Data-driven discovery of methane hydrate promoters
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.
Fields of Study & Subjects
Methane Hydrates and Related Phenomena
Machine Learning in Materials Science
Phase Equilibria and Thermodynamics
Publication details
- Journal / source
- npj Computational Materials
- Volume
- 12
- Issue
- 1
- Publication date
- 29 January 2026
- Language
- EN