Z
Zamaan Raza
Researcher at National Institute for Materials Science
Publications - 22
Citations - 839
Zamaan Raza is an academic researcher from National Institute for Materials Science. The author has contributed to research in topics: Glycolaldehyde & Phase (matter). The author has an hindex of 15, co-authored 22 publications receiving 662 citations. Previous affiliations of Zamaan Raza include Pierre-and-Marie-Curie University & University College London.
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Journal ArticleDOI
Impact of anharmonic effects on the phase stability, thermal transport, and electronic properties of AlN
Nina Shulumba,Zamaan Raza,Olle Hellman,Olle Hellman,Erik Janzén,Igor A. Abrikosov,Igor A. Abrikosov,Magnus Odén +7 more
TL;DR: In this article, anharmonic effects have a considerable impact on the phase stability and transport properties of AlN, since they are much stronger in the rocksalt phase than in the wurtzite phase.
Journal ArticleDOI
Structure of liquid carbon dioxide at pressures up to 10 GPa
Frédéric Datchi,Gunnar Weck,Antonino Marco Saitta,Zamaan Raza,Zamaan Raza,Gaston Garbarino,S. Ninet,Dylan Spaulding,Jean-Antoine Queyroux,Mohamed Mezouar +9 more
TL;DR: In this article, the short-range structure of liquid carbon dioxide is investigated by means of x-ray diffraction experiments in a diamond anvil cell (DAC) and classical molecular dynamics simulations.
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Highly accurate local basis sets for large-scale DFT calculations in conquest
David R. Bowler,David R. Bowler,David R. Bowler,Jack S. Baker,Jack T. L. Poulton,Shereif Y. Mujahed,Jianbo Lin,Sushma Yadav,Zamaan Raza,Tsuyoshi Miyazaki +9 more
TL;DR: In this article, the authors present a brief overview of the large-scale DFT code Conquest, which is capable of modelling such large systems, and discuss approaches to the generation of consistent, well-converged pseudo-atomic basis sets which will allow such large scale calculations.
Posted Content
Towards Reflectivity profile inversion through Artificial Neural Networks
TL;DR: It is demonstrated that, under certain circumstances, properly trained Deep Neural Networks are capable of correctly recovering plausible SLD profiles when presented with never-seen-before simulated reflectivity curves.
Journal ArticleDOI
Highly accurate local basis sets for large-scale DFT calculations in CONQUEST.
David R. Bowler,Jack S. Baker,Jack T. L. Poulton,Shereif Y. Mujahed,Jianbo Lin,Sushma Yadav,Zamaan Raza,Tsuyoshi Miyazaki +7 more
TL;DR: In this article, the authors present a brief overview of the large-scale DFT code Conquest, which is capable of modelling such large systems, and discuss approaches to the generation of consistent, well-converged pseudo-atomic basis sets which will allow such large scale calculations.