Required cookies

This website uses cookies necessary for its operation in order to provide the user with content and certain functionalities (e.g. language selection). You have no control over the use of these cookies.

Website visitor statistics

We collect visitor statistics on the use of the site. The data is not personally identifiable and is only stored in the Matomo visitor analytics tool managed by CSC.

By accepting visitor statistics, you allow Matomo to use various technologies, such as analytics cookies and web beacons, to collect statistics about your use of the site.

Change your cookie choices and read more about visitor statistics and cookies


In the selection criteria, the Scientific Customer Panel emphasized new approaches which have not been possible previously, ideally from all fields from natural sciences to humanities.

The chosen Grand Challenge projects

Miguel Caro: Machine learning force fields for modeling of noble metal catalyst nanoparticles, Aalto University

Noble metals are used for electrocatalytical applications, such as fuel production, due to their superior performance regarding selectivity and efficiency. However, the usability of bulk noble metal catalysts is questioned because of cost, Earth abundance, and limits to further engineering the materials. By using noble metal nanoparticles we can overcome the issues regarding scarcity of the raw material, since the amounts required are minute compared to using bulk catalysts. Via engineering of the nanoparticle’s size and morphology and, for alloys, also composition, we can further increase selectivity and efficiency for specific electrocatalytical reactions. For this, an inexpensive atomistic modeling framework can pave the wave towards efficient catalyst screening. In this project we will train state-of-the-art machine learning interatomic potentials for noble metals (Au, Pt, Ag, Cu, Ir, and alloys thereof). The potentials will then be used to find optimal catalysts for CO2 reduction.

Erkka J. Frankberg: Glass Plasticity at Room Temperature, Tampere University

Oxide glasses are important for applications ranging from smartphone screens to window panels and they show great promise for modern electronics, including potential uses in optoelectronics, flexible electronics, photovoltaics, single-electron transistors, and battery technologies. These glasses allow for a wide range of tailored, functional properties, from full dielectrics to tuned semiconductors coupled with visible light transparency, and good chemical and thermal stability. However, in practical terms inorganic oxide glasses are considered brittle, which has led to the current design paradigm of glass and ceramic materials. We aim to transform the current paradigm by attempting to verify that a bulk oxide glass can deform plastically at room temperature.

Gregor Hillers: Seismo-Acoustic Effects of EGS Induced Earthquakes Below Otaniemi, Institute of Seismology, University of Helsinki

We use the rewarded open-source seismic-wave propagation solver SeisSol to realize ‘grand challenge’ simulations aimed at investigating audible effects of engineered earthquakes in Finland. We conduct seismo-acoustic simulations to study the sound patterns excited by earthquakes that were induced by the 2018 geothermal stimulation below the Aalto University campus, which can negatively impact the acceptance of this carbon free energy production, even if the ground shaking levels are safe. To gain insights that can benefit reservoir planners and regulating authorities at other deep geothermal sites in Finland (Tampere, Turku), we use SeisSol to target challenging high-frequency wave field simulations to assess the influence of the earthquake source mechanism, subsurface velocity variations, water bodies, and topography on sound patterns. The lessons learned will also be used in other wave propagation applications with high societal relevance such as tsunamigenic earthquakes.

Hannu Häkkinen: Chimeric gold nanoclusters as novel drug carriers against gastric cancer, University of Jyväskylä

NanoGaC focuses on investigating novel treatment modalities for gastric cancer using functionalized gold nanoclusters (AuNCs) containing both cancer cell-recognizing molecules and drugs on their surface to improve the efficacy of current chemotherapy by directly killing cancer cells as well as inhibiting signaling pathways associated with cell survival.

Atomistic molecular dynamics (MD) simulations will be used for studying the interaction modes and binding affinities between the functionalized AuNCs and the cancer cell receptor (integrin AVB3). The resulting new molecular-level understanding of the binding mode of AuNCs-based targeted drug delivery systems to the cancer cell receptor will guide the experimental stage. NanoGaC is a proof-of-concept at in vitro level towards future collaboration with experimental groups for extending the evaluation of the proposed nanotherapy at in vivo level in pursuit of its clinical translation.

Ondrej Krejci: Kelvin Probe Force Microscopy Simulations with Density Functional Theory, Aalto University

During this grand project, I will run huge array calculations simulating the process, when ultra-sharp Kelvin Probe Force Microscope (KPFM) tip, terminated with a CO molecule, will descend above a measured sample – e.g. organic molecule on metal substrate. This can lead to a tool for chemical identification of single atoms. The KPFM technique is measuring the applied voltage, when the force is minimal. So, in our simulations we will calculate, how the forces acting on the tip are changing with the applied voltage between the tip and the sample. One calculation of the force can be calculated during standard super-computer run, but the necessity of tenths-hundreds scan points in both vertical and horizontal directions and several voltages for this project, is the biggest challenge. In order to run such a big amount of calculations simultaneously, automation of the calculations in form of workflows will be applied.

Maarit Käpylä: Emergence of vortices, bright spots and active regions in simulations of stellar convection, Aalto University

Emergence of structures by self-organisation is a fundamental feature of our world on all complexity levels from the quantum one to the human brain. In stars and planets, major instances of structure formation are thermal and magnetic spots and giant vortices, appearing on a background of turbulent heat transport by convection. In the solar system, prominent examples are sunspots, the “red spots” of Jupiter and the polar hexagon of Saturn. For none of them a fully satisfactory physical understanding exists. Large-scale simulations of a compressible plasma carrying heat from the bottom to the top of a spherical shell will be performed, thus modelling stellar convection zones, aiming at identifying the conditions under which large vortices accompanied by strong magnetic fields and heat excess can emerge. The project will have an important impact on the understanding of stellar activity, the prediction of space-weather events as well as on the modelling of habitability of exoplanets.

Kari Rummukainen: QCD phase transition at the chiral limit, University of Helsinki

QCD has a phase transition at high temperature, in which protons and neutrons “melt” into quark-gluon plasma. This transition is relevant for cosmology and it is probed in high energy nucleus-nucleus collision experiments. The nature of the transition in the chiral limit, where the quarks become massless, is still unclear. In this project we study the transition in QCD with 4 light quarks, using large scale lattice Monte Carlo methods. Previous results have suffered from large finite lattice spacing effects, thus, the physical continuum limit has been difficult to obtain reliably. We have developed a novel lattice action which strongly reduces these lattice effects, enabling us to take more reliable continuum limit.

Milica Todorovic: Active machine learning methods for atmospheric science applications, Aalto University

Public policy on climate change relies on climate models, which require an understanding of aerosol formation. The tendency of highly oxygenated molecules (HOMs) to condense into aerosol particles depends on their saturation vapor pressures (pSat). The huge variety of HOMs make experiment and computation difficult, so we turn to data-science. Here, we develop artificial intelligence (AI)-based predictor models that will be able to compute pSat for any input HOM structure and thus accelerate discovery in atmospheric science. For good AI prediction accuracy, we require a high-quality dataset with 10,000s of HOM entries. Our original active learning approach will allow us to curate a dataset that is maximally representative of HOM chemistry by including only the molecules with high information content. The novel dataset on HOM atmospheric properties will serve the aerosol research community and help us gain a better insight into atmospheric chemistry.