Loïc Dumortier

Computational chemist and physicist at the German Aerospace Center (DLR), working on machine learning for molecular simulation, reactive force fields, and quantum computing in chemistry.

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Profile

Computational chemist with a PhD in Physics, specialized in machine-learning interatomic potentials, reactive force-field development, high-throughput quantum chemistry, and large-scale atomistic modeling. Combines an analytical chemistry foundation with physics-level rigor.

Experience

Research Scientist · German Aerospace Center (DLR)

Cologne, Germany · 2025 – Present

  • Led and delivered cross-functional computational chemistry and materials science projects in collaboration with industry partners.
  • Engineered robust data pipelines using state-of-the-art molecular modeling methods for high-throughput surface detection, resulting in the identification and characterization of unknown stable high-index surfaces.

Doctoral Researcher · Center for Molecular Modeling, Ghent University

Ghent, Belgium · 2023 – 2025

  • Developed scalable empirical reactive force fields and machine-learning potentials through novel optimization frameworks, successfully modeling alumina and hydrated silicate ionic liquids with a 5–10× simulation speed-up.

Research Engineer · IFP Energies Nouvelles

Rueil-Malmaison, France · 2020 – 2023

  • Optimized ReaxFF reactive force-field parameters through the development and deployment of machine-learning algorithms, enabling accurate modeling of complex alumina systems in a catalytic context.
  • Board member of the doctoral association; organized network events, study trips, and PhD onboarding.

Research Intern · Institut Laue-Langevin & European Synchrotron Radiation Facility

Grenoble, France · 2019

  • Advanced soft-matter studies by identifying water contents from molecular dynamics simulations through 3D imaging and machine-learning approaches, resulting in a first-author publication.

Education

PhD in Physics · Ghent University

Ghent, Belgium · 2020 – 2025

Doctoral Training Program · IFP School

Rueil-Malmaison, France · 2020 – 2023

  • Coursework focused on energy economics, refining processes, and petroleum products.

MSc in Analytical Chemistry · Ghent University

Ghent, Belgium · 2014 – 2020

  • Graduated Summa Cum Laude.
  • Thesis: Development of advanced 2D/3D data analysis methods for on-the-fly scanning X-ray fluorescence experiments.
  • Extracurricular: Student Representative, Faculty Representative, and Chemistry PR Representative.

Technical skills

  • Core competencies: Machine-learning potentials, force-field development, HPC, molecular dynamics, DFT, materials science, X-ray fluorescence, X-ray diffraction, chemometrics.
  • Programming and tools: Python, Git, Unix, Docker, OpenMM, VASP, FHI-aims, LAMMPS, pymatgen, ASE, MACE, PyTorch, NumPy, Pandas, scikit-learn.
  • Languages: Dutch (native), English (C1), French (C1), German (A2, learning).

Grants and awards

Tier-1 Compute Grant · Flemish Supercomputing Center

2023

  • Secured and managed 2.25 million CPU hours and 27,000 GPU hours for large-scale computational research.

First Place · ParAMS Challenge, Software for Chemistry and Materials

2022

  • Recognized for innovations in molecular simulation and force-field optimization.

Publications

See the publications page for the full list, including my PhD thesis.

Nationality

Belgian and French citizen.