Awarded Projects

Abkarino is a domain-specialised, text-only AI foundation model for computational chemistry. It supports day-to-day work in organic, inorganic/non-organic, and physical chemistry while natively integrating regulatory and compliance reasoning.

The main goal of this computational project is to compute, from first principles lattice QCD simulations, the quark-gluon vertex, in both Landau gauge and in linear covariant gauges.

In the context of aeronautics and air transport, the industry has to face new challenges regarding performance and sustainability.

This project proposes a refined framework for automated, efficient, and transferable dataset generation called Smart Configuration Sampling. SCS employs active learning strategies, utilizing ensemble model deviations to guide the iterative selection of high-value atomic configurations.

Colloidal Quantum Dots (QDs) are nano-sized crystallites notable for their exceptional semiconductive properties, which were recently acknowledged with the Nobel Prize in Chemistry.

This project focuses on the design of lipid nanoparticles (LNPs) for efficient RNA-based therapeutics delivery, addressing critical challenges in the field.

Radiotherapy (RT) is one of the most frequently used methods for cancer treatment (above 50% of patients will receive RT).

This project is concerned with the general problem of nano-pollutant removal. The final objective is to provide a reliable framework for the study of aggregation and removal of suspended solids by means of Nanobubbles.

Contact line phenomena entail multi-physics processes that operate at disparate scales, often posing formidable large-scale computing challenges.