Jim Boelrijk

I am a Data Scientist at Evonik, working on machine learning for chemistry and analytical workflows. My interests include Bayesian optimization, experimental automation, liquid chromatography method development, deep learning, and molecular modeling.

I completed my PhD in machine learning and chemistry at the University of Amsterdam in 2024. My research brought together the Amsterdam Machine Learning Lab, AI4Science, and the Chemometrics and Advanced Separations Team, under the supervision of Bernd Ensing, Bob Pirok, and Patrick Forré.

Selected Work

Shape-Aware Gaussian Process Kernels for Closed-Loop Bayesian Optimization of Gradients in Automated LC Method Development

Boelrijk, Jim, Detlefsen, Sebastian, Dürholt, Johannes P., and Haas, Christian P.

ChemRxiv preprint 2026

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Bayesian optimization (BO) is a promising strategy for accelerating gradient-elution liquid chromatography (LC) method development, but its practical success depends on several tightly coupled components. Here we report two main advances. First, for the Gaussian process (GP) surrogate models used within BO, we introduce a shape-aware gradient representation that compares gradient profiles directly through a Wasserstein-distance kernel, rather than through the pump-timetable coordinates used by standard representations. Second, we establish a robust automated acquisition and analysis workflow based on a trap phase that isolates gradient-driven elution and makes reliable closed-loop measurement possible. These are supported by three refinements: a flexible gradient domain with variable method duration and user-defined composition bounds, a window-relative sampler for monotonic gradient programs, and an adaptive reference point for multi-objective optimization. Across in silico and experimental datasets, Wasserstein-based representations generally predict separation quality more accurately than timetable-based approaches, especially when only a few training samples are available. This improved accuracy translates into more data-efficient single- and multi-objective optimization campaigns. Finally, we demonstrate the closed-loop framework in experimental case studies, where both single- and multi-objective campaigns improve upon a custom baseline method for a polymer-additive sample. Together, these results show that shape-aware gradient representations improve both GP predictive accuracy and BO sample efficiency, and that a flexible gradient domain, robust automated acquisition and analysis, and compatible sampling and optimization strategies enable effective closed-loop LC method development.
Enhancing LC×LC separations through multi-task Bayesian optimization

Boelrijk, Jim, Molenaar, Stef R.A., Bos, Tijmen S., Dahlseid, Tina A., Ensing, Bernd, Stoll, Dwight R., Forré, Patrick, and Pirok, Bob W.J.

Journal of Chromatography A 2024

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Method development in comprehensive two-dimensional liquid chromatography (LC×LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, make trial-and-error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues. However, both approaches have their strengths and weaknesses. On the one hand, retention modeling, which approximates true retention behavior, depends on effective peak tracking and accurate retention time and width predictions, which are increasingly challenging for complex samples and advanced gradient assemblies. On the other hand, Bayesian optimization may require many experiments when dealing with many adjustable parameters, as in LC×LC. Therefore, in this work, we investigate the use of multi-task Bayesian optimization (MTBO), a method that can combine information from both retention modeling and experimental measurements. The algorithm was first tested and compared with BO using a synthetic retention modeling test case, where it was shown that MTBO finds better optima with fewer method-development iterations than conventional BO. Next, the algorithm was tested on the optimization of a method for a pesticide sample and we found that the algorithm was able to improve upon the initial scanning experiments. Multi-task Bayesian optimization is a promising technique in situations where modeling retention is challenging, and the high number of adjustable parameters and/or limited optimization budget makes traditional Bayesian optimization impractical.
Multi-objective optimization via equivariant deep hypervolume approximation

Boelrijk, Jim, Ensing, Bernd, and Forré, Patrick

ICLR 2023

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Optimizing multiple competing objectives is a common problem across science and industry. The inherent inextricable trade-off between those objectives leads one to the task of exploring their Pareto front. A meaningful quantity for the purpose of the latter is the hypervolume indicator, which is used in Bayesian Optimization (BO) and Evolutionary Algorithms (EAs). However, the computational complexity for the calculation of the hypervolume scales unfavorably with increasing number of objectives and data points, which restricts its use in those common multi-objective optimization frameworks. To overcome these restrictions we propose to approximate the hypervolume function with a deep neural network, which we call DeepHV. For better sample efficiency and generalization, we exploit the fact that the hypervolume is scale-equivariant in each of the objectives as well as permutation invariant w.r.t. both the objectives and the samples, by using a deep neural network that is equivariant w.r.t. the combined group of scalings and permutations. We evaluate our method against exact, and approximate hypervolume methods in terms of accuracy, computation time, and generalization. We also apply and compare our methods to state-of-the-art multi-objective BO methods and EAs on a range of synthetic benchmark test cases. The results show that our methods are promising for such multi-objective optimization tasks.