Andreas Erlebach
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Water Adsorption at Pairs of Proximate Brønsted Acid Sites in Zeolites Open
We model water adsorption at pairs of proximate Brønsted acid sites (BASs) in zeolites H-MFI, H-FAU, and H-CHA. We use machine-learning potentials to explore the potential energy surface, combined with quantum mechanical methods for chemic…
View article: Mobility and Sintering of Silica-Supported Platinum Clusters via Reactive Neural Network Potentials
Mobility and Sintering of Silica-Supported Platinum Clusters via Reactive Neural Network Potentials Open
Supported Pt atoms and their sub-nanometre cluster counterparts are a promising avenue for the targeted, bottom-up development of high specific activity heterogeneous catalysts. However, they suffer from instability with respect to growth …
Aluminum Siting in Zeolite RTH From a Combined Machine Learning - NMR Approach Open
Determining the distribution of aluminum in zeolite frameworks remains a significant challenge, due to the limited sensitivity of conventional characterization techniques and the complexity of possible Al configurations. In this work, we h…
<sup>27</sup> Al NMR chemical shifts in zeolite MFI <i>via</i> machine learning acceleration of structure sampling and shift prediction Open
Accurate prediction of 27 Al NMR chemical shifts in zeolites at operating conditions via a combination of neural network potential-driven dynamics sampling relevant structures and regression models for shift prediction.
27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction Open
Zeolites, such as MFI, are versatile microporous aluminosilicate materials that are widely used in catalysis and adsorption processes. The location of the aluminium within the zeolite framework is one of the important determinants of perfo…
Germanium Distributions in Zeolites Derived from Neural Network Potentials. Open
Germanosilicate zeolites have played a pivotal role in the recent surge in synthesis of novel zeolite topologies. This success has been attributed to the combined effect of the high hydrolytic lability and specific distribution of germaniu…
A machine learning approach for dynamical modelling of Al distributions in zeolites <i>via</i><sup>23</sup>Na/<sup>27</sup>Al solid-state NMR Open
A machine-learning approach for simulating Na/Al solid-state NMR spectra in zeolites was developed. Improved sampling provided insight into Al distributions and highlighted the importance of dynamical effects.
Germanium distributions in zeolites derived from neural network potentials Open
This work uses newly developed machine learning potentials to predict how germanium distributes within the zeolite catalysts, depending on both germanium content and the framework topology, aiding the rational zeolite design.
Migration of zeolite-encapsulated subnanometre platinum clusters <i>via</i> reactive neural network potentials Open
Pt atoms and small clusters move through the zeolite framework via distinct mechanisms, leading to complex size-dependent diffusivity.
Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials Open
Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International): Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz …
Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials Open
Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International): Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz …
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites Open
Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International): This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Pot…
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites Open
Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Pote…
Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials Open
The migration of atoms and small clusters is an important process in sub-nanometre scale heterogeneous catalysis, affecting activity, accessibility and deactivation through sintering. Control of migration can be partially achieved via enca…
Supplementary data (CC BY 4.0): A reactive neural network framework for water-loaded acidic zeolites Open
Content (Creative Commons Attribution 4.0 International):\n\n\n\n\t\nEnergy and force data (ASE trajectory files) calculated at the DFT (SCAN+D3(BJ)), NNP and ReaxFF level for error statistics of the NNP generalization tests\n\t\nTrajector…
Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential. Open
Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first pri…
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites Open
Content (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International):\n\n\n\n\t\nNeural Network Potential (NNP) files (SchNet architecture, compatible with SchnetPack 1.0) trained on SCAN+D3(BJ) data\n\t\nNNP files of the \\(\…
Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU Open
Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first pri…
The need for operando modelling of 27Al NMR in zeolites Open
Solid state (ss-) 27Al NMR is one of the most valuable tools for experimental characterization of zeolites, owing to its high sensitivity and the detailed structural information which can be extracted from the spectra. Unfortunately, the i…
Supporting datasets for "The need for Operando Modelling of 27Al NMR in Zeolites" Open
tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including MOR and CHA in H and Na form and various wate…
Supporting datasets for "The need for Operando Modelling of 27Al NMR in Zeolites" Open
tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including MOR and CHA in H and Na form and various wate…
Constructing Collective Variables Using Invariant Learned Representations Open
On the time scales accessible to atomistic numerical modeling, chemical reactions are considered rare events. Therefore, the atomistic simulations are commonly biased along a low-dimensional representation of a chemical reaction in an atom…
Correction: The need for <i>operando</i> modelling of <sup>27</sup>Al NMR in zeolites: the effect of temperature, topology and water Open
Correction for ‘The need for operando modelling of 27 Al NMR in zeolites: the effect of temperature, topology and water’ by Chen Lei et al. , Chem. Sci. , 2023, 14 , 9101–9113, https://doi.org/10.1039/D3SC02492J.
The need for <i>operando</i> modelling of <sup>27</sup> Al NMR in zeolites: the effect of temperature, topology and water Open
Operando modelling of 27 Al NMR in zeolites, showing the importance of hydration and dynamics in reproducing experimental data. Machine learning analysis obtains a simple correlation of chemical shielding which predicts chemical shifts acc…
Reactive Neural Network Potential for Aluminosilicate Zeolites and Water: Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU Open
Acidic zeolites are one of the most important catalysts. In many of their catalytic applications, the mode of interaction with water heavily influences their activity, efficiency, and durability as a catalyst. Despite the recent (first pri…
Understanding chemical reactions via variational autoencoder and atomic representations Open
On the time scales accessible to atomistic numerical modelling, chemical reactions are considered rare events. Atomistic simulations are typically biased along a low-dimensional representation of a chemical reaction in an atomic structure …
Supplementary data: Accurate large-scale simulations of siliceous zeolites by neural network potentials Open
Content 1. Zeolite databases Deem database containing 331170 hypothetical zeolite frameworks [Deem09, Pophale11] geometrically optimized at the NNPscan level (note, the first row of the database is alpha-quartz): "DEEM_NNPscan.db" Database…