Machine Learning Application In Thermoelectric Material

Thermoelectric material is a functional material that can directly convert thermal energy and electrical energy,and has been used in atomic energy battery in aerospace and refrigeration in electronic communications,and also shows a promising in waste heat harvesting and self-powered energy supply in IoT?Internet of Things?.The continuously discovering of new materials has made a significant contribution to the advance in thermoelectric field.The discovering of new thermoelectric materials can be traced back to Seebeck's exploratory research through trial-and-error efforts.In the 1950s,classical thermoelectric materials such as Bi₂Te₃ and PbTe were found due to the progress in condensed matter physics.Recent years,the development of first principle calculation has greatly promoted the discovery of new thermoelectric materials such as Mg₃Sb₂.However,compared with known-structure materials,the number of unknown-structure materials is even bigger.Here,we proposed a machine learning assistant discovering of the new thermoelectric materials.According to the classic Bi₂Te₃ material,we constructed a M₂X₃-type thermoelectric material library with 720 compounds using equivalent valence electron substitution in which 80 compounds were found to have crystalline structures in the ICSD database.The physical properties of constituent elements?such as atomic size,electronegativity,density,etc.?were used to define the feature of the compounds with a general formula M¹M²X¹X²X³?M¹+M²:X¹+X²+X³=2:3?.A random forest plus bayesian optimization of hyperpatameters was used as the machine leaning method,and the 80 compounds with known structures were used as the foundation database to learn the structure-classification rules and predict new materials.The first objective is to find the rule that could identify the compounds with the same rhombohedral structure of Bi₂Te₃.The first try found that the number of learning samples used to generate the rules was too small,and the accuracy of multi-task prediction for the complete prediction of the seven major crystal systems was rather limited.Therefore,we modified the strategy to only identify the compounds with the same rhombohedral structure of Bi₂Te₃.The cross validation of the machine learning process showed a high accuracy of 0.94 for the prediction of rhombohedral compounds.The prediction of 10 M₂X₃-type compounds that were found in the recent references,showed an accuracy of 0.9.We finally used the code to predict new compounds with similar rhombohedral structure and obtained 70+new compounds that could have similar structure with Bi₂Te₃.Furthermore,it was found the important features affecting the structure of M₂X₃–type thermoelectric materials through feature selection and exploratory data analysis.So we proposed structure rules based on elemental features such as electronegativity,ionic radius,and melting point.This rule could achieve the same accuracy as the machine learning model.However,only the similar structure can not guarantee the candidate must be a good thermoelectric material.The band gap is an important indicator to judge whether a compound is a potential thermoelectric material.Furthermore,based on the Slack's early work on the relationship between the band gap and average electronegativity difference between the cations?M?and anions?X?,we proposed a criteria of dual-similarity,i.e.structure similarity and electronegativity similarity,and some potential new compounds were given out,such as Sb₂S₂Te,SbFeTe₃,Sb₂TeSeS.