Dr. Cesare Roncaglia

Postdoctoral Researcher

Cesare Roncaglia received his PhD at the University of Genoa, Italy, in March 2024 under the supervision of Prof. Riccardo Ferrando, Nanobiocomp group at the Department of Physics. The subject of his thesis was the development and application of computational methods for the investigation of structures of metal nanoparticles. During his PhD, his main focus was the analysis of numerical simulations, including Monte Carlo, atomistic and ab-initio calculations as well as the application of machine learning algorithms. In the same period, he had different collaborations with the experimental groups of Prof. Andreazza of the University of Orléans, France and of Dr. Chloé Minnai at OIST, Japan.

Cesare joined the Atomistic Simulations group of nanotech@surfaces Laboratory in June 2024 to work on the TailorX project. Currently he is working on establishing a protocol for the screening of high-entropy MAX phases, through free energy evaluation at the ab initio level, complemented by machine learning.

Fields of interest

Solid state physics, density functional theory, atomistic simulations, machine learning

Selected publications

D. Nelli, C. Roncaglia, R. Ferrando, C. Minnai, Shape Changes in AuPd Alloy Nanoparticles Controlled by Anisotropic Surface Stress Relaxation, J. Phys. Chem. Lett. 12, 19, 4609–4615 (2021). DOI: 10.1021/acs.jpclett.1c00787

C. Roncaglia, D. Rapettia, R. Ferrando, Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition, Phys. Chem. Chem. Phys. 23, 23325-23335 (2021). DOI: 10.1039/d1cp02143e

D. Rapetti, C. Roncaglia, R. Ferrando, Optimizing the Shape and Chemical Ordering of Nanoalloys with Specialized Walkers, Adv. Theory Simul. 6, 2300268 (2023). DOI: 10.1002/adts.202300268

E. El koraychy, C. Roncaglia, D. Nelli, M. Cerbelaud, R. Ferrando, Growth mechanisms from tetrahedral seeds to multiply twinned Au nanoparticles revealed by atomistic simulations, Nanoscale Horiz. 7, 883–889 (2022). DOI: 10.1039/d1nh00599e