen, Kyle Bystrom, M. Dylla and 6 more. Computational materials science, 2018

925 citations recorded by the source corpus.

Abstract As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.

Subfields: Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/1e8b16cf1ade8e5a08eb517fee667a019f1dbb77

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Shyue Ping Ong
2. Chi Chen
3. Alok N. Choudhary
4. Chris Wolverton

In Materials Chemistry

1. Shyue Ping Ong
2. Chi Chen
3. Anubhav Jain
4. Ming Hu
5. Yong Zhao
6. Zhonghao Liu
7. Zheng Xiong
8. Jianjun Hu

Citations in the corpus

• Recent advances and applications of deep learning methods in materials science (2021, methodology): “There have been libraries of such descriptors developed such as MatMiner [161] and DScribe [168].”
• Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation (2020, methodology): “to convert compound composition into meaningful features [10], which can be conveniently calculated using matminer [33] and Pymatgen [34].”