63 citations · 272 across the 18 of their papers we have counts for
19 papers
PANNA 2.0: Efficient neural network interatomic potentials and new architectures
Franco Pellegrini, Ruggero Lot, Yusuf Shaidu +1
We present the latest release of PANNA 2.0 (Properties from Artificial Neural Network Architectures), a code for the generation of neural network interatomic potentials based on lo…
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?
Franco Pellegrini, Giulio Biroli
Pruning methods can considerably reduce the size of artificial neural networks without harming their performance. In some cases, they can even uncover sub-networks that, when train…
A Systematic Approach to Generating Accurate Neural Network Potentials: the Case of Carbon
Yusuf Shaidu, Emine Kucukbenli, Ruggero Lot +3
Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approa…
An analytic theory of shallow networks dynamics for hinge loss classification
Franco Pellegrini, Giulio Biroli
Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is sti…
PANNA: Properties from Artificial Neural Network Architectures
Ruggero Lot, Franco Pellegrini, Yusuf Shaidu +1
Prediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have b…
Thermolubricity and the Jarzynski equality
Franco Pellegrini, Emanuele Panizon, Giuseppe Santoro +1
We discuss and qualify a previously unnoticed connection between two different phenomena in the physics of nanoscale friction, general in nature and also met in experiments includi…