Sander Vandenhaute

Materials Science · Physical Sciences

9h-index374citations26works3.12yr avg
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Research Topics

Machine Learning in Materials Science(17), Metal-Organic Frameworks: Synthesis and Applications(10), Perovskite Materials and Applications(5), X-ray Diffraction in Crystallography(5), Zeolite Catalysis and Synthesis(2)

Publications26 total

A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids
arXiv (Cornell University)·2026Open Access
A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids
ArXiv.org·2026Open Access
The effect of the A-site cation on the phase transition temperature of metal halide perovskites
arXiv (Cornell University)·2026Open Access
The effect of the A-site cation on the phase transition temperature of metal halide perovskites
ArXiv.org·2026Open Access
Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Cluster-Based Machine Learning Potentials to Describe Disordered Metal–Organic Frameworks up to the Mesoscale
Chemistry of Materials·2025· 3 citedOpen Access
Orb-v3: atomistic simulation at scale
ArXiv.org·2025· 11 citedOpen Access
Cluster-based machine learning potentials to describe disordered metal-organic frameworks up to the mesoscale
arXiv (Cornell University)·2025Open Access
Accelerating molecular simulation using machine learning : from wave functions to thermodynamics
Ghent University Academic Bibliography (Ghent University)·2025
Water motifs in zirconium metal-organic frameworks induced by nanoconfinement and hydrophilic adsorption sites
Nature Communications·2024· 17 citedOpen Access
The Operando Nature of Isobutene Adsorbed in Zeolite H−SSZ−13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy
Angewandte Chemie International Edition·2024· 14 citedOpen Access
The Operando Nature of Isobutene Adsorbed in Zeolite H−SSZ−13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy
Angewandte Chemie·2024Open Access
Unraveling the nature of adsorbed isobutene in H-SSZ-13 with operando simulations at the top of Jacob’s ladder
2024
Rare Event Sampling using Smooth Basin Classification
arXiv (Cornell University)·2024· 2 citedOpen Access
Machine learning potentials for metal-organic frameworks using an incremental learning approach
npj Computational Materials·2023· 119 citedOpen Access
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
2023· 1 citedOpen Access
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach
2022· 9 citedOpen Access
Accurately Determining the Phase Transition Temperature of CsPbI<sub>3</sub> via Random-Phase Approximation Calculations and Phase-Transferable Machine Learning Potentials
Chemistry of Materials·2022· 23 citedOpen Access
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Frequent Co-authors

Véronique Van Speybroeck(22), Toon Verstraelen(7), Maarten Cools‐Ceuppens(6), Tom Braeckevelt(6), Massimo Bocus(6), Pieter Dobbelaere(5), Simon DeKeyser(4), Sven M. J. Rogge(4), Johan Hofkens(3), Liève Brochez(2), Ilse Mollet(2), Jo Lambert(2), Ruben Goeminne(2), Karel L. K. De Witte(2), Sven M. J. Rogge(2), Alexander E. J. Hoffman(1), N. van Geel(1), Reinhart Speeckaert(1), Luc De Cooman(1), Nanja van Geel(1)