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

Collapse authors list.
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) , Murray, Claire A, Glodan, Gyorgyi, Pérez, Olivier, Pelloquin, Denis, Manning, Troy D ORCID: 0000-0002-7624-4306, Alaria, Jonathan ORCID: 0000-0001-5868-0318, Darling, George R, Claridge, John B ORCID: 0000-0003-4849-6714 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2021) Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning Angewandte Chemie, 133 (30). pp. 16593-16601. ISSN 0044-8249, 1521-3757

Text ACIE_YBaTiO_final_manuscript.pdf - Author Accepted Manuscript Download (21MB) | Preview

Abstract

Abstract We report the aperiodic titanate Ba 10 Y 6 Ti 4 O 27 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:	3402 Inorganic Chemistry, 34 Chemical Sciences, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence
Divisions:	Faculty of Science & Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	08 Jul 2021 10:33
Last Modified:	23 May 2026 05:58
DOI:	10.1002/ange.202102073
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3129281
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.