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Title

Indirect Design of OCM Catalysts through Machine
Learning of Catalyst Surface Oxygen Species

Author(s)Nishino, Fumiya; Yoshida, Hiroshi; Machida,
Masato; Nishimura, Shun; Takahashi, Keisuke;
Ohyama, Junya

Citation

Catalysis Science & Technology, 13(19): 5576-5581

Issue Date

2023-08-21

Type

Journal Article

Text version

author

URL

http://hdl.handle.net/10119/19337

RightsCopyright (C) 2023 Royal Society of Chemistry.
Fumiya Nishino, Hiroshi Yoshida, Masato Machida,
Shun Nishimura, Keisuke Takahashi and Junya
Ohyama, Catalysis Science & Technology, 2023,
13(19), 5576-5581.
https://doi.org/10.1039/D3CY00587A Reproduced by permission of the Royal Society ofChemistry

Description

Japan Advanced Institute of Science and Technology

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ARTICLE
Indirect Design of OCM Catalysts through Machine Learning ofCatalyst Surface Oxygen Species
Received 00th January 20xx,
Accepted 00th January 20xx
DOI: 10.1039/x0xx00000x

Fumiya Nishino,a Hiroshi Yoshida,b Masato Machida,c Shun Nishimura,d Keisuke Takahashi *e andJunya Ohyama *c
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 ofcatalyst 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 physicalquantities determining the CO32- peak energy. According to the important physical quantities, catalyst compositions werepredicted. Based on the predicted compositions, 28 catalysts were synthesized to verify that their CO32- peak energies werein 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.

Introduction
Oxidative coupling of methane is a direct conversion reaction ofCH4 to ethylene and ethane (C2 compounds), which can offer amore energetically efficient and economical process than theconventional conversion of CH4 to olefin via CO and CH3OH.1
However, the OCM has a problem that the C2 yield hardly
exceeds 30%.1, 2 To overcome this problem, hundreds ofcatalysts have been developed.1-4 Among the catalysts, MnNa2WO4/SiO2 shows relatively high performance for the OCM.5-7
As seen in the case with Mn-Na2WO4/SiO2, combination ofelements is effective in developing the OCM catalysts. Here,data science techniques have been applied to search for goodcombinations of catalyst elements from a vast combinatorial
space.3, 4, 8-15 In fact, in our previous studies, catalysts have beena. Department of Applied Chemistry and Biochemistry, Graduate School of Scienceand Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto,
860-8555, Japanb. College of Science and Engineering, Kanazawa University, Kakuma-cho,Kanazawa, 920-1192, Japanc. Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1
Kurokami, Chuo-ku, Kumamoto, 860-8555, Japand. Graduate School of Advanced Science and Technology, Japan Advanced Instituteof Science and Technology (JAIST), 1-1 Asahidai, Nomi, 923-1292, Japan.
e. Department of Chemistry, Hokkaido University, N-15 W-8, Sapporo, 060-0815,
Japan
Electronic Supplementary Information (ESI) available: O1s XPS data; Supplementary
TablesA
andB
listing
predicted
element
combinations.
See
DOI: 10.1039/x0xx00000x

designed through machine learning and data mining of thedirect relationship between the C2 yield and the descriptors ofelements (e.g., element number, atomic weight, atomic radius,
melting point, evaporation heat, electronegativity, etc.).13 On
the other hand, catalytic performance is strongly affected by
the catalyst surface properties. In the case of the OCM catalysts,
the properties of surface oxygen species are known to have an
impact on the catalytic performance.16-19 Accordingly, the OCM
catalysts can also be designed by machine learning of therelationship between the surface oxygen properties and thedescriptors of catalyst elements. In addition, it is expected thatsome of the catalyst element combinations predicted from thesurface oxygen properties are the same as the ones predictedby the direct catalyst design from the relation between the
catalytic performance and the descriptors of elements, but theothers will be different because the properties of surface
oxygen species do not completely describe the catalytic
performance. Therefore, new catalyst combinations can be
explored with a guide of the rational descriptors.
In this study, indirect design of OCM catalysts throughmachine 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 fromthe 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 wereverified by XPS measurement and the OCM reaction tests.

