Machine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature

TL;DR

This paper presents a machine learning-guided extrusion printing method for fabricating complex 3D thermoelectric materials with ultrahigh performance at room temperature, significantly advancing manufacturing capabilities.

Contribution

It introduces a novel integration of high-throughput experimentation, Bayesian optimization, and Gaussian process regression to rapidly optimize ink formulation and printing parameters for high-performance thermoelectric devices.

Findings

Achieved an ultrahigh room temperature zT of 1.3 in printed thermoelectric materials.

Demonstrated the effectiveness of machine learning in optimizing complex fabrication processes.

Showed the method's potential for broad application to various functional materials.

Abstract

Thermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and limited to simple device geometries. This work reports an extrusion printing method to fabricate high-performance thermoelectric materials with complex 3D architectures. By integrating high-throughput experimentation and Bayesian optimization (BO), our approach significantly accelerates the simultaneous search for the optimal ink formulation and printing parameters that deliver high thermoelectric performances while maintaining desired shape fidelity. A Gaussian process regression (GPR)-based machine learning model is employed to expeditiously predict thermoelectric power factor as a function of ink formulation and printing parameters. The printed bismuth antimony telluride (BiSbTe)-based thermoelectric materials under the optimized conditions exhibit an ultrahigh room temperature zT of 1.3, which is by far the highest in the printed thermoelectric materials. The machine learning-guided ink-based printing strategy can be highly generalizable to a wide range of functional materials and devices for broad technological applications.