Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies

Journal of Materials Research

Volumn 30, Issue 7, 2015, Pages 879-889

  Bunn, Jonathan Kenneth ^a,b   Han, Shizhong ^a   Zhang, Yan ^a   Tong, Yan ^a   Hu, Jianjun ^a   Hattrick Simpers, Jason R ^a,b  

^a The Department of Mechanical Engineering   (United States)

^b UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALUMINUM ALLOYS; ALUMINUM METALLOGRAPHY; BINARY ALLOYS; MACHINE LEARNING; NICKEL METALLOGRAPHY;

DIFFRACTION DATA; HIGH-THROUGHPUT DATA; MACHINE LEARNING TECHNIQUES; MULTIPLE MEASUREMENTS; OVERALL ACCURACIES; PHASE IDENTIFICATION; PROCESSING EXPERIMENT; SPECTROSCOPIC TECHNIQUE;

LEARNING ALGORITHMS;

EID: 84934969998     PISSN: 08842914     EISSN: 20445326     Source Type: Journal    
DOI: 10.1557/jmr.2015.80     Document Type: Article

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