Advanced data science toolkit for non-data scientists – A user guide

Peng, Jian; Lee, Sangkeun (Matt); Williams, Andrew; Haynes, James A.; Shin, Dongwon

doi:10.1016/j.calphad.2019.101733

Advanced data science toolkit for non-data scientists – A user guide

Journal Article · Sat Feb 01 04:00:00 EST 2020 · Calphad

DOI:https://doi.org/10.1016/j.calphad.2019.101733· OSTI ID:1649534

Peng, Jian ^[1]; ^[1]; ^[2]; ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Cornell Univ., Ithaca, NY (United States)

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1649534

Alternate ID(s):: OSTI ID: 1693490

Journal Information:: Calphad, Journal Name: Calphad Vol. 68; ISSN 0364-5916

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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