Predicting the stability of crystalline materials is essential for developing advanced materials and pharmaceuticals. Researchers developed a new thermodynamic integration framework that calculates Gibbs free energies entirely under constant-pressure conditions, improving accuracy while simplifying the workflow. The method was validated using ice polymorphs and CsPbI₃ and relied on more than 10,000 GPU hours on VSC infrastructure.

Researchers at VUB developed a patient-relevant orthotopic lung cancer model and combined SEPARATE-Seq with spatial transcriptomics to map the immune landscape of lung tumours. Supported by VSC's high-performance computing infrastructure, the study provides new insights into immune cell behavior and offers a valuable resource for cancer immunology and future therapy development.

Researchers at Ghent University used large-scale simulations on the Flemish Supercomputer Center (VSC) to investigate how dangling bonds influence the electronic structure of III-V quantum dots. Their findings reveal how surface defects affect semiconductor performance and identify promising materials for next-generation infrared sensors, imagers, and optoelectronic devices. This research advances the design of safer, high-performance quantum dot technologies.

Researchers at Ghent University developed a patient-centered approach to detect cancer signals hidden in blood plasma using cell-free RNA (cfRNA). By combining RNA sequencing with large-scale computational analysis, they identified personalized molecular patterns that distinguish cancer patients from healthy individuals, advancing the future of precision oncology and personalized diagnostics.

Adaptive design of experiments improves model identification by selecting the most informative tests while reducing overall testing time. Demonstrated on gearbox power-loss modeling, the approach balances parameter accuracy with efficient test sequencing. Supported by VSC computing resources, it enables faster, reliable model development with fewer experiments.

Researchers at KU Leuven leveraged VSC Tier-1 supercomputing to train a large EEG foundation model on over 8 million segments, enabling robust brain signal decoding across tasks and subjects. The work underpins an ICLR 2026 study and achieved first place in the NeurIPS 2025 EEG Challenge, demonstrating the impact of large-scale AI and high-performance computing in neuroscience.

This study benchmarks single-cell chromatin accessibility technologies for training deep learning models in regulatory genomics. Using large-scale datasets from mouse and Drosophila, it shows that increasing dataset size can offset lower per-cell coverage. The optimized HyDrop v2 protocol enables cost-efficient, high-quality data generation, supporting robust sequence-to-function modeling powered by high-performance computing.

Brain activity measured with EEG offers a non-invasive way to study cognitive function. This research uses machine learning and brain connectivity patterns to detect Alzheimer’s disease and frontotemporal dementia, two conditions that are difficult to distinguish clinically. The approach improves diagnostic insights while highlighting the role of brain networks in neurodegenerative disorders.

Magnetic nanoparticles with a flower-like shape are among the most promising candidates for cancer heat therapy — but why do they perform so well? Using large-scale GPU simulations on the VSC and MeluXina supercomputers, researchers from Ghent University and the University of Luxembourg pinpointed a precise size "sweet spot" of 100–150 nm where spin disorder dramatically boosts heating performance.

Researchers developed the Generalized Van Cittert-Zernike Schell propagator, a new algorithm for simulating partially coherent light with unprecedented efficiency. The method achieves up to 100× faster computation and 1000× higher accuracy compared to existing approaches, enabling advanced optical simulations for holography, imaging, and photonics using VSC’s high-performance computing infrastructure.