New Finnish LUMI projects announced: computational pathology, dark matter, light-matter interaction and more

Sixteen new Finnish research projects have been chosen in the seventh Finnish LUMI Extreme Scale call, sharing resources from Finland’s country share of the LUMI resources. The projects span various scientific fields, from physical and space sciences to information sciences, and linguistics. In the selected projects, the researchers will be, for example, improve cancer diagnosis by using AI to uncover hidden patterns in large pathology datasets, perform simulations to reveal dark matter’s nature by precisely predicting axion properties, and explain cavity-induced changes in molecular chemical reactivity mechanisms via mesoscale simulations.

LUMI’s resources were in high demand: there was a roughly 1.5-fold oversubscription for LUMI-G and LUMI-C resources in this call. The selected projects went through a rigorous technical and scientific review.

As this was the only Finnish LUMI Extreme Scale call of the year, we encourage Finnish researchers to make use of the EuroHPC JU access calls for any larger future needs until the next Finnish extreme scale call – likely to take place in early 2027.

Examples of the accepted projects below and full list available here.

ComPatAI: AI-Enabled Computational Pathology for Massive Routine Diagnostic Dataset

PI: Pekka Ruusuvuori, University of Turku, co-PIs: Leena Latonen, University of Eastern Finland, and Artturi Mäkinen, FIMLAB laboratories & Tampere University Hospital

With the digitalization of pathology, exceptionally large and high-quality datasets are being accumulated. Population aging and the increasing incidence of cancer are expanding the need for analysis, creating pressure to develop new, scalable, and more precise analytical methods. Artificial intelligence, particularly deep learning, offers the possibility of identifying biological features and associations in tissue samples that are not detectable by the human eye. The ComPatAI project utilizes infrastructures for digital pathology and high-performance computing, leveraging digital data generated by healthcare for the development of diagnostic decision support tools and novel computational pathology methods for virtual staining. We build foundation AI models using a population level cohort of up to 1M whole slide images from routine diagnostics from Finnish university hospital.

CavChemOTF: On-the-fly mesoscale simulation of cavity modified chemical dynamics

PI: Gerrit Groenhof , University of Jyväskylä, co-PI: Arkajit Mandal, Texas A&M University

Over the past decade, experiments have reported dramatic changes in ground-state chemical reactivity when placing molecules inside an optical cavity. Specifically, it is observed that when confined radiation inside optical cavities couples strongly to the molecular vibrations, various ground-state chemical reactivities are modified even in the absence of external fields. When fully realized, this phenomenon has the potential to unlock a new paradigm of chemical transformation. However, despite extensive theoretical efforts, the microscopic understanding of this remarkable phenomenon remains elusive. The key shortcoming of prior theoretical efforts is the use of simplified model systems that do not capture the molecular complexity, the mesoscale nature of the cavity-coupled molecular systems, and the multimode nature of the confined radiation modes. Using on-the-fly mesoscale simulations, we will address these drawbacks and provide a microscopic understanding of this phenomenon.

DMAXX: Dark matter axion mass from extreme scale simulations

PI: Mark Hindmarsh, University of Helsinki, co-PI: Kari Rummukainen, University of Helsinki

Most of the matter in the Universe consists of an unknown particle which so far has eluded detection. A leading candidate for this “dark” matter is the axion. Experimental searches for the axion require accurate predictions of its mass to achieve sufficient sensitivity, which in turn requires accurate calculations of the density of axions in the universe today. Such calculations require large-scale numerical simulations of the extended string-like structures which produce the axions up to around a millisecond after the Big Bang.

To perform these simulations, we have developed a massively parallel code AXHILA, which uses the parallel framework HILA to run on multiple parallel systems, included GPUs. It has already run successfully on LUMI-G to produce the world’s largest fixed-grid axion string simulations. With new Extreme Scale access and upgrades to the code we can run faster in more than twice the volume, reducing the statistical and systematic uncertainties on our mass prediction.