Big Data of Materials Science: Critical Role of the Descriptor
Nov 26, 20145 pages
Published in:
- Phys.Rev.Lett. 114 (2015) 10, 105503
- Published: Mar 10, 2015
e-Print:
- 1411.7437 [physics.data-an]
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Abstract: (APS)
Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.- 61.50.-f
- 02.60.Ed
- 71.15.Mb
- 89.20.Ff
References(19)
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