OCPMDM: Online computation platform for materials data mining

Abstract With the rapid development of the Materials Genome Initiative (MGI), scientists and engineers are confronted with the need to conduct sophisticated data analytics in modeling the behaviors of materials. Nowadays, it is inconvenient for material researchers to carry out materials data mining work without an efficient platform for materials machine learning. So, it is meaningful to develop an online platform for material researchers in urgent need of using machine learning techniques by themselves. The typical case study is given to demonstrate the applications of the online computation platform for material data mining (OCPMDM) in our lab: The quantitative structure property relationship (QSPR) model for rapid prediction of Curie temperature of perovskite material can be applied to screen out perovskite candidates with higher Curie temperature than those of training dataset collected from references, efficiently. Material data mining tasks can be implemented via the OCPMDM, which provides powerful tools for material researchers in machine learning-assisted materials design and optimization. The URL of OCPMDM is http://materialdata.shu.edu.cn .

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