Daniel Willimetz
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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…
Ontological representation of experiments in electrochemistry and synthetic chemistry Open
Presentation given at the Ontologies4Chem Workshop 2025.Abstract: This talk presents a high-level abstraction model designed for the ontological representation of chemical experiments. Using two distinct and complex use cases, high-through…
Ontological representation of experiments in electrochemistry and synthetic chemistry Open
Presentation given at the Ontologies4Chem Workshop 2025.Abstract: This talk presents a high-level abstraction model designed for the ontological representation of chemical experiments. Using two distinct and complex use cases, high-through…
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning Open
Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-…
CRISP: Enhancing ASE Workflows with Advanced Molecular Simulation Post-Processing Open
Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customisation, interactivity, and efficiency with large datasets. To address this, we developed CRISP (…
CRISP: Enhancing ASE Workflows with Advanced Molecular Simulation Post-Processing Open
Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customisation, interactivity, and efficiency with large datasets. To address this, we developed CRISP (…
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning Open
Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-…
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…