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High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks
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Jie ZHAO1, Changqing DONG1, 2, *, Junjie XUE1, Xiaoying HU1, Junjiao ZHANG3
Journal of Fuel Chemistry and Technology | 2026, 54(7) : 20250302-1423 - 20250302-1429
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Journal of Fuel Chemistry and Technology | 2026, 54(7): 20250302-1423-20250302-1429
• RESEARCH PAPER •
High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks
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Jie ZHAO1, Changqing DONG1, 2, *, Junjie XUE1, Xiaoying HU1, Junjiao ZHANG3
Affiliations
  • 1National Engineering Laboratory for Biomass Power Generation Equipment (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China
  • 2New Energy Generation National Engineering Research Center (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China
  • 3School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Published: 2026-07-10 doi: 10.1016/S1872-5813(26)60680-9
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Low-temperature Chemical Looping Air Separation (CLAS) is a promising technology for producing oxygen-enriched gas streams. It utilizes the redox properties of solid oxygen carriers to selectively capture and release oxygen from the air. Oxygen vacancy formation energy (Eovf) can be used to evaluate the ease of oxygen release by the oxides. In this study, the applicable range of Eovf for CLAS oxygen carriers was determined to be <2.3 eV using the thermodynamic calculations. A graph neural network (GNN) was trained and used to predict the Eovf. By predicting Eovf with the GNN model, a screening from the 3649 compositions of SrxA1−xFeyB1−yO3 perovskites was conducted to discover the CLAS oxygen carriers. The prediction results indicate that the doping of Ba and Ca ions at the A site and Co ions at the B-site can effectively reduce the Eovf of the perovskite structure. The feasibility of screening the CLAS oxygen carriers using the Eovf method was validated through the rediscovery some known materials. These results support the effectiveness of GNNs in accelerating the discovery of CLAS oxygen carriers.

chemical looping air separation  /  oxygen vacancy formation energy  /  graph neural network  /  material discovery
赵杰, 董长青, 薛俊杰, 胡笑颖, 张俊姣. 使用图神经网络高通量筛选用于低温化学链空气分离的SrxA1−xFeyB1−yO3钙钛矿. 燃料化学学报（中英文）, 2026 , 54 (7) : 20250302-1423 -20250302-1429 . DOI: 10.1016/S1872-5813(26)60680-9
Jie ZHAO, Changqing DONG, Junjie XUE, Xiaoying HU, Junjiao ZHANG. High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks[J]. Journal of Fuel Chemistry and Technology, 2026 , 54 (7) : 20250302-1423 -20250302-1429 . DOI: 10.1016/S1872-5813(26)60680-9
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Chemical looping air separation (CLAS) is an attractive technology for industrial applications, owing to its high efficiency, low energy consumption, and the potential to integrate with thermoelectric power generation cycles to reduce CO2 emissions[1−3]. CLAS operates using oxide-based oxygen carriers that can reversibly absorb and release O2 in response to changes in the oxygen partial pressure or temperature. As shown in Figure 1, when the oxygen partial pressure is low, the lattice oxygen in the oxide is released in the form of gaseous oxygen, accompanied by the reduction of the oxide. Subsequently, the reduced oxide can be regenerated in air or oxygen-rich stream. Repeating this process in a cyclic manner enables a continuous air separation and oxygen production.
Perovskite materials possess superior oxygen transport capabilities, high chemical stability, and tunable redox properties, which render them ideal oxygen-carrier candidates for chemical looping (CL) processes[1,4−6]. SrFeO3−δ with a perovskite structure has been studied for low-temperature CLAS reactions (400−650 °C) due to its excellent oxygen release and absorption capabilities[2,7]. The perovskite structure of SrFeO3−δ provides the possibility of doping substitution atoms at the A-site or B-site to further regulate and enhance the material´s redox properties[7−8]. For instance, the doping of A-site with Ba[9] or Ca[8,10] and B-site with Co[11−12] of SrFeO3 has demonstrated an enhanced CLAS performance. However, the search for optimal doping elements and concentrations still relies largely on conventional heuristics and trial-and-error methods.
