Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

Huan Ma; Yueyue Jiao; Wenping Guo; Xingchen Liu; Yongwang Li; Xiaodong Wen

doi:10.1016/j.xinn.2024.100571

The Innovation > 2024 Vol. 5 > No. 2

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Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

1.
State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
National Energy Center for Coal to Liquids, Synfuels China Co., Ltd., Beijing 101400, China
4.
Beijing Advanced Innovation Center for Materials Genome Engineering, Industry−University Cooperation Base between Beijing Information S&T University and Synfuels China Co., Ltd., Beijing 100101, China

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Corresponding authors: ybyygu@gmail.com (W.G.); liuxingchen@sxicc.ac.cn (X.L.); wxd@sxicc.ac.cn (X.W.)
Received Date: August 04, 2023

Accepted Date: January 02, 2024

Published Online: January 08, 2024

The Innovation 5(2), https://doi.org/10.1016/j.xinn.2024.100571 | Cite this article

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PUBLIC SUMMARY
1. ■ The catalytic activity of multielement solid surfaces relies on their atomic-level structure under working conditions.
2. ■ An active learning scheme for identifying stable surface structures of multielement solids under working conditions is proposed.
3. ■ The morphological evolution of Fe₇C₃ nanoparticles in various chemical environments is accompanied by the drastic redistribution of exposed surface sites.
4. ■ In silico prediction of the surface sites of multielement catalysts in heterogeneous catalysis becomes computationally achievable.

ABSTRACT

ABSTRACT

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at ab initio accuracy using an approach that combines the bond valence model, Gaussian process regression, and ab initio thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.

