Niccolò Di Eugenio

collaborators

5 papers

cond-mat.supr-con2026

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa $_{2}$ Cu $_{3}$ O $_{7 - I^´}$

NiccolÃ² Di Eugenio, Ashley Dickson, Flyura Djurabekova +9

High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form…

cond-mat.supr-con2026

Insights Into Radiation Damage in YBa $_{2}$ Cu $_{3}$ O $_{7 - I^´}$ From Machine-Learned Interatomic Potentials

Ashley Dickson, NiccolÃ² Di Eugenio, Federico Ledda +10

Accurate prediction of radiation damage in YBa $_{2}$ Cu $3$ O $7 - I^´$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion…

cond-mat.supr-con2025

Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7

Federico Ledda, Daniele Torsello, Davide Gambino +8

Radiation damage in high-temperature cuprate superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors…

cond-mat.mtrl-sci2025

The diffusion-driven orthorhombic to tetragonal transition in YBa $_{2}$ Cu $_{3}$ O $_{7}$ derived with a machine learning interatomic potential

Davide Gambino, NiccolÃ² Di Eugenio, Jesper ByggmÃ¤star +4

Defects in high temperature superconductors such as YBa $_{2}$ Cu $_{3}$ O $_{7}$ (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress su…

cond-mat.mtrl-sci2025

Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure

Andrea Fantasia, Daniele Lanzoni, NiccolÃ² Di Eugenio +3

We introduce a unified machine-learning framework designed to conveniently tackle the temporal evolution of alloy microstructures under the influence of an elastic field. This appr…

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa2​Cu3​O7−I^´​

Insights Into Radiation Damage in YBa2​Cu3​O7−I^´​ From Machine-Learned Interatomic Potentials

Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7

The diffusion-driven orthorhombic to tetragonal transition in YBa2​Cu3​O7​ derived with a machine learning interatomic potential

Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa $_{2}$ Cu $_{3}$ O $_{7 - I^´}$

Insights Into Radiation Damage in YBa $_{2}$ Cu $_{3}$ O $_{7 - I^´}$ From Machine-Learned Interatomic Potentials

The diffusion-driven orthorhombic to tetragonal transition in YBa $_{2}$ Cu $_{3}$ O $_{7}$ derived with a machine learning interatomic potential