Oxygen vacancy formation energy (Eovf) can be used to evaluate the redox performance of CLAS materials[13−15]. Oxygen carrier materials with lower Eovf are more likely to release lattice oxygen and have a better performance in low-temperature CLAS[16−17]. Density functional theory (DFT) method is commonly used to calculate the Eovf of oxides in crystal structures[18−19]. The Eovf can be calculated through Equation (1):
$ \begin{matrix}{E}_{{\mathrm{ovf}}}={E}_{{\mathrm{def}}}-{E}_{{\mathrm{perf}}}+{\mu }_{{\mathrm{O}}} \end{matrix} $
where $ {E}_{{\mathrm{def}}} $ is the total energy of the supercell with an O vacancy, $ {E}_{{\mathrm{perf}}} $ is the total energy of the pristine supercell, $ {\mu }_{{\mathrm{O}}} $ is the O chemical potential. However, calculating the Eovf using DFT is expensive and time-consuming.
Machine learning methods have been researched for rapid prediction of Eovf. Linear models based on DFT descriptors such as band gap and formation energy can predict Eovf with a mean absolute error (MAE) of 0.39 eV, without the need to construct a supercell with an oxygen vacancy[20−21]. The unified atomistic line graph neural network force-field (ALIGNN-FF), trained with formation energy data of the defect-free crystals, can be directly applied to predict the Eovf with an MAE of 0.87 eV[22−23]. The defect graph neural network (dGNN) model, trained with Eovf data calculated by DFT, predicts the Eovf of oxides (containing elements present in the training set) with an MAE of 0.52 eV[14].
In this study, an ALIGNN model was trained using a curated dataset of Eovf and used for the prediction of Eovf. The range of Eovf suitable for CLAS oxygen carriers was estimated using thermodynamic calculations. High-throughput screening for CLAS was performed on 3649 SrxA1−xFeyB1−yO3 perovskites through stability judgment and Eovf prediction. The feasibility of the Eovf method for screening CLAS oxygen carriers was confirmed through materials rediscovery.
1　Methodology
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1.1　Machine learning
The charge-neutral Eovf dataset[14,24] utilized in this article was based on DFT-computed data using the Generalized Gradient Approximation (GGA) with Hubbard U correction. The dataset comprises 48 cationic elements, 102 space groups, 901 compounds, 2329 distinct oxygen vacancy structures, 9342 oxygen sites (see Section S1 in Supplemental Information). The distribution of elements in the dataset is shown in Figure 2(a).
The Atomistic Line Graph Neural Network (ALIGNN) demonstrated superior performance over other graph neural networks in multiple atomistic prediction tasks by incorporating bond angle information[23,25]. As shown in Figure 2(b), the ALIGNN was trained to predict the Eovf at all oxygen sites within the crystal structures. Details on the ALIGNN can be found in the related paper[23,25]. In brief, each node in the atomistic graph is assigned 9 input node features based on its atomic species: electronegativity, group number, covalent radius, valence electrons, first ionization energy, electron affinity, block and atomic volume. The inter-atomic bond distances are used as edge features with a radial basis function up to a cut-off of 8 Å. A periodic 12-nearest-neighbor graph construction was used. This atomistic graph is then used for constructing the corresponding line graph using interatomic bond-distances as nodes and bond-angles as edge features. The dataset was partitioned into training, validation, and test sets at a ratio of 80∶10∶10, and an ALIGNN model was trained to predict the Eovf.