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Journal Name

Methods
Catalyst preparation.
OCM catalysts predicted by machine learning, M1-M2-M3/A (M1,
M2, M3 = In, K, Rb, Cs, Ba, Yb, Bi, Sm, Ce, Li, W, Hf, Eu, Cu, Zn,
Nd, A = SiO2, La2O3, Yb2O3, CeO2), were prepared by a wet
impregnation method. In(NO3)3 3H2O, KNO3, RbNO3, CsNO3,Ba(NO3)2, Yb(NO3)3 5H2O, Bi(NO3)3 5H2O, Sm(NO3)3 6H2O,
LiNO3, (NH4)6H2W12O40 5H2O, HfOCl2 8H2O,
Zn(NO3)2 6H2O,
2
were used as the metal precursors. The loading amountof each of M1, M2, and M3 was 1 wt%. After impregnation, thematerials were dried at 110 °C overnight and calcined in air at
1000 °C for 3 h. The metals and supports used in the M1-M2M3/A preparation are shown in Table 1. Mn-Na2WO4/SiO2 was
prepared as a reference catalyst by impregnating SiO2 in an
aqueous solution containing Mn(NO3)2 and Na2WO4. After the
impregnation, the material was dried overnight at 110 °C andcalcined at 1000 °C for 3 h (10 °C/min) to obtain MnNa2WO4/SiO2 with 1.9 wt% Mn and 5.0 wt% Na2WO4.13
Table 1. List of the 28 catalysts prepared in this study.

M1
Ba
Ba
Ba
Bi
Eu
Eu
Hf
Bi
Hf
Ba
Cs
Sm
Ce
Cu
Zn
Zn
Ba
In
Bi
Ba
Ba
Bi
In
Ce
Cs
Bi

M2
Ce
Hf
Hf
Hf
Hf
Hf
In
In
In
Hf
Hf
Hf
Hf
Hf
Hf
W
In
Rb
In
In
Rb
In
Rb
In
In
Rb

M3
Cs
Cs
Sm
Sm
Sm
Nd
Sm
Sm
Nd

Rb
Yb
Rb
Yb
Yb
Yb
Sm
Rb
Rb
Yb

A
SiO2
CeO2 or Yb2O3
CeO2 or Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
Yb2O3
La2O3
La2O3
La2O3
La2O3
La2O3
La2O3
La2O3
La2O3
La2O3
La2O3

Catalyst preparation.
The OCM reaction was performed in a fixed-bed flow
reactor. 50 mg of catalystmesh) was placed in a quartz
glass tube reactor (inner diameter (ID) 4 mm) and fixed withquartz wool. Prior to the reaction, a catalyst bed was pretreatedat 400°C for 10 min under a O2 flow of 8.3 mL/min. After purging
with a N2 flow of 12 mL min-1, the reaction was started byflowing a reaction gas mixture of CH4/O2/N2 = 24/7.5/3.0 mL

min-1. Reaction temperature was increased to 500 °C, 600 °C,
700 °C, 800 °C, and 900 °C. The reaction gas was analyzed 15–
30 min after the temperature reached to the set value. The
outlet gas after cold traps at ca. 10 °C was analyzed using a gas
chromatograph with a thermal conductivity detector (490
Micro GC Agilent Technologies). The carbon missing wasdetermined by CH4 conversion – (sum of C2H4, C2H6, CO, and CO2
yield) (%).
XPS measurement.
XPS measurements were performed using K-Alpha (Thermo
Fisher Scientific) with monochromatic X-ray irradiation= 1486.6 eV). The samples were analyzed as prepared without
pretreatment. Binding energy was corrected with C1s peak at
285 eV. Peak deconvolution of O1s XPS spectra was performed
after background removal.
Machine learning and descriptors design.
Random forest regression is implemented within scikit-earn inorder to evaluate the importance of descriptors.20 The numberof tree is set to 100.
Catalysts descriptor is designed using physical quantities
from periodic table. In particular, XenonPy is used to assign thephysical 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 aphysical quantity of element i and Ci is its composition (mol%).

