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- 2016 AIChE Annual Meeting
- Computational Molecular Science and Engineering Forum
- Data Mining and Machine Learning in Molecular Sciences I
- (142f) Design of Ternary Transparent Conducting Oxides
The focus of this work is an examination of the phase diagram for ternary (AlxGayIn1-x-y)2O3 materials using DFT-based cluster expansion models combined with fast stochastic optimization techniques (e.g., nested sampling). This combined computational approach allows for both an efficient search of the stable and metastable configurations for (AlxGayIn1-x-y)2O3 at various lattice types and the consideration of entropy on the relative stability of ternary TCOs. Statistical learning, in particular compressed sensing, is used to efficiently identify a structure-property relationship between the targeted properties (e.g., mobilities and optical transparency) and the fundamental chemical and physical parameters that control these properties.
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[2] Ito et al., Jpn. J. Appl. Phys., 51, 100207 (2012); Zhang et al., Appl. Phys. Lett., 105, 162107 (2014).