journal article·2024-03-06

Searching for low thermal conductivity materials for thermal barrier coatings: A theoretical approach

Majid Zeraati, Artem R. Oganov, Tao Fan, Sergey F. Solodovnikov

Physical Review Materials· Volume 8· Issue 3
Publisher page

Abstract

Low thermal conductivity is important for thermal barrier coatings, thermoelectrics, and other applications in industry and materials science. Accurate calculation of their thermal conductivity $\ensuremath{\kappa}$ at high temperatures remains challenging: methods such as the Boltzmann transport equation (BTE) usually underestimate the actual value. Here we used the effective harmonic method and homogeneous nonequilibrium molecular dynamics simulations with machine-learning potentials to calculate the thermal conductivity of candidate materials at temperatures up to 1500 K. The results obtained for ${\mathrm{La}}2{\mathrm{Zr}}2{\mathrm{O}}7, {\mathrm{ZrSiO}}4$, and ${\mathrm{BaZrO}}3$ are in perfect agreement with the experiment at all temperatures. We used renormalized second- and third-order interatomic force constants and phonons at high temperatures to calculate the thermal conductivity using the BTE and confirmed these results with molecular dynamics simulations. Investigating the relationship of thermal conductivity with the elastic properties, Debye temperature, and the speed of sound, we proposed threshold values for future high-throughput screening for low-$\ensuremath{\kappa}$ materials. Using the molecular dynamics method at high temperatures, we calculated the volumetric thermal expansion coefficient and selected ten candidate materials for thermal barrier coatings at high temperatures. Besides thermal barrier coating materials, this approach can be applied to multiple classes of materials where thermal conductivity is important.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Thermal properties of materials · High-Temperature Coating Behaviors · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Bld. 1, 121205 Moscow, Russia

Nikolaev Institute of Inorganic Chemistry, Siberian Branch, Russian Academy of Sciences, Akademika Lavrentyeva Ave. 3, 630090 Novosibirsk, Russia

Funding

Russian Science Foundation · Center for High Performance Computing, Shanghai Jiao Tong University

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