Maarten Cools-Ceuppens
PhD-FWO Fellow (WE05) +32 (0)9 264 65 61 Tech Lane Ghent Science Park, Campus Ardoyen Technologiepark 46, 9052 Zwijnaarde, Belgium Office n°023  |
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Published
2022
Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential , , Journal of Chemical Theory and Computation (JCTC) , 18 (3), 1672–1691 , 2022 , IF: 6.006 , 5/37 [Q1]
2021
IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files , , Journal of Computational Chemistry , 45, 6, 458--464 , 2021 , IF: 2.976 , 78/177 [Q2]
Dissertations
(D2) , Uncertainty prediction in molecular simulations using ab initio derived force fields , , Supervisor(s): Prof. Dr. ir. Toon Verstraelen , 2017
2021
The eMLP: a novel machine learning potential to model electronic properties with explicit-electrons , , AutoCheMo International Reactive Force Field Workshop , Ghent, Belgium , wo, 08/12/2021 t/m do, 09/12/2021
2018
Evaluating a linear machine learning force field for aluminium , , L'intelligence artificielle pour la chimie des matériaux , Paris, France , di, 25/09/2018
2019
Comparing Different Machine Learning Force Fields: A Case Study of Aluminium , , MOFSIM 2019 , Ghent, Belgium , wo, 10/04/2019 t/m vr, 12/04/2019
2018
Machine learning and materials science: ab initio screening to microstructure analysis , , 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 , , The 34th Winter School in Theoretical Chemistry: Machine Learning , Helsinki, Finland , ma, 10/12/2018 t/m do, 13/12/2018