1.2　Thermodynamic calculations
The Eovf predicted by the ALIGNN corresponds to the zero-temperature limit. Therefore, thermodynamic calculations are required to determine the range of Eovf values that are suitable for various chemical looping applications. In light of the comparable Eovf across distinct sites in the ideal perovskite structure[14,26], it is reasonable to assume that the properties of oxygen vacancies have a degree of similarity among these sites. At a specified temperature T, the Gibbs free energy for the formation of oxygen vacancies in the crystalline structure is expressed as follows[27]：
$ \Delta G=n*{h}_{{\mathrm{ovf}}}-T{\Delta S}_{{\mathrm{cf}}} $
where n represents the number of vacancies, hovf denotes the formation enthalpy per defect (including contributions from vibrational enthalpy and entropy for each defect), and ΔScf is the configurational entropy. Assuming a total number of N atoms in the system (where N ≫ n), the structural entropy is derived via Stirling’s approximation as follows[27]：
$ {\Delta S}_{{\mathrm{cf}}}={k}_{{\mathrm{B}}}\times n \times (\ln N-\ln n)$
In the context of solids within the zero-temperature limit, the volume change is negligible, and thus the hovf can be reasonably approximated by the Eovf[28]. The chemical potential Δμ is introduced to determine the Gibbs free energy of oxygen vacancy formation under the relevant thermodynamic constraints[14]：
$ \Delta G=n \times \left({E}_{{\mathrm{ovf}}}+{\Delta }\mu \right)-T\times {k}_{{\mathrm{B}}}\times n \times (\ln N-\ln n)$
when the system reaches equilibrium, the condition $ \Delta G=0 $ leads to the following expression for the dimensionless oxygen vacancy concentration：
$ {c}_{v}=\frac{n}{N}={{\mathrm{e}}}^{-\left({E}_{{\mathrm{ovf}}}+{\Delta }\mu \right)/\left({k}_{{\mathrm{B}}}T\right)} $
Given a specific oxygen vacancy concentration, the corresponding Δμ at the thermodynamic constraints can be determined by inverting the above expression[14]. By comparing the Δμ curves corresponding to different Eovf values on the Ellingham diagram, the applicable ranges of Eovf for different chemical looping applications can be identified[13−14].
1.3　High-throughput screening for SrxA1−xFeyB1−yO3
The SrxA1−xFeyB1−yO3 perovskite structures were constructed by substituting A- and/or B-site cations in SrFeO3 using the pymatgen package[29]. Table 1 summarizes the dopant types and concentrations investigated in this study. By exhausting all possible combinations of A- and/or B-site dopants in the selected cation network, 3520 perovskite models were constructed. Through a preliminary screening process, some of these combinations were filtered out due to their inability to form stable perovskite phases. Firstly, the total valence of the compositions was examined to determine whether it can be zero (see Section S2 in Supplemental Information). Besides charge neutrality, a modified tolerance factor $ \tau =\dfrac{{r}_{{\mathrm{O}}}}{{r}_{{\mathrm{B}}}}-{n}_{{\mathrm{A}}}\left({n}_{{\mathrm{A}}}-\dfrac{{r}_{{\mathrm{A}}}/{r}_{{\mathrm{B}}}}{\ln\left({r}_{{\mathrm{A}}}/{r}_{{\mathrm{B}}}\right)}\right) $[30] was applied to estimate the stabilities of the perovskite structures, where rO, rA, rB represent the radii of oxygen anion, and the A- and B-site cations (see Table S1 in Supplemental Information). nA and nB are the oxidation states of the A- and B-site cations, respectively. The modified tolerance factor (τ) carries more chemical information and exhibits better predictive ability than the classical Goldschmidt tolerance factor[4,30].
After the initial construction of the doped crystal structures, the fast structure optimization was performed using the ALIGNN-FF[23] model, for which the model parameters were directly adopted from the literature[23] and shared publicly. The ALIGNN-FF was developed using both energies and forces for applications, such as, in fast structure optimization. Then, the optimized crystal structures were input into the ALIGNN model which is then trained for Eovf to predict the Eovf for each oxygen site. The average of all the Eovf values for each crystal structure was calculated and used as an indicator to evaluate the redox performance. The process is illustrated in Figure 3.
2　Results and discussion
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2.1　The range of Eovf for oxygen carrier selection
As shown in Figure 4, the Gibbs free energy of several key reactions is used to distinguish different chemical looping reactions[13,31]. The data of reactions was from calculations with the FactSage software. For a target oxygen vacancy concentration of [VO] = 1%, Figure 4 shows the oxygen vacancy formation chemical potential Δμ at various temperatures for different values of Eovf. It has been shown that the trends of Eovf across different perovskite oxides remain the same for different extents of oxygen vacancy[32]. Therefore, it is reasonable to estimate the performance of the oxygen carrier by using Eovf of the chemical potential corresponding to [VO] = 1%. From Figure 4, it can be inferred that for CLAS, the suitable Eovf values should be approximately less than 2.3 eV when the temperature is higher than 500 ℃. Previous studies have indicated that materials with Eovf less than 2.5 eV are suitable for thermochemical O2 storage and pumping/separation[13,15], which is in agreement with the estimated results. Additionally, this estimation method can also be applied to assess the suitability of materials in fields such as chemical looping full oxidation (CLFO), chemical looping with oxygen uncoupling (CLOU), and chemical looping syngas production.
