Materials prediction via classification learning

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.; Lookman, Turab

doi:10.1038/srep13285

Title: Materials prediction via classification learning

Journal Article · Tue Aug 25 00:00:00 EDT 2015 · Scientific Reports

DOI:https://doi.org/10.1038/srep13285· OSTI ID:1259293

Balachandran, Prasanna V. ^[1]; Theiler, James ^[2]; Rondinelli, James M. ^[3]; Lookman, Turab ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Northwestern Univ., Evanston, IL (United States)

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. In conclusion, our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF)

Grant/Contract Number:: DMR 1454688; 20140013DR

OSTI ID:: 1259293

Journal Information:: Scientific Reports, Vol. 5; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 55 works

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