Awarded Projects

This proposal aims to enhance the predictive accuracy of the MACE-MP machine learning model for modeling metallic phase transitions, a crucial step in developing advanced materials. .

The project investigates the characteristic frequencies and length-scales of wall pressure fluctuations inswept shock wave/turbulent boundary layer interactions in the presence of cylindrical symmetry, based on analysis of a direct numerical simulations database.

For the first time, this project will simulate the complex three-dimensional (3D) global kinetic interaction between the solar wind and the entire magnetosphere of Earth while maintaining true scales.

Understanding how nucleon properties emerge from quarks and gluons comprises one ofthe key goals of the Electron-Ion Collider (EIC) to be built at BNL.

This proposal aims to further instruction-tune TildeLM—a 30B-parameter, open-source multilingual language model optimized for underrepresented Nordic and Eastern European languages.

How did the Earth and the other terrestrial planets form? Even though there are many models in existence that attempt to solve this question, all of them suffer from specific shortages that can only be overcome with next-generation computation facilities.

The atom-scale design of surfaces holds enormous potential for applications such as catalysis or superlubricating interfaces: Approximately 24% of the global energy consumption is lost to friction, while the chemical industry consumes around 29% of the energy in the manufacturing sector.

The origin of multiple stellar populations (MPs) in globular clusters (GCs) is one of the most puzzling issues of stellar astrophysics. Many attempts have been done both from observational and theoretical studies to unveil the enigma of their formation but, so far, a clear picture is still lacking.

Within the framework of the European Innovation Council project MOJITO, this project aims to elucidate the atomistic mechanisms governing CaCO3 calcination through large-scale molecular dynamics (MD) simulations based on machine learning potentials (MLPs).