activity
20192026
most citedPANNA: Properties from Artificial Neural Network Architectures

60 citations · 61 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2026★ 1 cited

Seeking Cost-Optimal Infrastructure Size for Distributed Filesystems: A Ceph Case Study

Niccolo Tosato, Isac Pasianotto, Ruggero Lot +1

Distributed Filesystems (DFS) are a crucial component of modern computing environments, and their performance is critical to the success of all the facilities that rely on them. Ho…

cond-mat.mtrl-sci2025

An accurate alternative to hybrid functionals for germanium: DFT+

Abdulgaffar Abdurrazaq, Ruggero Lot, Antoine Jay +5

The accuracy of bulk property predictions in density functional theory (DFT) calculations depends on the choice of exchange-correlation functional. While the Perdew-Burke-Ernzerhof…

physics.comp-ph2023

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…

cond-mat.mtrl-sci2020

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…

physics.comp-ph2019★ 60 cited

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…