469029 Guiding Experiments Towards New Functional Materials with Informatics

Monday, November 14, 2016: 1:24 PM
Yosemite A (Hilton San Francisco Union Square)
Prasanna V. Balachandran1, Dezhen Xue2 and Turab Lookman1, (1)Los Alamos National Lab, Los Alamos, NM, (2)Xi'an Jiaotong University, Xi'an, China

In this talk, we will discuss our informatics approach that integrates machine learning, symmetry analysis and density functional theory (DFT) to guide experiments in search of novel BaTiO3-based ferroelectric materials with high Curie temperature (TC). BaTiO3-based materials have attracted significant attention as a potential alternative for Pb-free piezoelectrics. In the development of BaTiO3-based piezoelectrics, one of the common strategies involves mixing two end member compositions that have Cubic (Pm-3m) to Tetragonal (P4mm) and Cubic to Rhombohedral (R3m) transformations. Our design objective is to identify suitable chemical substitutions at the Ba- and Ti-sites such that the TC of the end members are high. We perform DFT calculations on undoped ATiO3 and BaBO3 perovskites in P4mm and R3m symmetries, respectively; A and B are divalent and tetravalent cations, respectively. From symmetry-mode analysis, we decompose the two ferroelectric phases (P4mm and R3m) in terms of irreducible representations (irreps). These irreps, along with the unit cell volume from DFT, serve as feature sets for understanding ATiO3 and BaBO3 crystal chemistries and their role in impacting the ferroelectric distortions. We then link these features with known experimental TC data using machine learning methods and predict a new CdTiO3-BaTiO3 solid solution for experimental synthesis. We performed experiments and found that our predictions do not agree with the measurements. The reason for the discrepancy is attributed to the anomalous behavior of the CdTiO3-BaTiO3 solid solution, the phase diagram of which is found to be different from any of the BaTiO3-based solid solutions studied so far.

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