Advanced data science toolkit for non-data scientists – A user guide. (March 2020)

• Record Type: Journal Article
• Authors: Peng, Jian; Lee, Sangkeun; Williams, Andrew; Haynes, J. Allen; Shin, Dongwon
• Is Part Of: Calphad. Volume 68(2020)
• Publication Date: 2020-03
• Subjects: Neural network -- Modern data analytics -- Correlation analysis -- Machine learning -- Materials research; Phase diagrams -- Data processing -- Periodicals; Thermochemistry -- Data processing -- Periodicals; Diagrammes de phases -- Informatique -- Périodiques; Thermochimie -- Informatique -- Périodiques; Thermodynamica; Electronic journals; 541.363
• DOI: 10.1016/j.calphad.2019.101733
• Languages: English
• ISSNs: 0364-5916

Abstract

Abstract: 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. Herein, 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.