This PhD project integrates advanced materials modelling and theoretical nuclear physics to support next-generation nuclear energy systems, with a particular focus on Small Modular Reactors and spent nuclear fuel management.
The research will examine radiation- and high-temperature-resistant high-entropy alloys using ab initio, many-body, and Monte Carlo methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant materials, predict material degradation under irradiation, and assist in designing advanced alloys for next-generation reactors. The project aims to address open challenges in materials physics and nuclear theory, contributing to the understanding and design of novel materials for extreme environments and to improved modelling of nuclear processes. Expected outcomes include validated computational tools, new insights into the properties of advanced structural materials, and theoretical results relevant to safe, sustainable nuclear energy and nuclear waste management, in line with European and Italian strategic research priorities.