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Found 6 papers, 4 papers with code
Machine learning Hubbard parameters with equivariant neural networks
We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations.
Ab initio simulation of band-to-band tunneling FETs with single- and few-layer 2-D materials as channels
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
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
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
Wannier90 as a community code: new features and applications
Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states.
Materials Science Computational Physics
Bayesian Neural Networks at Finite Temperature
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.
