Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning. Issue 30 (17th June 2021)

• Record Type: Journal Article
• Authors: Collins, Christopher M.; Daniels, Luke M.; Gibson, Quinn; Gaultois, Michael W.; Moran, Michael; Feetham, Richard; Pitcher, Michael J.; Dyer, Matthew S.; Delacotte, Charlene; Zanella, Marco; Murray, Claire A.; Glodan, Gyorgyi; Pérez, Olivier; Pelloquin, Denis; Manning, Troy D.; Alaria, Jonathan; Darling, George R.; Claridge, John B.; Rosseinsky, Matthew J.
• Is Part Of: Angewandte Chemie international edition. Volume 60:Issue 30(2021)
• Page Start: 16457
• Page End: 16465
• Publication Date: 2021-06-17
• Subjects: aperiodic structure -- machine learning -- metastable compounds -- thermal conductivity -- titanium oxides; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202102073
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: We report the aperiodic titanate Ba10 Y6 Ti4 O27 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. Abstract : Ba10 Y6 Ti4 O27 has a complex aperiodic structure with the lowest thermal conductivity of any first‐transition‐series oxide. It is metastable, which makes it challenging to identify. Experimental exploration was focused sufficiently by structure prediction and property‐targeted machine learning to enable the isolation of this material (see picture), whose structural motifs provide new insight into the control of heat transport.