Results and discussion
The relationship between the C2 yield and the O1s XPS spectraof the 51 OCM catalysts prepared in our previous study was
examined to find property of surface oxygen species related tothe catalytic activity for the OCM reaction (Table S1).9 Each of
the O1s XPS spectra was deconvoluted into three peaks, which
are assignable to superoxide (O2 ), carbonate (CO3 ) and latticeoxygen (O ) in order from high energy to low energy according
to the literature.16, 17 The relationship between the C2 yield andthe peak binding energy of each oxygen species is presented in
Figure 1(a)-(c). Although all the three plots do not show strong
correlations, the plot in Figure 1(b) exhibits better correlation
than the those in Figure 1(a) and (c). Thus, the CO32- species canbe a descriptor of the OCM catalyst. The correlation between
the O2-/O2- area ratio and the C2 yield was also investigated aspresented in Figure 1(d) because the O2-/O2- area ratio has beensuggested to be a descriptor of the C2 yield in the literature.17
As a result, the CO32- peak energy exhibited a better correlation
with the C2 yield than the O2-/O2- area ratio in the catalyst
dataset of this study. Although the correlation in Figure 1(b) is
weak, the catalyst design based on the CO32- peak energy is
considered reasonable because the CO32- peak energy can be
related to the surface basicity, which contributes to the catalytic
performance. Thus, the CO32- peak energy was selected as theguiding parameter for catalyst design. Since there is a roughtrend to increase the C2 yield with an increase of the CO3 peak

2 | J. Name., 2012, 00, 1-3

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ARTICLEbinding energy, a high CO3 peak binding energy can guide thedesign of OCM catalysts.

Figure 1. Plots of maximum C2 yield against the three deconvoluted O1s XPS peaks
assignable to (a) O2 , (b) CO3 , and (c) O species. (d) Plot of maximum C2 yieldagainst O2 /O ratio. Maximum C2 yield: the highest C2 yield value obtained witheach catalyst in the experiment protocol.

Random forest regression was performed on the dataset
consisting of “ (Pi * Ci)” as the descriptor variables and CO3
peak energy as the objective variable to identify the important
physical quantities of catalyst elements representing the CO3
peak energy. The importance of descriptor variables isdemonstrated in Figure 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 soundvelocity are not considered to directly relate to the CO32- peakenergy but indirectly represent some factors determining theCO32- peak energy.

Figure 2. The importance of weighted average physical quantities against CO3
peak energy.

en_ghosh and low sound velocity are explored from thecalculated en_ghosh and sound velocities of a variety ofelement combinations using(Pi * Ci). The element
combinations are created by selecting three elements from the33 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 theelements found in the literature to find new combinations.2
Here, 180,048 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 andweighted average of en_ghosh and sound velocity arecalculated (Supplementary Table A). Created catalysts
combinations are then visualized as shown in Figure 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 Figure 5, catalysts containing Si, Ce,
Bi, Yb, and Sm in supports show high en_ghosh and low soundvelocity. Based on the data, catalysts listed in Table 2, whichhave high en_ghosh and low sound velocity, are designed byrandomly selecting a Si-based catalyst having 0.177 en_ghoshand 2190 of sound velocity, two Ce-based catalysts having
0.167 en_ghosh and2110 of sound velocity, and eight Ybbased catalysts having 0.218 of en_ghosh and 1700 of soundvelocity. 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 haverelatively high activity for the OCM.13, 22 It should be noted thatthe prediction in this indirect design will not offer accurate or
pinpoint prediction of metals-support combinations having highC2 yield because the prediction is based on the weak trend
between the CO32- peak energy and the C2 yield. However, thepredicted catalyst group by this indirect method may contain
good catalysts, which may be different from catalysts the direct
prediction can find.

Figure 3. Ghosh’s scale of electronegativity (en_ghosh) and sound velocity against
CO3 peak energy.

Here, en_ghosh and sound velocity against CO3 arevisualized in Figure 3. It shows that high en_ghosh and low
sound velocity tend to result high CO3 energy peak. One canhypothesize that catalysts having high en_ghosh and low soundvelocity could result high C2 yield based on the fact high CO3
peak results high C2 yield. Therefore, catalysts having highThis journal is © The Royal Society of Chemistry 20xx

J. Name., 2013, 00, 1-3 | 3

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Figure 4. Created 180,048 catalysts combination of Ghosh’s scale ofelectronegativity (en_ghosh) and sound velocity.

Table 2. Designed catalysts having high en_ghosh and low sound velocity within eachsupported catalyst.

