High-throughput screening of high-activity oxygen carriers for chemical looping argon purification via a machine learning – density functional theory method

Abstract

Argon, a protective gas, is susceptible to contamination by impurity gases in the production of monocrystalline silicon for solar cells. Chemical looping combustion (CLC) technology offers a solution for argon recycling by leveraging the cyclic conversion of oxygen carriers. However, the desorption of low-concentration impurity gases requires high-activity oxygen carriers, and current screening methods primarily rely on experimental trial and error, which is time-consuming and labor-intensive. Herein, we propose machine learning-assisted Density Functional Theory (DFT) for high-throughput screening of oxygen carriers. Quaternary iron-based spinel oxygen carriers A1_xA2_1−xB_yFe_2−y were used as the object of study. DFT calculations were conducted on 756 oxygen carriers, while the remaining 3619 were predicted through machine learning, achieving a prediction accuracy R² of 0.87. Based on these predictions and a three-step screening criterion of synthesizability, thermodynamic stability, and reactivity, Cu_0.875Ni_0.125Al_0.5Fe_1.5O₄ exhibited the highest reactivity and its desorption of impurity gases is 6 times higher than that of fresh Fe₂O₃. In the stability test, Cu_0.875Ni_0.125Al_0.5Fe_1.5O₄ maintained 96% CO removal efficiency after 10 cycles, facilitating the cyclic purification of crude argon. This study provides new guidance for the design and discovery of high-activity materials through high-throughput screening.