2.2　The training results of the ALIGNN model for predicting Eovf
The ALIGNN model trained for predicting Eovf was tested. The oxides in the test set did not exist in the training set or the validation set. As shown in Figure 5, the ALIGNN model achieved a mean absolute error (MAE) of 0.26 eV in predicting the Eovf values at different oxygen sites of the oxides in the test set, and the coefficient of determination (R2) reached 0.94. The closer the MAE is to 0 and the R2 is to 1, the higher the accuracy of the model. This result outperforms the linear model using the DFT descriptors (0.39 eV)[20−21] and the dGNN model based on a simpler convolutional architecture relative to the ALIGNN (0.52 eV)[14].
2.3　High-throughput screening of SrxA1−xFeyB1−yO3
The screening results from the 3649 compositions of SrxA1−xFeyB1−yO3 perovskites are presented in Figure 6. Most of the compositions containing ions of Na, K, Mg and Cu were filtered out due to the inability to achieve electrical neutrality. It is noted that the nonstoichiometric cases of perovskites (SrxA1−xFeyB1−yO3−δ) were not considered in the preliminary screening process. The different grids in the Figure 6 may represent the same chemical composition.
The slight differences in the average Eovf for the same material, such as SrxBa1−xFe1Ti0O3 and SrxBa1−xFe1V0O3, may be attributed to random errors introduced during the crystal structure optimization process. For the Sr1−xCaxFeO3, it is known that the Eovf first decreases and then increases with the increase of the Ca2+ concentration from 0 to 0.5[10]. This is consistent with the predictions. In certain compositions, such as Sr0.125Ba0.875CoO3, the doping of the Ba ion at the A-sites also leads to a decrease in Eovf. The doping of the Co ion at the B-sites shows a pronounced trend of decreasing Eovf with an increasing concentration. Among all the compositions with a predicted average Eovf below 2.0 eV, the Co ion is consistently present (see Section S4 in Supplemental Information).
2.4　Rediscovery of low-temperature CLAS materials
Through the screening criterion Eovf < 2.3 eV, a series of known low-temperature CLAS oxygen carriers were rediscovered, as shown in Table 2. This indicates that the Eovf can be used to accelerate the material discovery for the low-temperature CLAS process. Still, this method has certain limitations. The Eovf can be used to assess the ease of oxygen release from the oxides, but it cannot be used to evaluate the material´s re-oxidation extent. For example, although the predicted Eovf for BaCoO3 is as low as 1.44 eV, it is difficult to be fully oxidized after oxygen loss, making it unsuitable for low-temperature CLAS[12].
3　Conclusions
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Based on the graph neural network for predicting the Eovf, this work proposes a fast method for the screening of low-temperature CLAS oxygen carriers, and conducts a high-throughput screening from the 3649 compositions of SrxA1−xFeyB1−yO3 perovskites. Through the thermodynamic estimation, it was found that the Eovf value for the CLAS material should be less than 2.3 eV. An the ALIGNN model was trained for the prediction of the Eovf and the test set shows that the model has a MAE of 0.26 eV. In the high-throughput screening, the doping of the Co ions at the B-site was found to be able to effectively reduce the Eovf of the perovskite structure. And the doping of specific proportions of Ba and Ca ions at the A site can also contribute to lowering the Eovf. Under the screening criterion of Eovf < 2.3 eV, the model rediscovered several previously reported oxygen carriers for the low-temperature CLAS. This work demonstrates that using GNN to predict Eovf can accelerate the discovery process of CLAS materials. In a broader context, the presented methods may also accelerate the discovery of materials for other related chemical looping applications, such as chemical looping full oxidation and chemical looping syngas production.
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Appendix
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Year 2026 volume 54 Issue 7
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doi: 10.1016/S1872-5813(26)60680-9
  • Receive Date：2025-10-23
  • Online Date：2026-09-23
  • Published：2026-07-10
Article Data
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History
  • Received：2025-10-23
  • Revised：2025-11-21
  • Accepted：2025-12-14
Affiliations
    1National Engineering Laboratory for Biomass Power Generation Equipment (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China
    2New Energy Generation National Engineering Research Center (NCEPU), School of New Energy, North China Electric Power University, Beijing 102206, China
    3School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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