Speaker
Description
The methanol-to-olefins (MTO) process utilizes acidic zeolites to convert methanol into olefins and plays a
pivotal role in the production of Sustainable Aviation Fuels (SAFs)1. This process involves a complex network
of reactions, with multiple competing pathways and mechanisms2. The initial and critical step is the
dehydration of methanol to form dimethyl ether (DME)3. Experimental and computational studies have
revealed two main reaction pathways for this step, whose predominance depends strongly on the operating
temperature4.
Given the central role of this step in shaping the subsequent formation of the so-called hydrocarbon pool, it
is essential to achieve a clear and detailed molecular-level description. However, current modeling
approaches fall short of capturing the realistic complexity of the process under operando conditions, limiting
their ability to provide accurate thermodynamic and mechanistic insights5. In addition, a systematic
exploration of the broad physico-chemical space—defined by variations in temperature and zeolite
topology—is still lacking, yet crucial for initiating an effective in silico screening aimed at process
optimization.
In this work, we address these challenges by employing metadynamics simulations coupled with machine
learning potentials (MLPs), exploring a range of temperatures (600–800 K) and zeolite frameworks (chabazite,
ZSM-5, and mordenite). This approach enables the investigation of reaction mechanisms with atomistic
resolution, estimation of energy barriers, and construction of free energy profiles.
Using metadynamics simulations powered by MLPs trained on accurate ab initio data, we elucidated both the
associative and dissociative pathways involved in methanol dehydration. The resulting free energy profiles
allowed us to determine the thermodynamic and kinetic parameters associated with each mechanism. By
comparing these parameters, we identified the dominant reaction pathway for the methanol-to-DME
conversion under different conditions.
This study establishes a robust framework for mechanistic analysis of catalytic reactions in complex
environments and provides fundamental insights into the MTO process. Our findings aim to support the
development of more efficient catalytic systems and contribute to the broader goal of advancing sustainable
and economically viable energy solutions.
(1) Van der Mynsbrugge, J.; Moors, S. L. C.; De Wispelaere, K.; Van Speybroeck, V. ChemCatChem 2014, 6 (7), 1906
1918. https://doi.org/10.1002/cctc.201402146.
(2) Wang, S.; Chen, Y.; Qin, Z.; Zhao, T.-S.; Fan, S.; Dong, M.; Li, J.; Fan, W.; Wang, J. J. Catal. 2019, 369, 382–395.
https://doi.org/10.1016/j.jcat.2018.11.018.
(3) Blaszkowski, S. R.; Van Santen, R. A. J. Phys. Chem. B 1997, 101 (13), 2292–2305.
https://doi.org/10.1021/jp962006+.
(4) Ghorbanpour, A.; Rimer, J. D.; Grabow, L. C. ACS Catal. 2016, 6 (4), 2287–2298.
https://doi.org/10.1021/acscatal.5b02367.
(5) Barducci, A.; Bussi, G.; Parrinello, M. Rev. Lett. 2008, 100 (2), 020603.
https://doi.org/10.1103/PhysRevLett.100.020603.
