Combining Machine Learning Models with First-Principles High-Throughput Calculation to Accelerate the Search of Promising Thermoelectric Materials

TL;DR

This paper combines first-principles high-throughput calculations with machine learning models to efficiently identify promising thermoelectric materials, significantly accelerating discovery compared to traditional methods.

Contribution

It introduces a novel integrated approach using high-throughput calculations and multiple machine learning models for thermoelectric material discovery.

Findings

Built a thermoelectric materials database with 796 compounds.

Achieved over 85% classification accuracy in predicting promising materials.

M3GNet model for n-type data achieved over 90% accuracy, precision, and recall.

Abstract

Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments and first-principles calculations. However, these methods are usually expensive and time-consuming. Recently, the prediction of properties via machine learning has emerged as a popular method in materials science. Herein, we firstly did first-principles high-throughput calculations for a large number of chalcogenides and built a thermoelectric database containing 796 compounds. Many novel and promising thermoelectric materials were discovered. Then, we trained four ensemble learning models and two deep learning models to distinguish the promising thermoelectric materials from the others for n type and p type doping, respectively. All the presented models achieve classification accuracy higher than 85% and area under the curve (AUC) higher than 0.9. Especially, the M3GNet model for n type data achieve accuracy, precision and recall all higher than 90%. Our works demonstrate a very efficient way of combining machine learning prediction and first-principles high-throughput calculations together to accelerate the discovery of advanced thermoelectric materials.