机器学习辅助3D打印室温超高性能热电材料
Machine Learning-Assisted 3D Printing of Thermoelectric Materials of Ultrahigh Performances at Room Temperature
AI总结：
本文提出机器学习辅助的挤出打印方法，通过高通量实验与贝叶斯优化协同搜索墨水配方和打印参数，打印出室温zT达1.3的BiSbTe基热电材料，为复杂三维热电材料与器件制造提供了通用策略。
摘要：
热电能量转换是一种具有吸引力的技术，既可以利用废热发电，也可以利用电力实现固态制冷。然而，热电设备的传统制造工艺成本高昂，且仅限于简单的器件几何形状。本工作报道了一种挤出打印方法，用于制造具有复杂三维结构的高性能热电材料。通过集成高通量实验和贝叶斯优化（BO），我们的方法显著加速了对最优墨水配方和打印参数的同时搜索，从而在保持所需形状保真度的同时实现高热电性能。采用基于高斯过程回归（GPR）的机器学习模型，快速预测热电功率因子随墨水配方和打印参数的变化。在优化条件下打印的碲化铋锑（BiSbTe）基热电材料展现出高达1.3的室温zT值，这是迄今为止打印热电材料中的最高值。这种机器学习引导的基于墨水的打印策略具有高度通用性，可广泛应用于各类功能材料和器件，具有广阔的技术应用前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