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[1]	Zhou, Y., Fuentes-Hernandez, C., Shim, J., et al. (2012). A universal method to produce low-work function electrodes for organic electronics. Science *336*: 327–332. https://doi.org/10.1126/science.1218829. View in Article CrossRef Google Scholar
[2]	Hou, L., Liu, X., Ge, X., et al. (2023). Designing of anisotropic gradient surfaces for directional liquid transport: Fundamentals, construction, and applications. Innovation 4: 100508. https://doi.org/10.1016/j.xinn.2023.100508. View in Article CrossRef Google Scholar
[3]	Xu, X., Shi, S., Tang, Y., et al. (2021). Growth of NiAl-layered double hydroxide on graphene toward excellent anticorrosive microwave absorption application. Adv. Sci. 8: 2002658. https://doi.org/10.1002/advs.202002658. View in Article CrossRef Google Scholar
[4]	Stamenkovic, V., Mun, B.S., Mayrhofer, K.J.J., et al. (2006). Changing the activity of electrocatalysts for oxygen reduction by tuning the surface electronic structure. Angew. Chem. Int. Ed. 45: 2897–2901. https://doi.org/10.1002/anie.200504386. View in Article CrossRef Google Scholar
[5]	Nørskov, J.K., Bligaard, T., Rossmeisl, J., et al. (2009). Towards the computational design of solid catalysts. Nat. Chem. 1: 37–46. https://doi.org/10.1038/nchem.121. View in Article CrossRef Google Scholar
[6]	Wu, T., Sun, M., and Huang, B. (2021). Atomic-strain mapping of high-index facets in late-transition-metal nanoparticles for electrocatalysis. Angew. Chem. Int. Ed. 60: 22996–23001. https://doi.org/10.1002/anie.202110636. View in Article CrossRef Google Scholar
[7]	Huš, M., Kopač, D., Bajec, D., et al. (2021). Effect of surface oxidation on oxidative propane dehydrogenation over chromia: An ab initio multiscale kinetic study. ACS Catal. 11: 11233–11247. https://doi.org/10.1021/acscatal.1c01814. View in Article CrossRef Google Scholar
[8]	Cao, J., Rinaldi, A., Plodinec, M., et al. (2020). In situ observation of oscillatory redox dynamics of copper. Nat. Commun. 11: 3554. https://doi.org/10.1038/s41467-020-17346-7. View in Article CrossRef Google Scholar
[9]	Ruan, P., Chen, B., Zhou, Q., et al. (2023). Upgrading heterogeneous Ni catalysts with thiol modification. Innovation 4: 100362. https://doi.org/10.1016/j.xinn.2022.100362. View in Article CrossRef Google Scholar
[10]	Li, H., Jiang, Y., Li, X., et al. (2023). C2+ selectivity for CO2 electroreduction on oxidized Cu-based catalysts. J. Am. Chem. Soc. *145*: 14335–14344. https://doi.org/10.1021/jacs.3c03022. View in Article CrossRef Google Scholar
[11]	de Smit, E., and Weckhuysen, B.M. (2008). The renaissance of iron-based Fischer–Tropsch synthesis: On the multifaceted catalyst deactivation behaviour. Chem. Soc. Rev. 37: 2758–2781. https://doi.org/10.1039/b805427d. View in Article CrossRef Google Scholar
[12]	Alaba, P.A., Abbas, A., Huang, J., et al. (2018). Molybdenum carbide nanoparticle: Understanding the surface properties and reaction mechanism for energy production towards a sustainable future. Renew. Sustain. Energy Rev. 91: 287–300. https://doi.org/10.1016/j.rser.2018.03.106. View in Article CrossRef Google Scholar
[13]	Fischer, N., and Claeys, M. (2020). In situ characterization of Fischer–Tropsch catalysts: A review. J. Phys. D Appl. Phys. 53: 293001. https://doi.org/10.1088/1361-6463/ab761c. View in Article CrossRef Google Scholar
[14]	Li, H., Jiao, Y., Davey, K., et al. (2023). Data-driven machine learning for understanding surface structures of heterogeneous catalysts. Angew. Chem. Int. Ed. 62: e202216383. https://doi.org/10.1002/anie.202216383. View in Article CrossRef Google Scholar
[15]	Padhi, S., and Behera, A. (2022). Biosynthesis of silver nanoparticles: Synthesis, mechanism, and characterization. In Agri-Waste and Microbes for Production of Sustainable Nanomaterials, K.A. Abd-Elsalam, R. Periakaruppan, and S. Rajeshkumar, eds. (Elsevier), pp. 397–440. https://doi.org/10.1016/b978-0-12-823575-1.00008-1. View in Article Google Scholar
