Litcius/Paper detail

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

Christopher M. Collins, Luke M. Daniels, Quinn Gibson, Michael W. Gaultois, Michael D. Moran, Richard Feetham, Michael J. Pitcher, Matthew S. Dyer, C. Delacotte, Marco Zanella, Claire A. Murray, Györgyi Glodán, Olivier Pérez, D. Pelloquin, Troy D. Manning, Jonathan Alaria, George R. Darling, John B. Claridge, Matthew J. Rosseinsky

2021Angewandte Chemie International Edition30 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Thermal conductivityArtificial intelligenceOxideMaterials scienceMachine learningComputer scienceComposite materialMetallurgyMachine Learning in Materials ScienceThermal properties of materialsnanoparticles nucleation surface interactions