Key Takeaways & Executive Findings
- •• • The thermodynamic threshold for CLAS oxygen carriers is Eovf < 2.3 eV, ensuring feasible oxygen release at low temperatures (400–650 °C). • • The ALIGNN graph neural network predicts Eovf with a mean absolute error (MAE) of 0.26 eV, enabling reliable high-throughput screening. • • Screening of 3,649 SrxA1−xFeyB1−yO3 compositions identified that Co doping at the B-site and Ba/Ca doping at the A-site effectively lower Eovf, guiding rational material design. • • The model rediscovered known low-temperature CLAS oxygen carriers, validating the screening methodology and accelerating material discovery.
Abstract
Low-temperature chemical looping air separation (CLAS) is a promising technology for producing oxygen-enriched gas streams, utilizing the redox properties of solid oxygen carriers to selectively capture and release oxygen from air. Oxygen vacancy formation energy (Eovf) is a key descriptor for evaluating the ease of oxygen release. In this study, the applicable range of Eovf for CLAS oxygen carriers was determined to be <2.3 eV via thermodynamic calculations. A graph neural network (GNN) model, specifically the ALIGNN architecture, was trained to predict Eovf with a mean absolute error (MAE) of 0.26 eV on the test set. Using this model, a high-throughput screening of 3,649 compositions of SrxA1−xFeyB1−yO3 perovskites was conducted to identify promising CLAS oxygen carriers. The predictions revealed that doping with Ba and Ca at the A-site and Co at the B-site effectively reduces Eovf. The screening criterion of Eovf < 2.3 eV successfully rediscovered several previously reported low-temperature CLAS oxygen carriers, validating the approach. This work demonstrates that GNN-based Eovf prediction can significantly accelerate the discovery of CLAS materials, with broader implications for other chemical looping applications such as full oxidation and syngas production.
1. Introduction
Chemical looping air separation (CLAS) offers a low-energy route to oxygen production, but its commercial deployment hinges on oxygen carriers that can operate efficiently at low temperatures (400–650 °C). Conventional perovskite oxides like SrFeO3−δ show promise, yet their performance is limited by high oxygen vacancy formation energies (Eovf), which dictate the ease of oxygen release. Traditional trial-and-error doping strategies are time-consuming and costly, impeding the discovery of optimal compositions.
This study addresses this bottleneck by employing a graph neural network (GNN) to predict Eovf across a vast compositional space of SrxA1−xFeyB1−yO3 perovskites. By establishing a thermodynamic threshold of Eovf < 2.3 eV and achieving a prediction MAE of 0.26 eV, the authors enable rapid screening of 3,649 candidates. This computational approach not only identifies promising dopants (Ba, Ca at A-site; Co at B-site) but also redisovers known materials, validating its efficacy. The methodology accelerates the discovery of low-temperature CLAS oxygen carriers, potentially reducing development time and cost for industrial oxygen production systems.
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ZHAO Jie, DONG Changqing, XUE Junjie, HU Xiaoying, ZHANG Junjiao (2026). High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60680-9
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Frequently Asked Questions
What is the physical significance of the Eovf threshold of 2.3 eV for CLAS oxygen carriers?
The threshold is derived from thermodynamic calculations: for oxygen release to occur at low temperatures (400–650 °C) under typical oxygen partial pressures, the oxygen vacancy formation energy must be below 2.3 eV. Materials with higher Eovf would require impractically high temperatures or low pressures to release oxygen, making them unsuitable for low-temperature CLAS.
How does the ALIGNN model's MAE of 0.26 eV impact the reliability of screening predictions?
An MAE of 0.26 eV is relatively small compared to the 2.3 eV threshold, meaning the model can distinguish materials near the boundary with reasonable confidence. However, for compositions with predicted Eovf close to 2.3 eV, experimental validation is necessary to avoid false positives/negatives. The error is acceptable for initial screening but not for final material selection.
What are the practical implications of Co doping at the B-site and Ba/Ca doping at the A-site for material synthesis?
Co doping at the B-site is known to enhance oxygen mobility and reduce Eovf, but it may also introduce cost and stability issues. Ba and Ca doping at the A-site can alter lattice strain and oxygen vacancy ordering. These dopants must be carefully balanced to maintain perovskite phase stability and avoid segregation during cyclic operation. The study provides a compositional map to guide synthesis efforts.
How does the screening methodology account for real-world operating conditions such as cyclic stability and sulfur poisoning?
The current screening is based solely on Eovf, which is a thermodynamic descriptor. It does not consider kinetic factors, long-term cyclic stability, or resistance to contaminants like sulfur. Therefore, promising candidates from this screen must undergo experimental testing under realistic CLAS conditions to evaluate their durability and performance.
What is the scalability of the GNN-based screening approach for other chemical looping applications?
The methodology is generalizable: as long as a reliable descriptor (like Eovf) can be defined and a training dataset is available, the same GNN framework can be applied to screen materials for chemical looping full oxidation, syngas production, or other redox applications. This accelerates discovery across multiple fields.
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