The data contained in these databases are a variety of material properties, obtained in-house or from other external databases, that are either calculated, measured from experiments, or learned from trained algorithms. The MPDataRetrieval and CitrineDataRetrieval classes can be used to retrieve data from the biggest open-source materials database collections of the Materials Project and Citrine Informatics, respectively, in a Pandas dataframe format. Retrieve data from the biggest materials databases, such as the Materials Project and Citrine’s databases, in a Pandas dataframe format Matminer is open source via a BSD-style license.Ī general workflow and overview of matminer’s capabilities is presented below:ĭata retrieval easily puts complex online data into dataframes ¶ Matminer does not contain machine learning routines itself, but works with the pandas data format in order to make various downstream machine learning libraries and tools available to materials science applications. One-line access to pre-trained deep learning models for inference ( matminer.models - Coming soon!) Tools and utilities for practically handling materials data, in dataframe format ( )įull table of featurizers here: Table of Featurizers. transforming and featurizing complex materials attributes into numerical descriptors ( matminer.featurizers)ħ0+ featurizers adapted from scientific publications.Such as The Materials Project and Citrination, among others easily creating your own datasets from online repositories ( matminer.data_retrieval).one-line access to 40+ ready-made datasets ( matminer.datasets)įull list of datasets here: Table of Datasets.Matminer is a Python library for data mining the properties of materials.
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