paper.bib

@article{mpds,
    Abstractnote = {Online materials database (known as PAULING FILE project) with nearly 2 million entries},
    Author = {Evgeny Blokhin and Pierre Villars},
    Doi = {10.17616/R3RV1M},
    Publisher = {re3data.org},
    Title = {MPDS},
    Year = {2017},
    Url = {https://www.re3data.org/repository/r3d100012448}}

@article{labs,
    Abstractnote = {This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure},
    Author = {Evgeny Blokhin},
    Doi = {10.5281/zenodo.1316962},
    Month = {Jul},
    Publisher = {Zenodo},
    Title = {mpds-io/mpds-ml-labs: v0.0.2},
    Year = {2018},
    Url = {https://zenodo.org/record/1316962}}

@article{Ward:2018,
    Abstractnote = {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},
    Author = {Ward and Dunn and Faghaninia and Zimmermann and Bajaj and Wang and Montoya and Chen and Bystrom and Dylla and Chard and Asta and Persson and Snyder and Foster and Jain},
    Doi = {10.1016/j.commatsci.2018.05.018},
    Journal = {Comput. Mater. Sci.},
    Publisher = {Elsevier},
    Volume = {152},
    Pages = {60-69},
    Title = {Matminer: An open source toolkit for materials data mining},
    Year = {2018},
    Url = {https://www.sciencedirect.com/science/article/pii/S0927025618303252}}

@article{zenodo,
    Abstractnote = {MPDS Platform API Data Retrieval Client Helper Utilities in Python},
    Author = {Evgeny Blokhin and Martin Uhrin},
    Doi = {10.5281/zenodo.1316932},
    Month = {Jul},
    Publisher = {Zenodo},
    Title = {mpds-io/python-api-client: v0.0.17},
    Year = {2018},
    Url = {https://zenodo.org/record/1316932}}