Fumiya
Nishino
a,
Hiroshi
Yoshida
b,
Masato
Machida
c,
Shun
Nishimura
d,
Keisuke
Takahashi
*e and
Junya
Ohyama
*c
aDepartment of Applied Chemistry and Biochemistry, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto, 860-8555, Japan
bCollege of Science and Engineering, Kanazawa University, Kakuma-cho, Kanazawa, 920-1192, Japan
cFaculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto, 860-8555, Japan. E-mail: ohyama@kumamoto-u.ac.jp
dGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, 923-1292, Japan
eDepartment of Chemistry, Hokkaido University, N-15 W-8, Sapporo, 060-0815, Japan. E-mail: keisuke.takahashi@sci.hokudai.ac.jp
First published on 21st August 2023
Catalysts for oxidative coupling of methane (OCM) were designed through machine learning of a property of surface oxygen species on the basis of the knowledge that catalytic performance for the OCM is affected by catalyst surface oxygen species. To select the property of the surface oxygen species used as a guide of catalyst design via machine learning, the relationships between the total yield of ethylene and ethane (C2 yield) and the O1s X-ray photoelectron spectral (XPS) features of the 51 catalysts prepared in our previous study were evaluated. Since a weak correlation was seen between the C2 yield and the O1s XPS peak energy of CO32− species on the catalyst surface, the CO32− peak energy was chosen as the guiding parameter of catalyst design in this work. Machine learning was then performed on the dataset consisting of the CO32− peak energy (objective variable) and the physical quantities of elements in the catalysts (descriptor) to find the important physical quantities determining the CO32− peak energy. According to the important physical quantities, catalyst compositions were predicted. Based on the predicted compositions, 28 catalysts were synthesized to verify that their CO32− peak energies were in the range where high catalytic performance can be expected. Furthermore, the catalysts are tested for the OCM reaction. As a result, Ba–In–Rb/La2O3 was found as a new highly active OCM catalyst having compatible activity to the conventional Mn–Na2WO4/SiO2 catalyst. Therefore, it was demonstrated that the indirect catalyst through machine learning of the catalyst surface property is effective for development of catalysts.
In this study, indirect design of OCM catalysts through machine learning of a property of catalyst surface oxygen species was performed. The property of catalyst surface oxygen species used as the guide for catalyst design was selected from the features of O1s X-ray photoelectron spectra (XPS). Based on the guide of the selected surface oxygen property, OCM catalysts were predicted using machine learning and were verified by XPS measurement and the OCM reaction tests.
M1 | M2 | M3 | A |
---|---|---|---|
Ba | Ce | Cs | SiO2 |
Ba | Hf | Cs | CeO2 or Yb2O3 |
Ba | Hf | Sm | CeO2 or Yb2O3 |
Bi | Hf | Sm | Yb2O3 |
Eu | Hf | Sm | Yb2O3 |
Eu | Hf | Nd | Yb2O3 |
Hf | In | Sm | Yb2O3 |
Bi | In | Sm | Yb2O3 |
Hf | In | Nd | Yb2O3 |
Ba | Hf | — | Yb2O3 |
Cs | Hf | — | Yb2O3 |
Sm | Hf | — | Yb2O3 |
Ce | Hf | — | Yb2O3 |
Cu | Hf | — | Yb2O3 |
Zn | Hf | — | Yb2O3 |
Zn | W | — | Yb2O3 |
Ba | In | Rb | La2O3 |
In | Rb | Yb | La2O3 |
Bi | In | Rb | La2O3 |
Ba | In | Yb | La2O3 |
Ba | Rb | Yb | La2O3 |
Bi | In | Yb | La2O3 |
In | Rb | Sm | La2O3 |
Ce | In | Rb | La2O3 |
Cs | In | Rb | La2O3 |
Bi | Rb | Yb | La2O3 |
Catalysts descriptor is designed using physical quantities from periodic table. In particular, XenonPy is used to assign the physical quantity in order to define catalysts.21 Here, catalyst descriptor is designed by weighted average physical quantities based on the following equation: ∑(Pi × Ci), where Pi is a physical quantity of element i and Ci is its composition (mol%).
Random forest regression was performed on the dataset consisting of “∑(Pi × Ci)” as the descriptor variables and CO32− peak energy as the objective variable to identify the important physical quantities of catalyst elements representing the CO32− peak energy. The importance of descriptor variables is demonstrated in Fig. 2. Ghosh's scale of electronegativity (en_ghosh) and sound velocity have high importance. It should be noted that Ghosh's scale of electronegativity and sound velocity are not considered to directly relate to the CO32− peak energy but indirectly represent some factors determining the CO32− peak energy.
