Data-driven discovery of methane hydrate promoters
Source abstract
Abstract
Methane hydrates require extreme conditions, and promoter discovery remains largely empirical. We develop a multimodal deep-learning framework that predicts methane-hydrate equilibrium pressures from molecular structure. Trained on over eighty promoters, the model extrapolates beyond its domain and prospectively identifies ethylene sulfite as a new thermodynamic promoter, experimentally validated within 1 MPa accuracy while forming structure II hydrates.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Methane Hydrates and Related Phenomena · Hydrocarbon exploration and reservoir analysis · Seismic Imaging and Inversion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (58)
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1. Lee, Y., Seo, D., Lee, S. & Park, Y. Advances in nanomaterials for sustainable gas separation and storage: focus on clathrate hydrates. Acc. Chem. Res. 56, 3111–3120 (2023).10.1021/acs.accounts.3c00406 ↗
2. Park, Y. et al. Sequestering carbon dioxide into complex structures of naturally occurring gas hydrates. Proc. Natl. Acad. Sci. USA 103, 12690–12694 (2006).10.1073/pnas.0602251103 ↗
3. Lee, S. et al. Enhancing gas storage capacity by controlling the cage occupancy of natural gas hydrate. Chem. Eng. J. 518, 164493 (2025).10.1016/j.cej.2025.164493 ↗
4. Susilo, R., Ripmeester, J. A. & Englezos, P. Characterization of gas hydrates with PXRD, DSC, NMR, and Raman spectroscopy. Chem. Eng. Sci. 62, 3930–3939 (2007).10.1016/j.ces.2007.03.045 ↗
5. Gupta, A., Lachance, J., Sloan, E. D. & Koh, C. A. Measurements of methane hydrate heat of dissociation using high pressure differential scanning calorimetry. Chem. Eng. Sci. 63, 5848–5853 (2008).10.1016/j.ces.2008.09.002 ↗
6. Seo, D., Moon, S., Lee, Y., Hong, S., Lee, S. & Park, Y. Investigation of tuning behavior of trimethylene oxide hydrate with guest methane molecule and its critical guest concentration. Chem. Eng. J. 389, 123582 (2020).10.1016/j.cej.2019.123582 ↗
7. Sharma, S. & Jana, A. K. A comparative study on four thermodynamic frameworks of the clathrate phase: predicting the three-phase hydrate equilibrium. Ind. Eng. Chem. Res. 64, 17165–17183 (2025).10.1021/acs.iecr.5c00817 ↗
8. Klauda, J. B. & Sandler, S. I. A fugacity model for gas hydrate phase equilibria. Ind. Eng. Chem. Res. 39, 3377–3386 (2000).10.1021/ie000322b ↗
9. Shin, K., Moudrakovski, I. L., Ratcliffe, C. I. & Ripmeester, J. A. Managing hydrogen bonding in clathrate hydrates by crystal engineering. Angew. Chem. Int. Ed. 56, 6171–6175 (2017).10.1002/anie.201700654 ↗
10. Hu, Y. H. & Ruckenstein, E. Clathrate hydrogen hydrate—A promising material for hydrogen storage. Angew. Chem. Int. Ed. 45, 2011–2013 (2006).10.1002/anie.200504149 ↗
11. Li, H., Sun, H., Chen, J., Chen, B., Zhong, D. & Yang, M. Advances, applications, and perspectives of machine learning approaches in predicting gas hydrate phase equilibrium. Energy Fuels 38, 23320–23335 (2024).10.1021/acs.energyfuels.4c04924 ↗
12. Wan, L., Cao, P. & Sheng, J. Phase stability of CH4 and CO2 hydrates under confinement predicted by machine learning. Langmuir 40, 22058–22067 (2024).10.1021/acs.langmuir.4c02357 ↗
13. Mok, J., Go, W. & Seo, Y. Predicting phase equilibria of CO2 hydrate in complex systems containing salts and organic inhibitors for CO2 storage: a machine learning approach. Energy Fuels 38, 5322–5333 (2024).10.1021/acs.energyfuels.3c04930 ↗
14. Song, Y., Zhou, H., Wang, P. & Yang, M. Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks. J. Chem. Thermodyn. 135, 86–96 (2019).10.1016/j.jct.2019.03.030 ↗
15. Landgrebe, M. K. B. & Nkazi, D. Toward a robust, universal predictor of gas hydrate equilibria by means of a deep learning regression. ACS Omega 4, 22399–22417 (2019).10.1021/acsomega.9b02961 ↗
16. Hosseini, M., Boudreault, R. & Leonenko, Y. Insights into predicting equilibrium conditions of clathrate hydrates of methane + water-soluble hydrate former. Fuel 384, 133945 (2025).10.1016/j.fuel.2024.133945 ↗
17. Zong, B., Li, J., Yuan, T., Wang, J. & Yuan, R. Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge. J. Mater. 11, 100916 (2025).
