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Description
Neural network potentials (NNPs) are becoming increasingly popular in multiple areas of material science and chemistry thanks to their ability to keep ab initio accuracy at the cost of the standard reactive force fields such as ReaxFF [1,2]. However, the vast majority of the studies focused on systems with rather low-dimensional configurational space. This apparent curse of dimensionality could be behind the fact that NNPs for microporous solids such as zeolites are, to the best of our knowledge, non-existent [1,2].
In this work, we present the development of linear-scaling, reactive NNPs using the SchNet architecture [3] for various zeolite classes by using robust training and data curation procedures. The resulting NNPs retain DFT accuracy across the complex zeolitic classes considered, outperforming specialized ReaxFF by order(s) of magnitude in accuracy, while speeding-up calculations by at least three orders of magnitude compared to DFT. Using the developed NNPs we have been witnessing intriguing results such as: i) large-scale simulations of zeolite databases (>330k hypothetical zeolites) revealing more than 20k additional hypothetical frameworks in the thermodynamically accessible range for zeolite synthesis [4]; ii) multi-nanosecond molecular dynamics (MD) simulations of reactive diffusion of sub-nanometer Pt clusters in zeolites showing intermittent bond breaking of the zeolite framework [5]; iii) effects of minor topology variations on the germanium distribution in germanosilicate zeolites with profound effects on their delamination propensity [6,7]; iv) MD simulations of aluminosilicate zeolites quantifying the effects of water loadings and Si/Al ratios on proton solvation and water diffusivity [8]. Lastly, our recent work [9] suggests that the learned NNP representations of atomic environments, a by-product of our NNP generation, can be reused to construct (using variational autoencoders) robust collective variables (CVs) that are "aware" of the underlying potential energy surfaces - such CVs will facilitate any subsequent biased MD simulations that generate realistic free energy surfaces of reactive processes in zeolitic systems. Therefore, the herein developed, reactive zeolite NNPs enable calculations of complex zeolitic frameworks under experimentally relevant - operando - conditions making them a new standard in the field.
References:
[1] N. Artrith, J. Phys. Energy 2019, 1, 032002.
[2] O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko, K.-R. Müller, Chem. Rev. 2021, 121, 10142–10186.
[3] K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, K.-R. Müller, J. Chem. Phys. 2018, 148, 241722.
[4] A. Erlebach, P. Nachtigall, L. Grajciar, npj Comput. Mater. 2022, 8, 1–12.
[5] D. Hou, L. Grajciar, P. Nachtigall, C. J. Heard, ACS Catal. 2020, 10, 11057–11068.
[6] M. Opanasenko, M. Shamzhy, Y. Wang, W. Yan, P. Nachtigall, J. Čejka, Angew. Chem. 2020, 132, 19548–19557.
[7] M. Jin, O. Veselý, C. J. Heard, M. Kubů, P. Nachtigall, J. Čejka, L. Grajciar, J. Phys. Chem. C 2021, 125, 23744–23757.
[8] I. Saha, A. Erlebach, P. Nachtigall, C. J. Heard, L. Grajciar, preprint: ChemRxiv. Cambridge: Cambridge Open Engage 2022, https://doi.org/10.26434/chemrxiv-2022-d1sj9.
[9] M. Šípka, A. Erlebach, L. Grajciar, J. Chem. Theory Comput. (under revision) 2022, preprint: http://arxiv.org/abs/2203.08097.
