Ghent University Academic Bibliography

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Materials for a changing planet

Veronique Van Speybroeck (UGent) and Guillaume Maurin

(2023) NATURE MATERIALS. 22(1). p.12-13
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Machine learning potentials for metal-organic frameworks using an incremental learning approach

Sander Vandenhaute (UGent) , Maarten Cools-Ceuppens (UGent) , Simon DeKeyser, Toon Verstraelen (UGent) and Veronique Van Speybroeck (UGent)

(2023) NPJ COMPUTATIONAL MATERIALS. 9(1).
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Quantum free energy profiles for molecular proton transfers

Aran Lamaire (UGent) , Maarten Cools-Ceuppens (UGent) , Massimo Bocus (UGent) , Toon Verstraelen (UGent) and Veronique Van Speybroeck (UGent)

(2023) JOURNAL OF CHEMICAL THEORY AND COMPUTATION. 19(1). p.18-24
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Exploring the phase stability in interpenetrated diamondoid covalent organic frameworks

Sander Borgmans (UGent) , Sven Rogge (UGent) , Juul De Vos (UGent) , Pascal Van Der Voort (UGent) and Veronique Van Speybroeck (UGent)

(2023) COMMUNICATIONS CHEMISTRY. 6(1).
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A recyclable rhodium catalyst anchored onto a bipyridine covalent triazine framework for transfer hydrogenation of N-heteroarenes in water

Jonas Everaert (UGent) , Karen Leus (UGent) , Hannes Rijckaert (UGent) , Maarten Debruyne (UGent) , Kristof Van Hecke (UGent) , Rino Morent (UGent) , Nathalie De Geyter (UGent) , Veronique Van Speybroeck (UGent) , Pascal Van Der Voort (UGent) and Christian Stevens (UGent)

(2023) GREEN CHEMISTRY. 25(8). p.3267-3277
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Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics

Massimo Bocus (UGent) , Ruben Goeminne (UGent) , Aran Lamaire (UGent) , Maarten Cools-Ceuppens (UGent) , Toon Verstraelen (UGent) and Veronique Van Speybroeck (UGent)

(2023) NATURE COMMUNICATIONS. 14(1).
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Microscopic linker distribution in mixed-linker zeolitic imidazolate frameworks via computational raman spectroscopy : implications for gas separation

Alexander Hoffman (UGent) , João Marreiros, Sven Rogge (UGent) , Rob Ameloot and Veronique Van Speybroeck (UGent)

(2023) ACS APPLIED NANO MATERIALS. 6(7). p.5645-5652
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Understanding the phase transition mechanism in the lead halide perovskite CsPbBr3 via theoretical and experimental GIWAXS and Raman spectroscopy

Alexander Hoffman (UGent) , Rafikul Ali Saha, Sander Borgmans (UGent) , Pascal Puech, Tom Braeckevelt (UGent) , Maarten B. J. Roeffaers, Julian A. A. Steele, Johan Hofkens and Veronique Van Speybroeck (UGent)

(2023) APL MATERIALS. 11(4).
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Universal descriptors for zeolite topology and acidity to predict the stability of butene cracking intermediates

Pieter Cnudde (UGent) , Michel Waroquier (UGent) and Veronique Van Speybroeck (UGent)

(2023) CATALYSIS SCIENCE & TECHNOLOGY. 13(16). p.4857-4872
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DFT-quality adsorption simulations in metal–organic frameworks enabled by machine learning potentials

Ruben Goeminne (UGent) , Louis Vanduyfhuys (UGent) , Veronique Van Speybroeck (UGent) and Toon Verstraelen (UGent)

(2023) JOURNAL OF CHEMICAL THEORY AND COMPUTATION.

Academic Bibliography

Materials for a changing planet

Machine learning potentials for metal-organic frameworks using an incremental learning approach

Quantum free energy profiles for molecular proton transfers

Exploring the phase stability in interpenetrated diamondoid covalent organic frameworks

A recyclable rhodium catalyst anchored onto a bipyridine covalent triazine framework for transfer hydrogenation of N-heteroarenes in water

Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics

Microscopic linker distribution in mixed-linker zeolitic imidazolate frameworks via computational raman spectroscopy : implications for gas separation

Understanding the phase transition mechanism in the lead halide perovskite CsPbBr3 via theoretical and experimental GIWAXS and Raman spectroscopy

Universal descriptors for zeolite topology and acidity to predict the stability of butene cracking intermediates

DFT-quality adsorption simulations in metal–organic frameworks enabled by machine learning potentials

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