A Python library for data mining and analysis in materials science, providing tools for featurizing materials and accessing materials data.
Matminer
- Python
- AI and Machine Learning, Chemistry, Data Analytics, Materials, Nanotechnologies
- Composites, Computational Fluid Dynamics (CFD), Design for Additive Manufacturing (DfAM), Machine Learning, Materials, Product Development, Sustainability Metrics
Features:
- Access to materials databases (Materials Project, Citrine, AFLOW, etc.), featurization of materials (compositional, structural, band structure), data retrieval and processing pipelines, integration with machine learning libraries (scikit-learn, Keras), tools for plotting materials properties.
Pricing:
- Free
- Facilitates materials informatics research by providing easy access to data and featurization tools, integrates well with the Python materials science ecosystem (Pymatgen, ASE), promotes reproducible research.
- Relies on external databases which may have access limitations or require API keys, featurization can be computationally intensive, learning curve for understanding various featurizers.
Best for:
- Materials scientists and researchers using data-driven approaches and machine learning for materials discovery, design, and property prediction.