Application of Materials Genome Methods in Thermoelectrics

Abstract

Materials genome methods have played an essential role in accelerating the discovery of high-performance novel materials, and include high-throughput calculation, database construction, and machine learning. Over the past decades, these approaches have been increasingly used in lithium battery materials, solar cells, transparent conductors, and thermoelectrics. Thermoelectrics are functional materials that can directly convert electricity into heat and vice versa, offering new ideas for conventional power generation and refrigeration. The application of high-throughput methods can achieve more efficient screening of new thermoelectric materials and accelerate experimental development. This review summarizes the recent progress in the application of materials genome methods for different thermoelectric materials, such as half-Heuslers, diamond-like structures, oxides, and other materials. Finally, current advances in machine learning for thermoelectrics are discussed. The progress of the theoretical design of thermoelectrics has driven the development of high-performance thermoelectrics.

High‐Throughput Strategies in the Discovery of Thermoelectric Materials

“…mathub3d.net), can greatly speed up the discovery of new TE materials. [65,66] Based on the Materials Project database, Ricci et al have created the largest computational database including the electronic transport properties of 48 000 materials by using the interpolation approach developed in the BoltzTraP software under the constant relaxation time approximation. [67][68][69] These material databases not only provide the possibility to screen new TE materials by using the known performance descriptors, but also constitute a powerful basis for the development of new performance descriptors, HTP calculation methods, and machine learning (ML) methods to quickly predict the material's TE performance.…”

“…mathub3d.net), can greatly speed up the discovery of new TE materials. [ 65,66 ] Based on the Materials Project database, Ricci et al. have created the largest computational database including the electronic transport properties of 48 000 materials by using the interpolation approach developed in the BoltzTraP software under the constant relaxation time approximation.…”

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

“…Due to the complexity of the composition and ratio of TGAs, as well as the high cost of synthesis, trial and error methods for all possible components are obviously not feasible [8] . In order to accelerate material design, advanced computational simulation methods have emerged, [9–10] such as non‐equilibrium Green's function (NEGF) theory, [11] density functional theory (DFT), [12] molecular dynamics (MD), [13] etc.…”

“…Due to the complexity of the composition and ratio of TGAs, as well as the high cost of synthesis, trial and error methods for all possible components are obviously not feasible. [8] In order to accelerate material design, advanced computational simulation methods have emerged, [9][10] such as non-equilibrium Green's function (NEGF) theory, [11] density functional theory (DFT), [12] molecular dynamics (MD), [13] etc. However, most of the computational simulation are only for specific systems, and there exist an unbearable computational load for complex systems, especially for solid solutions, defects and multi-component compounds.…”

A Critical Review of Machine Learning Techniques on Thermoelectric Materials

“…Thermoelectric (TE) materials are green energy materials that can realize direct conversion of thermal energy to electric energy using the Seebeck effect and Peltier effect. − They have potential applications in solid-state power generation and refrigeration, including industrial waste heat recovery, solid-state refrigeration in electronic communications, and special deep-sea power supplies. , Devices fabricated using TE materials are noise-free and environmentally friendly and have drawn worldwide attention over the years owing to their simple structures and unique features. People usually use the dimensionless figure of merit ZT = S 2 σT /( κ L + κ e ) to quantify the properties of TE materials, where S is the Seebeck coefficient; T is the absolute temperature in Kelvin; σ is the electrical conductivity; and κ L and κ e represent the lattice thermal conductivity and electronic thermal conductivity, respectively. , It is well-known that TE materials with good performance are expected to have a higher power factor (PF) S 2 σ and lower thermal conductivity.…”