I am a computational chemist with a PhD in Physics, working at the intersection of molecular simulation, machine learning, and scientific software. My work spans machine-learning interatomic potentials, reactive force-field development, high-throughput quantum chemistry, and large-scale atomistic modeling.
I currently work as a Research Scientist at the German Aerospace Center (DLR) in Cologne, where I explore quantum computing for chemistry and materials science. Before that, I developed machine-learning methods for ReaxFF parameterization at IFP Energies Nouvelles and completed my PhD at the Center for Molecular Modeling, Ghent University.
Machine-learned potentials
Building and validating machine-learning interatomic potentials for reactive chemistry, from training-data curation to production simulations.
Reactive force fields
Developing optimization workflows and loss functions that make ReaxFF parameterization more robust and easier to validate.
Quantum computing for chemistry
Exploring quantum-computing workflows for molecular and materials problems at the German Aerospace Center.
Scientific software & HPC
Writing maintainable Python, automating high-throughput workflows, and running large-scale simulations on HPC systems.
Selected publications
2025
Model-Informed Training Data Curation for Reactive All-Atom Potentials
BibTeX
@phdthesis{dumortier2025thesis,
author = {Loïc Dumortier},
title = {Model-Informed Training Data Curation for Reactive All-Atom Potentials},
school = {Ghent University},
year = {2025},
url = {https://biblio.ugent.be/publication/01K5X8ZHSY4J1BXBREV8S66FSG}
}
2024
Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: An Application to Water Adsorption on Alumina
BibTeX
@article{dumortier2024managing,
author = {Loïc Dumortier and Céline Chizallet and Benoit Creton and Theodorus de Bruin and Toon Verstraelen},
title = {Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: An Application to Water Adsorption on Alumina},
journal = {Journal of Chemical Theory and Computation},
year = {2024},
doi = {10.1021/acs.jctc.3c01009},
eprint = {2404.14338},
archivePrefix = {arXiv}
}
2020
From Ionic Surfactants to Nafion through Convolutional Neural Networks
BibTeX
@article{dumortier2020ionic,
author = {Loïc Dumortier and Stefano Mossa},
title = {From Ionic Surfactants to Nafion through Convolutional Neural Networks},
journal = {The Journal of Physical Chemistry B},
year = {2020},
doi = {10.1021/acs.jpcb.0c06172}
}
Get in touch
I am always happy to discuss computational chemistry, machine learning for molecular simulation, or scientific software. The best way to reach me is by email or LinkedIn.