Maarten Cools-Ceuppens

Voluntary staff
+32 (0)9 264 65 61
​​​​​Tech Lane Ghent Science Park, Campus Ardoyen
Technologiepark 46, 9052 Zwijnaarde, Belgium
Office n°023

A1 Publications



Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential , M. Cools-Ceuppens, J. Dambre, T. Verstraelen , Journal of Chemical Theory and Computation (JCTC) , 18 (3), 1672–1691 , 2022 , IF: 6.006 , 5/37 [Q1]


IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files , T. Verstraelen, W. Adams, L. Pujal, A. Teherani, B. D. Kelly, L. Macaya, F. Meng, M. Richer, R. Hernández-Esparza, X. D. Yang, M. Chan, T. D. Kim, M. Cools-Ceuppens, V. Chuiko, E. Vohringer-Martinez, P.W. Ayers, F. Heidar-Zadeh , Journal of Computational Chemistry , 45, 6, 458--464 , 2021 , IF: 3.672 , 88/179 [Q2]

Other Publications


(D1) , Incorporating long-range interactions and polarization in machine learning potentials with explicit electrons , M. Cools-Ceuppens , Supervisor(s): prof. dr. ir. T. Verstraelen; prof. dr. ir. J. Dambre , 15/09/2022
(D2) , Uncertainty prediction in molecular simulations using ab initio derived force fields , M. Cools-Ceuppens , Supervisor(s): Prof. Dr. ir. Toon Verstraelen , 2017

Keynote / Plenary / Invited talks



The influence of nuclear quantum effects on proton hopping kinetics in the H-SSZ-13 zeolite through ab initio derived machine learning potentials , M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen, V. Van Speybroeck , NCCC XXIII , Noordwijkerhout,The Netherlands , ma, 09/05/2022 t/m wo, 11/05/2022


The eMLP: a novel machine learning potential to model electronic properties with explicit-electrons , M. Cools-Ceuppens, J. Dambre, T. Verstraelen , AutoCheMo International Reactive Force Field Workshop , Ghent, Belgium , wo, 08/12/2021 t/m do, 09/12/2021


Evaluating a linear machine learning force field for aluminium , M. Cools-Ceuppens, T. Verstraelen , L'intelligence artificielle pour la chimie des matériaux , Paris, France , di, 25/09/2018



Comparing Different Machine Learning Force Fields: A Case Study of Aluminium , M. Cools-Ceuppens, J. Dambre, T. Verstraelen , MOFSIM 2019 , Ghent, Belgium , wo, 10/04/2019 t/m vr, 12/04/2019


Machine learning and materials science: ab initio screening to microstructure analysis , M. Larmuseau, M. Cools-Ceuppens, M. Sluydts, T. Verstraelen, S. Cottenier , 34th winter school in theoretical chemistry: Machine Learning in Theoretical Chemistry , Helsinki, Finland , ma, 10/12/2018 t/m do, 13/12/2018
Comparing different machine learning force fields: a case study of aluminium , M. Cools-Ceuppens, J. Dambre, T. Verstraelen , The 34th Winter School in Theoretical Chemistry: Machine Learning , Helsinki, Finland , ma, 10/12/2018 t/m do, 13/12/2018