Jim Boelrijk


I am a Phd student at the Amsterdam Machine Learning Lab (AMLab) in the AI4Science initiative. I collaborate with the Chemometrics and Advanced Separations Team (CAST) and the Computational Chemistry group at the Van ‘t Hoff institute for Molecular Sciences (HIMS).

Under supervision of Bernd Ensing, Bob Pirok and Patrick Forré, I am currently working on closed-loop automated method development in liquid chromatography, applying both phyiscal models and machine learning. My main research interests are Bayesian optimization, deep learning, molecular modeling, multi-objective optimization and general application of machine learning to problems in chemistry.

Selected Work

  1. Multi-objective optimization via equivariant deep hypervolume approximation
    Boelrijk, Jim, Ensing, Bernd, and Forré, Patrick
    ICLR 2023
  2. Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization
    Boelrijk, Jim, Ensing, Bernd, Forré, Patrick, and Pirok, Bob
    Analytica Chimica Acta 2022
  3. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography
    Bos, Tijmen,  Boelrijk, Jim, Molenaar, Stef, Veer, Brian, Niezen, Leon, Herwerden, Denice, Samanipour, Saer, Stoll, Dwight, Forré, Patrick, Ensing, Bernd, Somsen, Govert, and Pirok, Bob
    Analytical Chemistry 2022
  4. Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data
    Boelrijk, Jim, Herwerden, Denice, Samanipour, Saer, Ensing, Bernd, and Forré, Patrick
    Preprint, submitted to Journal of Cheminformatics 2022
  5. Bayesian optimization of comprehensive two-dimensional liquid chromatography separations
    Boelrijk, Jim, Pirok, Bob, Ensing, Bernd, and Forré, Patrick
    Journal of Chromatography A 2021
  6. Incorporating maximally localized Wannier functions into neural network potentials [Master thesis, supervised by Ambuj Tiwari]
    Boelrijk, Jim