Speaker
Description
Zeolites are traditionally used in high-temperature applications at low water vapor pressures, such as heterogeneous catalysis and gas separation, where the zeolite framework is generally considered to be stable and static. Increasingly, zeolites are being considered for applications under milder aqueous conditions, in emerging fields such as biomass conversion, low-temperature oxidation catalysis, and medicine. However, it has not yet been established how neutral liquid water at mild conditions affects the stability and reactivity of the zeolite framework at the atomistic level. Therefore, we carried out (biased) ab initio molecular dynamics (MD) calculations that predict novel, energetically viable reaction mechanisms by which Al-O, Si-O, Ge-O bonds rapidly and reversibly break [1]. Even at ambient conditions, zeolites show significant, fast lability of their bonds when in contact with liquid water. In addition, solvation of the extra-framework cations compensating the negative charge of the framework are solvated in the nanoporous channel system, influencing the catalytic activity of the material [2].
However, ab initio MD simulations are computationally too demanding for the statistically significant sampling of reaction mechanisms of larger systems at several different synthesis/operating conditions. While available empirical force fields allow such large scale sampling of the configuration space, their accuracy is too low to provide a reliable atomistic description of the water-zeolite interface. Therefore, we develop a computational tool providing the best compromise of accuracy and computational costs by combining state-of-the-art artificial neural networks and ab initio (MD) simulations. Such neural network potentials (NNP) are capable of modeling interatomic interactions with considerably reduced computational effort and retaining the accuracy of first-principles calculations [3]. Consequently, atomistic simulations employing NNP are expected to provide a deeper understanding of zeolite hydrolysis mechanisms at realistic conditions relevant for, e.g., the targeted synthesis of novel zeolites via the ADOR process [4].
[1] C. J. Heard, L. Grajciar, C. M. Rice, S. M. Pugh, P. Nachtigall, S. Ashbrook, and R. E. Morris, “Fast room temperature lability of aluminosilicate zeolites”, Nat. Comm., 2019, under review.
[2] C. J. Heard, L. Grajciar, and P. Nachtigall, “The effect of water on the validity of Lowenstein’s rule”, Chem. Sci., 2019, DOI: 10.1039/C9SC00725C.
[3] J. Behler, “First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems”, Angew. Chem. Int. Ed., 2017, 56, 12828-12840.
[4] W. J. Roth, P. Nachtigall, R. E. Morris, P. S. Wheatley, V. R. Seymour, S. E. Ashbrook, P. Chlubná, L. Grajciar, M. Položij, A. Zukal, O. Shvets, and J. Cejka, “A family of zeolites with controlled pore size prepared using a top-down method.”, Nat. Chem., 2013, 5, 628–633.