[16]	Ke, X., Bittencourt, C., and Van Tendeloo, G. (2015). Possibilities and limitations of advanced transmission electron microscopy for carbon-based nanomaterials. Beilstein J. Nanotechnol. 6: 1541–1557. https://doi.org/10.3762/bjnano.6.158. View in Article CrossRef Google Scholar
[17]	Wang, Z.L., and Lee, J.L. (2008). Electron microscopy techniques for imaging and analysis of nanoparticles. In Developments in Surface Contamination and Cleaning, R. Kohli and K.L. Mittal, eds. (William Andrew Publishing), pp. 395–443. https://doi.org/10.1016/B978-0-323-29960-2.00009-5. View in Article Google Scholar
[18]	Hansen, T.W., and Wagner, J.B. (2012). Environmental transmission electron microscopy in an aberration-corrected environment. Microsc. Microanal. 18: 684–690. https://doi.org/10.1017/S1431927612000293. View in Article CrossRef Google Scholar
[19]	Reuter, K., and Scheffler, M. (2001). Composition, structure, and stability of RuO2(110) as a function of oxygen pressure. Phys. Rev. B 65: 035406. https://doi.org/10.1103/PhysRevB.65.035406. View in Article CrossRef Google Scholar
[20]	Reuter, K., and Scheffler, M. (2003). Composition and structure of the RuO2(110) surface in an O2 and CO environment: Implications for the catalytic formation of CO2. Phys. Rev. B 68: 045407. https://doi.org/10.1103/PhysRevB.68.045407. View in Article CrossRef Google Scholar
[21]	Opalka, D., Scheurer, C., and Reuter, K. (2019). Ab initio thermodynamics insight into the structural evolution of working IrO2 catalysts in proton-exchange membrane electrolyzers. ACS Catal. 9: 4944–4950. https://doi.org/10.1021/acscatal.9b00796. View in Article CrossRef Google Scholar
[22]	Timmermann, J., Kraushofer, F., Resch, N., et al. (2020). IrO2 surface complexions identified through machine learning and surface investigations. Phys. Rev. Lett. *125*: 206101. https://doi.org/10.1103/PhysRevLett.125.206101. View in Article CrossRef Google Scholar
[23]	Lee, Y., Scheurer, C., and Reuter, K. (2022). Epitaxial core-shell oxide nanoparticles: First-principles evidence for increased activity and stability of rutile catalysts for acidic oxygen evolution. ChemSusChem 15: e202200015. https://doi.org/10.1002/cssc.202200015. View in Article CrossRef Google Scholar
[24]	Li, H., and Reuter, K. (2022). Ab initio thermodynamic stability of carbide catalysts under electrochemical conditions. ACS Catal. 12: 10506–10513. https://doi.org/10.1021/acscatal.2c01732. View in Article CrossRef Google Scholar
[25]	Kocer, E., Ko, T.W., and Behler, J. (2022). Neural network potentials: A concise overview of methods. Annu. Rev. Phys. Chem. 73: 163–186. https://doi.org/10.1146/annurev-physchem-082720-034254. View in Article CrossRef Google Scholar
[26]	Rivera de la Cruz, J.G., Sabbe, M.K., and Reyniers, M.-F. (2017). First principle study on the adsorption of hydrocarbon chains involved in Fischer–Tropsch synthesis over iron carbides. J. Phys. Chem. C *121*: 25052–25063. https://doi.org/10.1021/acs.jpcc.7b05864. View in Article CrossRef Google Scholar
[27]	Ulissi, Z.W., Tang, M.T., Xiao, J., et al. (2017). Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO2 reduction. ACS Catal. 7: 6600–6608. https://doi.org/10.1021/acscatal.7b01648. View in Article CrossRef Google Scholar
[28]	Sun, S.P., Zhu, J.L., Gu, S., et al. (2019). First principles investigation of the surface stability and equilibrium morphology of MoO3. Appl. Surf. Sci. *467–468*: 753–759. https://doi.org/10.1016/j.apsusc.2018.10.162. View in Article CrossRef Google Scholar
[29]	Zuo, E., Dou, X., Chen, Y., et al. (2021). Electronic work function, surface energy and electronic properties of binary Mg-Y and Mg-Al alloys: A DFT study. Surf. Sci. *712*: 121880. https://doi.org/10.1016/j.susc.2021.121880. View in Article CrossRef Google Scholar