18. Bashiri, R. et al. Discovery of dual ion-electron conductivity of metal–organic frameworks via machine learning-guided experimentation. Chem. Mater. 37, 1143–1153 (2025).10.1021/acs.chemmater.4c02974 ↗
19. Aksu, G. O. & Keskin, S. The COF space: materials features, gas adsorption, and separation performances assessed by machine learning. ACS Mater. Lett. 7, 954–960 (2025).10.1021/acsmaterialslett.4c02594 ↗
20. Ge, W., De Silva, R., Fan, Y., Sisson, S. A. & Stenzel, M. H. Machine learning in polymer research. Adv. Mater. 37, 2413695 (2025).10.1002/adma.202413695 ↗
21. Zhang, L., Bing, Q., Qin, H., Yu, L., Li, H. & Deng, D. Artificial intelligence for catalyst design and synthesis. Matter 8, 102138 (2025).10.1016/j.matt.2025.102138 ↗
22. Kretschmer, F., Seipp, J., Ludwig, M., Klau, G. W. & Böcker, S. Coverage bias in small molecule machine learning. Nat. Commun. 16, 554 (2025).10.1038/s41467-024-55462-w ↗
23. Takeya, S. et al. Phase transition of a structure II cubic clathrate hydrate to a tetragonal form. Angew. Chem. Int. Ed. 55, 9287–9291 (2016).10.1002/anie.201602733 ↗
24. Ok, Y., Lee, S., Seo, D., Yang, W. & Park, Y. Lattice-level structural insight into cyclohexyl amine hydrate for hydrate-based CH4 storage. Chem. Eng. J. 514, 163269 (2025).10.1016/j.cej.2025.163269 ↗
25. Moon, S. et al. Distinct hydrophobic–hydrophilic dual interactions occurring in the clathrate hydrates of 3,3-dimethyl-1-butanol with help gases. Chem. Eng. J. 348, 583–591 (2018).10.1016/j.cej.2018.04.129 ↗
26. Park, Y., Cha, M., Shin, W., Lee, H. & Ripmeester, J. A. Spectroscopic observation of critical guest concentration appearing in tert-butyl alcohol clathrate hydrate. J. Phys. Chem. B 112, 8443–8446 (2008).10.1021/jp804447r ↗
27. Moon, S., Hong, S., Lee, Y., Lee, J. S., Ahn, Y.-H. & Park, Y. Enhancing hydrogen cluster storage in clathrate hydrates via defect-mediated lattice engineering. J. Phys. Chem. C 125, 1767–1773 (2021).10.1021/acs.jpcc.0c10508 ↗
28. Seo, D., Lee, S., Lee, Y. & Park, Y. Tailoring gas hydrate lattice dimensions for enhanced methane selectivity in biogas upgrading. Chem. Eng. J. 472, 145079 (2023).10.1016/j.cej.2023.145079 ↗
29. Khan, S. T. & Moosavi, S. M. Connecting metal-organic framework synthesis to applications using multimodal machine learning. Nat. Commun. 16, 5642 (2025).10.1038/s41467-025-60796-0 ↗
30. Lee, S., Seo, D., Lee, Y., Moon, S. & Park, Y. Promoting thermodynamic stability of hydrogen hydrates with gas-phase modulators for energy-efficient blue hydrogen storage. Fuel 372, 132196 (2024).10.1016/j.fuel.2024.132196 ↗
31. Peng, C. et al. AI-driven discovery and molecular engineering design for enhancing interface stability of black phosphorus. Angew. Chem. Int. Ed. 64, e202508454 (2025).10.1002/anie.202508454 ↗
32. Park, Y., Choi, Y. N., Yeon, S.-H. & Lee, H. Thermal expansivity of tetrahydrofuran clathrate hydrate with diatomic guest molecules. J. Phys. Chem. B 112, 6897–6899 (2008).10.1021/jp8020112 ↗
33. Liu, S., Chen, M., Yao, X. & Liu, H. Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction. J. Pharm. Anal. 15, 101242 (2025).10.1016/j.jpha.2025.101242 ↗
34. Zhang, Q., Mao, D., Tu, Y. & Wu, Y.-Y. A new fingerprint and graph hybrid neural network for predicting molecular properties. J. Chem. Inf. Model. 64, 5853–5866 (2024).10.1021/acs.jcim.4c00586 ↗
35. Daub, E. E. Gibbs phase rule: a centenary retrospect. J. Chem. Educ. 53, 747 (1976).10.1021/ed053p747 ↗
36. Sloan, E. D. Fundamental principles and applications of natural gas hydrates. Nature 426, 353–359 (2003).10.1038/nature02135 ↗
37. Lee, Y., Seo, D., Lee, S. & Park, Y. Unlocking enhanced gas storage capacity in tuned methane hydrates: exploring eutectic compositions and water-to-hydrate conversion. Chem. Eng. J. 475, 146381 (2023).10.1016/j.cej.2023.146381 ↗
38. Lee, S., Seo, D., Lee, Y., Yang, W., Moon, S. & Park, Y. Enhancing hydrate-based natural gas storage capacity via optimal concentrations of epoxycyclopentane. Chem. Eng. J. 502, 157992 (2024).10.1016/j.cej.2024.157992 ↗
39. Reymond, J.-L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).10.1021/ar500432k ↗
40. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).10.1038/nature14539 ↗
41. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).10.1038/323533a0 ↗
42. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. 14th International Joint Conference on Artificial Intelligence - Volume 2) (Morgan Kaufmann Publishers Inc, 1995).