Here, en_ghosh and sound velocity against CO32− are visualized in Fig. 3. It shows that high en_ghosh and low sound velocity tend to result high CO32− energy peak. One can hypothesize that catalysts having high en_ghosh and low sound velocity could result high C2 yield based on the fact high CO32− peak results high C2 yield. Therefore, catalysts having high en_ghosh and low sound velocity are explored from the calculated en_ghosh and sound velocities of a variety of element combinations using ∑(Pi × Ci). The element combinations are created by selecting three elements from the 33 elements for M1, M2, and M3: Li, Na, Mg, Al, Si, Ni, K, Ca, Ti, V, Mn, Fe, Co, Cu, Zn, Rb, Sr, Y, Zr, Mo, Pd, In, Cs, Ba, La, Ce, Nd, Sm, Eu, Yb, Hf, W, Bi. These elements are selected from the elements found in the literature to find new combinations.2 Here, 180048 combinations of M1, M2, M3, and support (33C3 (M1, M2, M3 combinations) × 33 (support) = 180
048) where the mol% is set to 2, 4, 2, and 92, respectively are created and weighted average of en_ghosh and sound velocity are calculated (ESI† Table A). Created catalysts combinations are then visualized as shown in Fig. 4. It must be noted that mol% of support is set to 92 which has a large impact on weighted average, therefore, data are aggregated by support. As it can be seen in Fig. 5, catalysts containing Si, Ce, Bi, Yb, and Sm in supports show high en_ghosh and low sound velocity. Based on the data, catalysts listed in Table 2, which have high en_ghosh and low sound velocity, are designed by randomly selecting a Si-based catalyst having ≥0.177 en_ghosh and ≤2190 of sound velocity, two Ce-based catalysts having ≥0.167 en_ghosh and ≤2110 of sound velocity, and eight Yb-based catalysts having ≥0.218 of en_ghosh and ≤1700 of sound velocity. In addition, La-based catalysts are designed by selecting combinations having ≥0.161 of en_ghosh and ≤2404 of sound velocity since La-based catalysts are known to have relatively high activity for the OCM.13,22 It should be noted that the prediction in this indirect design will not offer accurate or pinpoint prediction of metals–support combinations having high C2 yield because the prediction is based on the weak trend between the CO32− peak energy and the C2 yield. However, the predicted catalyst group by this indirect method may contain good catalysts, which may be different from catalysts the direct prediction can find.
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Fig. 4 Created 180![]() |
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Fig. 5 Catalytic performance of the 28 predicted catalysts for the OCM reaction together with only supports and Mn–Na2WO4/SiO2 for comparison. |
M1 | C1 | M2 | C2 | M3 | C3 | A | CA | en_ghosh | Sound velocity (m s−1) |
---|---|---|---|---|---|---|---|---|---|
Ba | 0.02 | Ce | 0.04 | Cs | 0.02 | Si | 0.92 | 0.177 | 2184 |
Ba | 0.02 | Cs | 0.04 | Hf | 0.02 | Ce | 0.92 | 0.168 | 2109 |
Ba | 0.02 | Cs | 0.04 | Sm | 0.02 | Ce | 0.92 | 0.167 | 2094 |
Ba | 0.02 | Cs | 0.04 | Hf | 0.02 | Yb | 0.92 | 0.218 | 1637 |
Ba | 0.02 | Hf | 0.04 | Sm | 0.02 | Yb | 0.92 | 0.220 | 1651 |
Hf | 0.02 | In | 0.04 | Sm | 0.02 | Yb | 0.92 | 0.219 | 1615 |
Hf | 0.02 | In | 0.04 | Nd | 0.02 | Yb | 0.92 | 0.219 | 1618 |
Bi | 0.02 | In | 0.04 | Sm | 0.02 | Yb | 0.92 | 0.218 | 1590 |
Bi | 0.02 | Hf | 0.04 | Sm | 0.02 | Yb | 0.92 | 0.220 | 1654 |
Eu | 0.02 | Hf | 0.04 | Nd | 0.02 | Yb | 0.92 | 0.220 | 1679 |
Eu | 0.02 | Hf | 0.04 | Sm | 0.02 | Yb | 0.92 | 0.220 | 1676 |
Ba | 0.02 | Hf | 0.04 | Yb | 0.02 | Yb | 0.92 | 0.220 | 1641 |
Cs | 0.02 | Hf | 0.04 | Yb | 0.02 | Yb | 0.92 | 0.220 | 1652 |
Cu | 0.02 | Hf | 0.04 | Yb | 0.02 | Yb | 0.92 | 0.220 | 1680 |
Hf | 0.02 | Yb | 0.04 | Zn | 0.02 | Yb | 0.92 | 0.220 | 1656 |
Hf | 0.02 | Sm | 0.04 | Yb | 0.02 | Yb | 0.92 | 0.220 | 1638 |
Ce | 0.02 | Hf | 0.04 | Yb | 0.02 | Yb | 0.92 | 0.220 | 1651 |
W | 0.02 | Yb | 0.04 | Zn | 0.02 | Yb | 0.92 | 0.220 | 1699 |
