Unveiling the hidden reaction kinetic network of carbon dioxide in supercritical aqueous solutions
Chu Li, Yuan Yao, Ding Pan

TL;DR
This study uses advanced simulations and machine learning to uncover detailed reaction pathways and intermediates of CO$_2$ in supercritical water, revealing new stable species and mechanisms especially under nanoconfinement.
Contribution
It introduces a novel combination of ab initio molecular dynamics and unsupervised learning to automatically identify complex reaction pathways and intermediates of CO$_2$ in supercritical water, including under nanoconfinement.
Findings
Discovery of a stable pyrocarbonate intermediate in nanoconfined water.
Identification of pyrocarbonic acid formation in supercritical water.
Revealed different proton transfer mechanisms in bulk versus nanoconfined solutions.
Abstract
Dissolution of CO in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite enormous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO in supercritical water both in the bulk and nanoconfined states. The integration of unsupervised learning with first-principles data allows us to identify complex reaction coordinates and pathways automatically instead of a priori human speculation. Interestingly, our unbiased modelling found a novel pathway of dissolving CO(aq) under graphene nanoconfinement, involving the pyrocarbonate anion (CO(aq)) as an intermediate state. The pyrocarbonate anion was previously…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science
