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

Christopher Collins (1) , Luke Daniels (1) , Quinn Gibson (1) , Michael Gaultois (1) , Michael Moran (1) , Richard Feetham (1) , Michael Pitcher (1) , Matthew Dyer (1) , Charlene Delacotte (1) , Marco Zanella (1) , Claire Murray (2) , Gyorgyi Glodan (3) , Olivier Pérez (4) , D. Pelloquin (4) , Troy Manning (1) , Jonathan Alaria (1) , George Darling (1) , John Claridge (1) , Matthew Rosseinsky (1)

1 University of Liverpool
2 DIAMOND Light source
3 University of Manchester [Manchester]
4 CRISMAT - Laboratoire de cristallographie et sciences des matériaux

We report the aperiodic titanate Ba10Y6Ti4O27 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.

Mots clés: Aperiodic structure; Machine learning; Metastable compounds; Thermal conductivity; Titanium; Oxides

Domaines: Chimie; Chimie inorganique; Cristallographie

Christopher Collins, Luke Daniels, Quinn Gibson, Michael Gaultois, Michael Moran, et al.. Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning. Angewandte Chemie International Edition, 2021, 60 (30), pp.16457-16465. ⟨10.1002/anie.202102073⟩. ⟨hal-03447129⟩