Materials Selection: Selecting Appropriate Clustering Methods for Materials Science Applications of Machine Learning (Adv. Theory Simul. 12/2019). Issue 12 (4th December 2019)

• Record Type: Journal Article
• Authors: Parker, Amanda J.; Barnard, Amanda S.
• Is Part Of: Advanced theory and simulations. Volume 2:Issue 12(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-04
• Subjects: Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201970040
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract : Clustering is an important method to determine the classes of materials and nanoparticles. Knowing the number and density of clusters in advance can accelerate method selection and evaluation. In article number 1900145, Amanda J. Parker and Amanda S. Barnard show how iterative label spreading (ILS) can guide the use of clustering algorithms and simplifies the identification of classes of materials using machine learning.