43. Wrodnigg, G. H., Besenhard, J. O. & Winter, M. Ethylene sulfite as electrolyte additive for lithium-ion cells with graphitic anodes. J. Electrochem. Soc. 146, 470 (1999).10.1149/1.1391630 ↗
44. Takeya, S., Udachin, K. A., Moudrakovski, I. L., Susilo, R. & Ripmeester, J. A. Direct space methods for powder X-ray diffraction for guest−host materials: applications to cage occupancies and guest distributions in clathrate hydrates. J. Am. Chem. Soc. 132, 524–531 (2010).10.1021/ja905426e ↗
45. Seol, J., Park, J. & Shin, W. Epoxycyclopentane hydrate for sustainable hydrate-based energy storage: notable improvements in thermodynamic condition and storage capacity. Chem. Commun. 56, 8368–8371 (2020).10.1039/D0CC02195D ↗
46. Conrad, H. et al. Tetrahydrofuran clathrate hydrate formation. Phys. Rev. Lett. 103, 218301 (2009).10.1103/PhysRevLett.103.218301 ↗
47. Adisasmito, S. & Frank, R. J. Hydrates of carbon dioxide and methane mixtures. J. Chem. Eng. Data 36, 68–71 (1991).10.1021/je00001a020 ↗
48. Phan, A. Perspectives on molecular mechanisms of hydrate formation and growth at interfaces: a mini-review. Energy Fuels 39, 10150–10164 (2025).10.1021/acs.energyfuels.5c00942 ↗
49. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).10.1038/s41586-018-0337-2 ↗
50. Liu, J. et al. Prediction of efficient promoter molecules of sH hydrogen hydrate: An ab initio study. Chem. Phys. 516, 15–21 (2019).10.1016/j.chemphys.2018.08.032 ↗
51. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning (eds Doina P., Yee Whye T.) (PMLR, 2017).
52. Janet, J. P. & Kulik, H. J. Predicting electronic structure properties of transition metal complexes with neural networks. Chem. Sci. 8, 5137–5152 (2017).10.1039/C7SC01247K ↗
53. Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).10.1021/acscentsci.7b00572 ↗
54. Lee, S., Moon, S., Ok, Y., Lee, Y. & Park, Y. Magnetically recoverable nanoparticle organic hybrid materials as kinetic hydrate inhibitors. Chem. Eng. J. 501, 157612 (2024).10.1016/j.cej.2024.157612 ↗
55. Lee, Y., Moon, S., Lee, S., Seo, D. & Park, Y. Significance of wettability of porous media and its implication for hydrate-based pre-combustion CO2 capture. Chem. Eng. J. 446, 136832 (2022).10.1016/j.cej.2022.136832 ↗
56. Toby, B. H. & Von Dreele, R. B. GSAS-II: the genesis of a modern open-source all purpose crystallography software package. J. Appl. Crystallogr. 46, 544–549 (2013).10.1107/S0021889813003531 ↗
57. Momma, K. & Izumi, F. VESTA: a three-dimensional visualization system for electronic and structural analysis. J. Appl. Crystallogr. 41, 653–658 (2008).10.1107/S0021889808012016 ↗
58. Frisch, M. J. et al. Gaussian 16 Rev. C.01.) (2016).10.5965/1984723816322015001 ↗