Research Explorer

Your browser does not support JavaScript or JavaScript is not enabled. Without JavaScript some functions of this webapplication may be disabled or cause error messages. To enable JavaScript, please consult the manual of your browser or contact your system administrator.

Researcher

Sander Vandenhaute

Publications & Research Data

Awards & Distinctions

10 Results

2025

Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale

Pieter DobbelaereSander VandenhauteVeronique Van Speybroeck

A1 Journal Article in CHEMISTRY OF MATERIALS

The operando nature of isobutene adsorbed in Zeolite H‐SSZ‐13 unraveled by machine learning potentials beyond DFT accuracy

Massimo BocusSander VandenhauteVeronique Van Speybroeck

A1 Journal Article in ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

2024

Water motifs in zirconium metal-organic frameworks induced by nanoconfinement and hydrophilic adsorption sites

Aran LamaireJelle WiemeSander VandenhauteRuben GoeminneSven RoggeVeronique Van Speybroeck

A1 Journal Article in NATURE COMMUNICATIONS

2023

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

Sander VandenhauteMaarten Cools-CeuppensSimon DeKeyserToon VerstraelenVeronique Van Speybroeck

A1 Journal Article in NPJ COMPUTATIONAL MATERIALS

Nuclear quantum effects in proton transfer reactions

Aran LamaireMassimo BocusRuben GoeminneSander VandenhauteMaarten Cools-CeuppensToon VerstraelenVeronique Van Speybroeck

2022

Accurately determining the phase transition temperature of CsPbI3 via random-phase approximation calculations and phase-transferable machine learning potentials

Tom BraeckeveltRuben GoeminneSander VandenhauteSander BorgmansToon VerstraelenJulian A. SteeleMaarten B. J. RoeffaersJohan HofkensSven RoggeVeronique Van Speybroeck

A1 Journal Article in CHEMISTRY OF MATERIALS

Accurately determining the transition temperature of metal halide perovskites via RPA calculations and phase-transferable machine learning potentials

Tom BraeckeveltRuben GoeminneSander VandenhauteSander BorgmansToon VerstraelenJulian A. SteeleMaarten B. J. RoeffaersJohan HofkensSven RoggeVeronique Van Speybroeck

Machine learning potentials for metal-organic frameworks with thermodynamic transferability

Sander VandenhauteVeronique Van SpeybroeckToon VerstraelenMaarten Cools-Ceuppens

2021

Large-scale molecular dynamics simulations reveal new insights into the phase transition mechanisms in MIL-53(Al)

Sander VandenhauteSven RoggeVeronique Van Speybroeck

A1 Journal Article in FRONTIERS IN CHEMISTRY

Towards modeling spatiotemporal processes in metal–organic frameworks

Veronique Van SpeybroeckSander VandenhauteAlexander HoffmanSven Rogge

A1 Journal Article in TRENDS IN CHEMISTRY