1 citations · 1 across the 3 of their papers we have counts for
10 papers
Complex topological features of reservoirs shape learning performances in bio-inspired recurrent neural networks
Valeria d'Andrea, Michele Puppin, Manlio De Domenico
Recurrent networks are a special class of artificial neural systems that use their internal states to perform computing tasks for machine learning. One of its state-of-the-art deve…
Atomic-level description of thermal fluctuations in inorganic lead halide perovskites
Oliviero Cannelli, Julia Wiktor, Nicola Colonna +10
The potential of lead-halide perovskites for realistic applications is currently hindered by their limited long-term stability under functional activation. While the role of lattic…
Quantifying Photoinduced Polaronic Distortions in Inorganic Lead Halide Perovskites Nanocrystals
Oliviero Cannelli, Nicola Colonna, Michele Puppin +16
The development of next generation perovskite-based optoelectronic devices relies critically on the understanding of the interaction between charge carriers and the polar lattice i…
Direct measurement of key exciton properties: energy, dynamics and spatial distribution of the wave function
Shuo Dong, Michele Puppin, Tommaso Pincelli +15
Excitons, Coulomb-bound electron-hole pairs, are the fundamental excitations governing the optoelectronic properties of semiconductors. While optical signatures of excitons have be…
Nonequilibrium Charge-Density-Wave Order Beyond the Thermal Limit
J. Maklar, Y. W. Windsor, C. W. Nicholson +17
The interaction of many-body systems with intense light pulses may lead to novel emergent phenomena far from equilibrium. Recent discoveries, such as the optical enhancement of the…
Light-induced renormalization of the Dirac quasiparticles in the nodal-line semimetal ZrSiSe
G. Gatti, A. Crepaldi, M. Puppin +16
In nodal-line semimetals linearly dispersing states form Dirac loops in the reciprocal space, with high degree of electron-hole symmetry and almost-vanishing density of states near…