[30]	Esposito, D. (2018). Mind the gap. Nat. Catal. 1: 807–808. https://doi.org/10.1038/s41929-018-0188-0. View in Article CrossRef Google Scholar
[31]	Brown, I.D. (2009). Recent developments in the methods and applications of the bond valence model. Chem. Rev. *109*: 6858–6919. https://doi.org/10.1021/cr900053k. View in Article CrossRef Google Scholar
[32]	Opeyemi Otun, K., Yao, Y., Liu, X., et al. (2021). Synthesis, structure, and performance of carbide phases in Fischer–Tropsch synthesis: A critical review. Fuel *296*: 120689. https://doi.org/10.1016/j.fuel.2021.120689. View in Article CrossRef Google Scholar
[33]	Chang, Q., Zhang, C., Liu, C., et al. (2018). Relationship between iron carbide phases (ε-Fe2C, Fe7C3, and χ-Fe5C2) and catalytic performances of Fe/SiO2 Fischer–Tropsch catalysts. ACS Catal. 8: 3304–3316. https://doi.org/10.1021/acscatal.7b04085. View in Article CrossRef Google Scholar
[34]	Kresse, G., and Furthmüller, J. (1996). Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54: 11169–11186. https://doi.org/10.1103/PhysRevB.54.11169. View in Article CrossRef Google Scholar
[35]	Kresse, G., and Furthmüller, J. (1996). Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6: 15–50. https://doi.org/10.1016/0927-0256(96)00008-0. View in Article CrossRef Google Scholar
[36]	Perdew, J.P., Burke, K., and Ernzerhof, M. (1996). Generalized gradient approximation made simple. Phys. Rev. Lett. 77: 3865–3868. https://doi.org/10.1103/PhysRevLett.77.3865. View in Article CrossRef Google Scholar
[37]	Blöchl, P. (1994). Projector augmented-wave method. Phys. Rev. B 50: 17953–17979. https://doi.org/10.1103/PhysRevB.50.17953. View in Article CrossRef Google Scholar
[38]	Kresse, G., and Joubert, D. (1999). From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59: 1758–1775. https://doi.org/10.1103/PhysRevB.59.1758. View in Article CrossRef Google Scholar
[39]	Ong, S.P., Richards, W.D., Jain, A., et al. (2013). Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 68: 314–319. https://doi.org/10.1016/j.commatsci.2012.10.028. View in Article CrossRef Google Scholar
[40]	Ma, H., Jiao, Y., Guo, W., et al. (2020). Predicting crystal morphology using a geometric descriptor: A comparative study of elemental crystals with high-throughput DFT calculations. J. Phys. Chem. C *124*: 15920–15927. https://doi.org/10.1021/acs.jpcc.0c03537. View in Article CrossRef Google Scholar
[41]	Liu, X.-W., Zhao, S., Meng, Y., et al. (2016). Mössbauer spectroscopy of iron carbides: From prediction to experimental confirmation. Sci. Rep. 6: 26184. https://doi.org/10.1038/srep26184. View in Article CrossRef Google Scholar
[42]	Bisbo, M.K., and Hammer, B. (2020). Efficient global structure optimization with a machine-learned surrogate model. Phys. Rev. Lett. *124*: 086102. https://doi.org/10.1103/PhysRevLett.124.086102. View in Article CrossRef Google Scholar
[43]	Ulissi, Z.W., Singh, A.R., Tsai, C., et al. (2016). Automated discovery and construction of surface phase diagrams using machine learning. J. Phys. Chem. Lett. 7: 3931–3935. https://doi.org/10.1021/acs.jpclett.6b01254. View in Article CrossRef Google Scholar
[44]	Ulissi, Z.W., Medford, A.J., Bligaard, T., et al. (2017). To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8: 14621. https://doi.org/10.1038/ncomms14621. View in Article CrossRef Google Scholar
[45]	Mamun, O., Winther, K.T., Boes, J.R., et al. (2020). A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts. npj Comput. Mater. 6: 177. https://doi.org/10.1038/s41524-020-00447-8. View in Article CrossRef Google Scholar
[46]	Ringe, E., Van Duyne, R.P., and Marks, L.D. (2011). Wulff construction for alloy nanoparticles. Nano Lett. 11: 3399–3403. https://doi.org/10.1021/nl2018146. View in Article CrossRef Google Scholar
[47]	Honkala, K., Hellman, A., Remediakis, I.N., et al. (2005). Ammonia synthesis from first-principles calculations. Science *307*: 555–558. https://doi.org/10.1126/science.1106435. View in Article CrossRef Google Scholar
