Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning

Collins, Christopher M ORCID: 0000-0002-0101-4426, Daniels, Luke M ORCID: 0000-0002-7077-6125, Gibson, Quinn, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Moran, Michael, Feetham, Richard, Pitcher, Michael J, Dyer, Matthew S ORCID: 0000-0002-4923-3003, Delacotte, Charlene, Zanella, Marco ORCID: 0000-0002-6164-6169
et al (show 9 more authors) (2021) Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 60 (30). pp. 16457-16465.

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We report the aperiodic titanate Ba<sub>10</sub> Y<sub>6</sub> Ti<sub>4</sub> O<sub>27</sub> with a room-temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low-thermal-conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine-learning models.

Item Type: Article
Uncontrolled Keywords: aperiodic structure, machine learning, metastable compounds, thermal conductivity, titanium oxides
Divisions: Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 11 Jun 2021 07:49
Last Modified: 16 Feb 2023 14:48
DOI: 10.1002/anie.202102073
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