M1
Ba
Ba
Ba
Ba
Ba
Hf
Hf
Bi
Bi
Eu
Eu
Ba
Cs
Cu
Hf
Hf
Ce
W
Ba
In
Bi
Ba
Ba
Bi
In
Ce
Cs
Bi

C1
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02

M2
Ce
Cs
Cs
Cs
Hf
In
In
In
Hf
Hf
Hf
Hf
Hf
Hf
Yb
Sm
Hf
Yb
In
Rb
In
In
Rb
In
Rb
In
In
Rb

C2
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04

M3
Cs
Hf
Sm
Hf
Sm
Sm
Nd
Sm
Sm
Nd
Sm
Yb
Yb
Yb
Zn
Yb
Yb
Zn
Rb
Yb
Rb
Yb
Yb
Yb
Sm
Rb
Rb
Yb

C3
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02

A
Si
Ce
Ce
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
Yb
La
La
La
La
La
La
La
La
La
La

CA
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92

Sound
en_gho velocity
sh
(m/s)
0.177
2184
0.168
2109
0.167
2094
0.218
1637
0.220
1651
0.219
1615
0.219
1618
0.218
1590
0.220
1654
0.220
1679
0.220
1676
0.220
1641
0.220
1652
0.220
1680
0.220
1656
0.220
1638
0.220
1651
0.220
1699
0.162
2392
0.162
2392
0.163
2396
0.164
2397
0.162
2400
0.165
2401
0.161
2402
0.162
2402
0.162
2403
0.162
2404

deconvoluted into three peaks to evaluate the CO3 peakenergy (Figure S2). As a result, the CO3 peak energies of all the
28 catalysts were larger than 531.2 eV (Table S2), which meansthat the 28 catalysts are in the high energy side of Figure 1.
Therefore, the predictions in Table 2 were verified.
Figure 5 shows the results of the OCM reaction over the 28
catalysts together with only supports and Mn-Na2WO4/SiO2 forcomparison. 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 Yb2O3and 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 exhibitedcomparable 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 bymachine learning of catalyst surface properties is effective fordevelopment of catalysts.

Figure 5. Catalytic performance of the 28 predicted catalysts for the OCM reaction
together with only supports and Mn-Na2WO4/SiO2 for comparison.

The catalysts were actually prepared based on the predicted
elements listed in Table 2. The loading amount of each element
was set to 1wt%. Although the loading amount (1 wt%) issmaller than those calculated from the compositions in theprediction (2 or 4 mol%), the surface composition of thecatalysts 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 preparedbased 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 Figure S1. Each of the spectra was

The maximum C2 yields of the predicted catalysts at 400900 °C were plotted against their CO32- peak binding energies in
Figure 6. The figure does not show a correlation because of thelimited 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 Figure 1(b). However, the predictedcatalysts designed from the not-strong descriptor contained Ba-

4 | J. Name., 2012, 00, 1-3

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ARTICLEIn-Rb/La2O3 having high catalytic performance. This resultshows that the indirect catalyst design is effective indevelopment of catalysts.

Acknowledgements
This work was supported by the Japan Science and Technology
Agency (JST) CREST (grant no. JPMJCR17P2).

References
1
2
3
4
Figure 6. CO3 XPS peak binding energy vs. maximum C2 yield of the predicted 28
catalysts.

5
6
7

Conclusions
The OCM catalysts were developed through machine learningof property of their surface oxygen species. In this study, theCO3 peak energy was selected as the property of the surface
oxygen species contributing to the OCM catalysts based on therough trend of the C2 yield increasing with the CO3 peakenergy in the data of the previously reported catalysts. Machinelearning was then performed to find as the important physicalquantities of catalyst elements representing the CO3 peakenergy. Based on the relation between CO3 peak energy andthe important physical quantities, the catalyst compositions
resulting high CO3 peak energy were designed and the 28
catalysts were experimentally prepared. The O1s XPS spectral
analysis verified that all the 28 catalysts have relatively highCO3 peak energy. Furthermore, Ba-In-Rb/La2O3, one of thepredicted catalysts, exhibited compatible C2 yield with a higher
selectivity at lower reaction temperature compared to theconventional Mn-Na2WO4/SiO2 catalyst. The results suggestthat the indirect design of catalyst through machine learning of
catalyst surface property is effective in developing catalysts.
This means that, if one has an understanding or a hypothesis
about how catalyst surface properties affect a target catalytic
reaction and there is appropriate database of catalyst
properties, one can obtain catalyst designs in a similar way tothe indirect catalyst design method performed in this study.

The manuscript was written through contributions of all authors. All
authors have given approval to the final version of the manuscript.

There are no conflicts to declare.

9
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Author Contributions

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