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Journal Articles by A. Erlebach

A. Erlebach, M. Šípka, I. Saha, P. Nachtigall, C. J. Heard, and L. Grajciar,
A reactive neural network framework for water-loaded acidic zeolites

Nature Communications, 15(1), 2024

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C. Lei, C. Bornes, O. Bengtsson, A. Erlebach, B. Slater, L. Grajciar, and C. J. Heard,
A machine learning approach for dynamical modelling of Al distributions in zeolites via23Na/27Al solid-state NMR

Faraday Discussions, 2024

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I. Saha, A. Erlebach, P. Nachtigall, C. J. Heard, and L. Grajciar,
Germanium distributions in zeolites derived from neural network potentials

Catalysis Science & Technology, 2024

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C. J. Heard, L. Grajciar, and A. Erlebach,
Migration of zeolite-encapsulated subnanometre platinum clusters via reactive neural network potentials

Nanoscale, 16(16), 2024

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D. Willimetz, A. Erlebach, C. J. Heard, and L. Grajciar,
27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction.

Digital Discovery, 2024

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C. Lei, A. Erlebach, F. Brivio, L. Grajciar, Z. Tošner, C. J. Heard, and P. Nachtigall,
The need for operando modelling of 27Al NMR in zeolites: the effect of temperature, topology and water

Chem. Sci., 14, 2023

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C. Lei, A. Erlebach, F. Brivio, L. Grajciar, Z. Tosner, C. J. Heard, and P. Nachtigall,
The need for operando modelling of 27Al NMR in zeolites: the effect of temperature, topology and water

Chem. Sci., 14, 2023

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M. Sipka, A. Erlebach, and L. Grajciar,
Constructing Collective Variables Using Invariant Learned Representations

Journal of Chemical Theory and Computation (PMID: 36696574), 19(3), 2023

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A. Erlebach, P. Nachtigall, and L. Grajciar,
Accurate large-scale simulations of siliceous zeolites by neural network potentials

npj Computational Materials, 8(1), 2022

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