Ba | 0.02 | In | 0.04 | Rb | 0.02 | La | 0.92 | 0.162 | 2392 |
In | 0.02 | Rb | 0.04 | Yb | 0.02 | La | 0.92 | 0.162 | 2392 |
Bi | 0.02 | In | 0.04 | Rb | 0.02 | La | 0.92 | 0.163 | 2396 |
Ba | 0.02 | In | 0.04 | Yb | 0.02 | La | 0.92 | 0.164 | 2397 |
Ba | 0.02 | Rb | 0.04 | Yb | 0.02 | La | 0.92 | 0.162 | 2400 |
Bi | 0.02 | In | 0.04 | Yb | 0.02 | La | 0.92 | 0.165 | 2401 |
In | 0.02 | Rb | 0.04 | Sm | 0.02 | La | 0.92 | 0.161 | 2402 |
Ce | 0.02 | In | 0.04 | Rb | 0.02 | La | 0.92 | 0.162 | 2402 |
Cs | 0.02 | In | 0.04 | Rb | 0.02 | La | 0.92 | 0.162 | 2403 |
Bi | 0.02 | Rb | 0.04 | Yb | 0.02 | La | 0.92 | 0.162 | 2404 |
The catalysts were actually prepared based on the predicted elements listed in Table 2. The loading amount of each element was set to 1 wt%. Although the loading amount (1 wt%) is smaller than those calculated from the compositions in the prediction (2 or 4 mol%), the surface composition of the catalysts prepared by the impregnation method are considered to be compatible to or greater than the compositions used in the prediction. As a result, a total of 28 catalysts were prepared based on the prediction (Table 1). To verify the predictions, O1s XPS spectra of the 28 catalysts were measured. All the O1s XPS spectra are shown in Fig. S1.† Each of the spectra was deconvoluted into three peaks to evaluate the CO32− peak energy (Fig. S2†). As a result, the CO32− peak energies of all the 28 catalysts were larger than 531.2 eV (Table S2†), which means that the 28 catalysts are in the high energy side of Fig. 1. Therefore, the predictions in Table 2 were verified.
Fig. 5 shows the results of the OCM reaction over the 28 catalysts together with only supports and Mn–Na2WO4/SiO2 for comparison. The experimental error was evaluated by five blank (no catalyst) tests, which gave 9.7 ± 0.9% of the C2 yield at 900 °C. The La2O3-based catalysts presented <5% of the carbon missing at all reaction temperatures. The SiO2-based catalysts exhibited <5% at ≤800 °C and 10–15% at 900 °C. The Yb2O3- and CeO2-based catalysts showed <5% at ≤800 °C and 5–10% at 900 °C. The increase of the carbon missing at 900 °C and by using the SiO2-based catalyst might be due to coke formation.
The La2O3- and Yb2O3-based catalysts showed higher activity at lower temperatures than those supported on SiO2 and CeO2. More importantly, several predicted catalysts exhibited comparable C2 yields to that of Mn–Na2WO4/SiO2. Specifically, the most active catalyst was Ba–In–Rb/La2O3, which gave 19% C2 yield, 28% CH4 conversion, and 69% C2 selectivity at 700 °C, while the reference catalyst Mn–Na2WO4/SiO2 gave 19% C2 yield, 34% CH4 conversion, and 57% C2 selectivity at 900 °C. Therefore, Ba–In–Rb/La2O3 exhibited comparable or better catalytic performance to Mn–Na2WO4/SiO2 at lower temperature. This demonstrates that the catalyst design by machine learning of catalyst surface properties is effective for development of catalysts.
The maximum C2 yields of the predicted catalysts at 400–900 °C were plotted against their CO32− peak binding energies in Fig. 6. The figure does not show a correlation because of the limited kinds of catalysts in the verification experiment: the left lower data in the figure is derived from SiO2 supported catalyst, and the other data are La2O3, Yb2O3, and CeO2 supported catalysts. This result suggests that the CO32− peak binding energy is not the only descriptor of the OCM reaction. This is consistent with the rough trend in Fig. 1(b). However, the predicted catalysts designed from the not-strong descriptor contained Ba–In–Rb/La2O3 having high catalytic performance. This result shows that the indirect catalyst design is effective in development of catalysts.
Footnote |
† Electronic supplementary information (ESI) available: O1s XPS data; supplementary Tables A and B listing predicted element combinations. See DOI: https://doi.org/10.1039/d3cy00587a |
This journal is © The Royal Society of Chemistry 2023 |