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Hundreds of new, stable, one-dimensional materials from a generative machine learning model
Hadeel Moustafa, Peder Meisner Lyngby, Jens Jørgen Mortensen +2
We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computation…
Two-dimensional ferroelectrics from high throughput computational screening
Mads Kruse, Urko Petralanda, Morten N. Gjerding +3
We report a high throughput computational search for two-dimensional ferroelectric materials. The starting point is 252 pyroelectric materials from the computational 2D materials d…
Combining experiments on luminescent centres in hexagonal boron nitride with the polaron model and ab initio methods towards the identification of their microscopic origin
Moritz Fischer, Ali Sajid, Jake Iles-Smith +10
The two-dimensional material hexagonal boron nitride (hBN) hosts luminescent centres with emission energies of 2 eV which exhibit pronounced phonon sidebands. We investigate the mi…
Indirect band gap semiconductors for thin-film photovoltaics: High-throughput calculation of phonon-assisted absorption
Jiban Kangsabanik, Mark Kamper Svendsen, Alireza Taghizadeh +2
Discovery of high-performance materials remains one of the most active areas in photovoltaics (PV) research. Indirect band gap materials form the largest part of the semiconductor…
Absorption Adsorption: High-Throughput Computation of Impurities in 2D Materials
Joel Davidsson, Fabian Bertoldo, Kristian S. Thygesen +1
Doping of a two-dimensional (2D) material by impurity atoms occurs \textit{via} two distinct mechanisms: absorption of the dopants by the 2D crystal or adsorption on its surface. T…
Data-driven discovery of novel 2D materials by deep generative models
Peder Lyngby, Kristian Sommer Thygesen
Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here we show that a crystal dif…