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Projects: pypi: matminer

https://packages.ecosyste.ms/registries/pypi.org/packages/matminer

matminer is a library that contains tools for data mining in Materials Science
68 versions
Latest release: about 3 years ago
14 dependent packages
17,098 downloads last month

Enhanced Analysis
Educational Contributors: alireza@wustl.edu scherfaoui@berkeley.edu jc75@rice.edu kylebystrom@berkeley.edu maxwelldylla2020@u.northwestern.edu ongsp@eng.ucsd.edu danieldopp@uky.edu ongsp@ucsd.edu km468@cornell.edu dwinston@alum.mit.edu ldwillia@umich.edu
Repository Activity:
Repository Owner: Hacking Materials Research Group (organization) Academic
README Analysis:
Science Score: 100/100
Starting Score: 100 points
Bonuses:
  • +220 Educational commit emails
    11 contributors with educational email addresses
  • +20 Academic repository owner
    Repository owned by academic institution
  • +15 Institutional repository owner
    Repository owned by research institution
  • +4 Science terms in README
    2 scientific terms found in README
Penalties:
  • -10 PyPI ecosystem
    General-purpose ecosystem
Very Likely Science (100)

Papers Mentioning matminer 4

10.1039/d0sc01101k
Autonomous intelligent agents for accelerated materials discovery
Cited by: 46
Author(s): Joseph H. Montoya, Kirsten T. Winther, Raul A. Flores, Thomas Bligaard, Jens Strabo Hummelshøj, Muratahan Aykol
Software Mentions: 8
Published: over 6 years ago
10.1016/j.patter.2020.100013
Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure
Cited by: 40
Author(s): Zheng Chen, Chi Chen, Yiming Chen, Shyue Ping Ong
Software Mentions: 3
Published: about 6 years ago
10.34133/2020/6375171
Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials
Cited by: 29
Author(s): Maxwell Dylla, Alexander Dunn, Shashwat Anand, Anubhav Jain, G. Jeffrey Snyder
Software Mentions: 2
Published: over 6 years ago
10.1038/s41598-021-88027-8
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Cited by: 24
Author(s): Aditi S. Krishnapriyan, Joseph Montoya, Maciej Harańczyk, Jens Strabo Hummelshøj, Dmitriy Morozov
Software Mentions: 2
Published: about 5 years ago