[48]	Davydov, V., Rakhmanina, A., Allouchi, H., et al. (2012). Carbon-encapsulated iron carbide nanoparticles in the thermal conversions of ferrocene at high pressures. Fullerenes, Nanotubes, Carbon Nanostruct 20: 451–454. https://doi.org/10.1080/1536383X.2012.655649. View in Article CrossRef Google Scholar
[49]	Malina, O., Jakubec, P., Kašlík, J., et al. (2017). A simple high-yield synthesis of high-purity Hägg carbide (χ-Fe5C2) nanoparticles with extraordinary electrochemical properties. Nanoscale 9: 10440–10446. https://doi.org/10.1039/C7NR02383A. View in Article CrossRef Google Scholar
[50]	Romanyuk, O., Hattori, K., Someta, M., et al. (2014). Surface structure and electronic states of epitaxial β-FeSi2(100)/Si(001) thin films: Combined quantitative LEED, ab initio DFT, and STM study. Phys. Rev. B 90: 155305. https://doi.org/10.1103/PhysRevB.90.155305. View in Article CrossRef Google Scholar
[51]	Liu, Q.-Y., Shang, C., and Liu, Z.-P. (2021). In situ active site for CO activation in Fe-catalyzed Fischer–Tropsch synthesis from machine learning. J. Am. Chem. Soc. *143*: 11109–11120. https://doi.org/10.1021/jacs.1c04624. View in Article CrossRef Google Scholar
[52]	Cuong, L.T., Dung, N.D., Tuan, T.Q., et al. (2018). In situ observation of phase transformation in iron carbide nanocrystals. Micron *104*: 61–65. https://doi.org/10.1016/j.micron.2017.10.009. View in Article CrossRef Google Scholar
[53]	Liu, P., Qin, R., Fu, G., et al. (2017). Surface coordination chemistry of metal nanomaterials. J. Am. Chem. Soc. *139*: 2122–2131. https://doi.org/10.1021/jacs.6b10978. View in Article CrossRef Google Scholar
[54]	Gu, G.H., Lim, J., Wan, C., et al. (2021). Autobifunctional mechanism of jagged Pt nanowires for hydrogen evolution kinetics via end-to-end simulation. J. Am. Chem. Soc. *143*: 5355–5363. https://doi.org/10.1021/jacs.0c11261. View in Article CrossRef Google Scholar
[55]	Huo, C.-F., Li, Y.-W., Wang, J., et al. (2009). Insight into CH4 formation in iron-catalyzed Fischer–Tropsch synthesis. J. Am. Chem. Soc. *131*: 14713–14721. https://doi.org/10.1021/ja9021864. View in Article CrossRef Google Scholar
[56]	Chun, H.-J., and Kim, Y.T. (2021). Theoretical study of CO adsorption and activation on orthorhombic Fe7C3(001) surfaces for Fischer–Tropsch synthesis using density functional theory calculations. Energies 14: 563. https://doi.org/10.3390/en14030563. View in Article CrossRef Google Scholar
[57]	Liu, Q.-Y., Shang, C., and Liu, Z.-P. (2022). In situ active site for Fe-catalyzed Fischer–Tropsch synthesis: Recent progress and future challenges. J. Phys. Chem. Lett. 13: 3342–3352. https://doi.org/10.1021/acs.jpclett.2c00549. View in Article CrossRef Google Scholar
[58]	Tajima, S., and Hirano, S.-i. (1990). Synthesis and magnetic properties of Fe7C3 particles with high saturation magnetization. Jpn. J. Appl. Phys. 29: 662. https://doi.org/10.1143/JJAP.29.662. View in Article CrossRef Google Scholar
[59]	Starchikov, S.S., Zayakhanov, V.A., Vasiliev, A.L., et al. (2021). Core@shell nanocomposites Fe7C3/FexOy/C obtained by high pressure-high temperature treatment of ferrocene Fe(C5H5)2. Carbon *178*: 708–717. https://doi.org/10.1016/j.carbon.2021.03.052. View in Article CrossRef Google Scholar
[60]	van der Maaten, L.J.P. (2014). Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15: 3221–3245. View in Article Google Scholar

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Ma H., Jiao Y., Guo W., et al., (2024). Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments. The Innovation 5(2), 100571. https://doi.org/10.1016/j.xinn.2024.100571

Ma H., Jiao Y., Guo W., et al., (2024). Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments. The Innovation 5(2), 100571. https://doi.org/10.1016/j.xinn.2024.100571

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Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

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