Please refer to my personal page for more information.
Sander Vandenhaute
PhD-FWO Fellow (EA17) Tech Lane Ghent Science Park, Campus Ardoyen Technologiepark 46, 9052 Zwijnaarde, Belgium Office n°021 |
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Published
2023
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach , , npj Computational Materials , 9, 1, 19 , 2023
2022
Accurately Determining the Phase Transition Temperature of CsPbI3 via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials , , Chemistry of Materials , 34, 19, 8561–8576 , 2022 , IF: 8.6 , 63/342 [Q1]
2021
Large-Scale Molecular Dynamics Simulations Reveal New Insights Into the Phase Transition Mechanisms in MIL-53(Al) , , Frontiers in Chemistry , 9, 718920 , 2021 , IF: 5.545 , 59/179 [Q2]
Towards modeling spatiotemporal processes in metal–organic frameworks , , Trends in Chemistry , 3 (8): 605-619 , 2021 , IF: 22.448 , 9/179 [Q1]
Dissertations
(D2) , Constructing accurate coarse-grained force fields by variational minimization: The subtle balance between accuracy and sparseness , , Supervisor(s): Prof. Dr. ir. Veronique Van Speybroeck, Prof. Dr. ir. Toon Verstraelen , 2018
2022 - 2024
Invited talk
Towards modeling spatiotemporal processesin metal-organic frameworks , , MOF2022 , Dresden, Germany , Sun, 04/09/2022 to Wed, 07/09/2022 2018
Invited talk
Towards modeling long-range disorder in MOFs: Development of a computational toolbox to extend the length scale in molecular simulations , , CECAM workshop: Multi-scale modelling of flexible and disordered porous materials , Paris, France , Mon, 11/06/2018 to Wed, 13/06/2018 2022 - 2024
Nuclear quantum effects in proton transfer reactions , , Quantum2 on machine learning enhanced sampling , Lausanne, Switzerland , Wed, 29/11/2023 to Fri, 01/12/2023
Machine Learning Potentials for Accurate Simulations of Zeolite-Catalyzed Reactions , , XLIX Congress of the Physical Chemistry Division of the Società Chimica Italiana , Torino, Italy , Mon, 04/09/2023 to Thu, 07/09/2023
Accurately determining the transition temperature of metal halide perovskites via RPA calculations and phase-transferable machine learning potentials , , DFT2022 , Brussels, Belgium , Mon, 29/08/2022 to Fri, 02/09/2022
2019
Reliably modeling the mechanical stability of MOFs at increasing length scales to unleash their full industrial potential , , MMC , Oxford, United Kingdom , Tue, 04/06/2019
2018
Reliably modeling the mechanical stability of MOFs at increasing length scales to unleash their full industrial potential , , MOF2018 , Auckland, New Zealand , Sun, 09/12/2018 to Thu, 13/12/2018