Niccolò Di Eugenio

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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, Niccolò Di Eugenio, Jesper Byggmästar +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, Niccolò 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…

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

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