High-Throughput Prediction of the Thermal and Electronic Transport Properties of Large Physical and Chemical Spaces Accelerated by Machine Learning: Charting the ZT of Binary Skutterudites.

Thermal and electronic transport properties are the keys to many technological applications of materials. Thermoelectric, TE, materials can be considered a singular case in which not only one but three different transport properties are combined to describe their performance through their TE figure of merit, ZT. Despite the availability of high-throughput experimental techniques, synthesizing, characterizing, and measuring the properties of samples with numerous variables affecting ZT are not a cost- or time-efficient approach to lead this strategy. The significance of computational materials science in discovering new TE materials has been running in parallel to the development of new frameworks and methodologies to compute the electron and thermal transport properties linked to ZT. Nevertheless, the trade-off between computational cost and accuracy has hindered the reliable prediction of TE performance for large chemical spaces. In this work, we present for the first time the combination of new ab initio methodologies to predict transport properties with machine learning and a high-throughput framework to establish a solid foundation for the accurate prediction of thermal and electron transport properties. This strategy is applied to a whole family of materials, binary skutterudites, which are well-known as good TE candidates. Following this methodology, it is possible not only to connect ZT with the experimental synthetic (carrier concentration and grain size) and operando (temperature) variables but also to understand the physical and chemical phenomena that act as driving forces in the maximization of ZT for p-type and n-type binary skutterudites.

High-Throughput Screening of the Thermoelastic Properties of Ultrahigh-Temperature Ceramics.

Ultrahigh-temperature ceramics (UHTCs) are a group of materials with high technological interest because of their applications in extreme environments. However, their characterization at high temperatures represents the main obstacle for their fast development. Obstacles are found from an experimental point of view, where only few laboratories around the world have the resources to test these materials under extreme conditions, and also from a theoretical point of view, where actual methods are expensive and difficult to apply to large sets of materials. Here, a new theoretical high-throughput framework for the prediction of the thermoelastic properties of materials is introduced. This approach can be systematically applied to any kind of crystalline material, drastically reducing the computational cost of previous methodologies up to 80% approximately. This new approach combines Taylor expansion and density functional theory calculations to predict the vibrational free energy of any arbitrary strained configuration, which represents the bottleneck in other methods. Using this framework, elastic constants for UHTCs have been calculated in a wide range of temperatures with excellent agreement with experimental values, when available. Using the elastic constants as the starting point, other mechanical properties such a bulk modulus, shear modulus, or Poisson ratio have been also explored, including upper and lower limits for polycrystalline materials. Finally, this work goes beyond the isotropic mechanical properties and represents one of the most comprehensive and exhaustive studies of some of the most important UHTCs, charting their anisotropy and thermal and thermodynamical properties.