Search Results for author: Nicola Marzari

Found 5 papers, 3 papers with code

Ab initio simulation of band-to-band tunneling FETs with single- and few-layer 2-D materials as channels

no code implementations8 Dec 2020 Áron Szabó Cedric Klinkert, Davide Campi, Christian Stieger, Nicola Marzari, Mathieu Luisier

Full-band atomistic quantum transport simulations based on first principles are employed to assess the potential of band-to-band tunneling FETs (TFETs) with a 2-D channel material as future electronic circuit components.

Mesoscale and Nanoscale Physics

Materials Cloud, a platform for open computational science

no code implementations27 Mar 2020 Leopold Talirz, Snehal Kumbhar, Elsa Passaro, Aliaksandr V. Yakutovich, Valeria Granata, Fernando Gargiulo, Marco Borelli, Martin Uhrin, Sebastiaan P. Huber, Spyros Zoupanos, Carl S. Adorf, Casper W. Andersen, Ole Schütt, Carlo A. Pignedoli, Daniele Passerone, Joost VandeVondele, Thomas C. Schulthess, Berend Smit, Giovanni Pizzi, Nicola Marzari

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling.

Materials Science Computational Physics J.2; I.6; H.4

Automated high-throughput Wannierisation

2 code implementations1 Sep 2019 Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Jonathan R. Yates, Nicola Marzari, Arash A. Mostofi

Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations.

Computational Physics Materials Science

Bayesian Neural Networks at Finite Temperature

1 code implementation8 Apr 2019 Robert J. N. Baldock, Nicola Marzari

We recapitulate the Bayesian formulation of neural network based classifiers and show that, while sampling from the posterior does indeed lead to better generalisation than is obtained by standard optimisation of the cost function, even better performance can in general be achieved by sampling finite temperature ($T$) distributions derived from the posterior.

Bayesian Inference Model Selection

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