Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Ferenc Tasnádi"'
Autor:
Shuyao Lin, Luis Casillas-Trujillo, Ferenc Tasnádi, Lars Hultman, Paul H. Mayrhofer, Davide G. Sangiovanni, Nikola Koutná
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-16 (2024)
Abstract Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivia
Externí odkaz:
https://doaj.org/article/b4bc86e2c3914badab9b9f154f5fe91d
Autor:
Shuyao Lin, Luis Casillas-Trujillo, Ferenc Tasnádi, Lars Hultman, Paul H. Mayrhofer, Davide G. Sangiovanni, Nikola Koutná
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/f83c1b6ee08b4babae712719fe85491d
Autor:
Maxim Bykov, Stella Chariton, Hongzhan Fei, Timofey Fedotenko, Georgios Aprilis, Alena V. Ponomareva, Ferenc Tasnádi, Igor A. Abrikosov, Benoit Merle, Patrick Feldner, Sebastian Vogel, Wolfgang Schnick, Vitali B. Prakapenka, Eran Greenberg, Michael Hanfland, Anna Pakhomova, Hanns-Peter Liermann, Tomoo Katsura, Natalia Dubrovinskaia, Leonid Dubrovinsky
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
High pressure experiments may yield materials with unusual combinations of properties, but typically in small amounts and unstable. Here the authors synthesize millimeter-sized samples of metallic, ultraincompressible and very hard rhenium nitride pe
Externí odkaz:
https://doaj.org/article/ef157f0f2346433d95c67692d7922b61
Autor:
Alexander V Shapeev, Evgeny V Podryabinkin, Konstantin Gubaev, Ferenc Tasnádi, Igor A Abrikosov
Publikováno v:
New Journal of Physics, Vol 22, Iss 11, p 113005 (2020)
A combination of quantum mechanics calculations with machine learning techniques can lead to a paradigm shift in our ability to predict materials properties from first principles. Here we show that on-the-fly training of an interatomic potential desc
Externí odkaz:
https://doaj.org/article/dad481a1d3cb40bf971b5690f4e0a277
Autor:
Magnus Odén, Lars Hultman, Hans Lind, Ferenc Tasnádi, Björn Alling, Axel Knutsson, Igor A. Abrikosov
Publikováno v:
Materials, Vol 4, Iss 9, Pp 1599-1618 (2011)
We review results of recent combined theoretical and experimental studies of Ti1−xAlxN, an archetypical alloy system material for hard-coating applications. Theoretical simulations of lattice parameters, mixing enthalpies, and elastic properties ar
Externí odkaz:
https://doaj.org/article/059dcebcc7c24d35bb7b110a6f2f15a0
Publikováno v:
New Journal of Physics, Vol 19, Iss 3, p 033020 (2017)
The influence of pressure on the electronic structure of Os has attracted substantial attention recently due to reports on isostructural electronic transitions in this metal. Here, we theoretically investigate the Fermi surface of Os from ambient to
Externí odkaz:
https://doaj.org/article/195196f288614492b3064cb575d4ac9f
Publikováno v:
APL Materials, Vol 2, Iss 4, Pp 046106-046106-5 (2014)
Isostructural stability of B1-NaCl type SiN on (001) and (111) oriented ZrN surfaces is studied theoretically and experimentally. The ZrN/SiNx/ZrN superlattices with modulation wavelength of 3.76 nm (dSiNx∼0.4 nm) were grown by dc-magnetron sputter
Externí odkaz:
https://doaj.org/article/4cf87c0891af46b88020ce303c643e09
Autor:
Dat Q. Tran, Ferenc Tasnádi, Agnė Žukauskaitė, Jens Birch, Vanya Darakchieva, Plamen P. Paskov
Publikováno v:
Applied Physics Letters. 122
Owing to their very large piezoelectric coefficients and spontaneous polarizations, (Sc,Y)xAl1−xN alloys have emerged as a new class of III-nitride semiconductor materials with great potential for high-frequency electronic and acoustic devices. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35a2d1ba5594f27f575f133470d4d63a
https://doi.org/10.1063/5.0145847
https://doi.org/10.1063/5.0145847
Autor:
Henrik Levämäki, Florian Bock, Davide G. Sangiovanni, Lars J.S. Johnson, Ferenc Tasnádi, Rickard Armiento, Igor A. Abrikosov
Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&D). Supervised machine learning of statistical models can utilize databases of materials parameter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44aa43eaaeca5fa6f4e147675eca0497
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-191645
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-191645
Autor:
Dominique Laniel, Florian Trybel, Adrien Néri, Yuqing Yin, Andrey Aslandukov, Timofey Fedotenko, Saiana Khandarkhaeva, Ferenc Tasnádi, Stella Chariton, Carlotta Giacobbe, Eleanor Lawrence Bright, Michael Hanfland, Vitali Prakapenka, Wolfgang Schnick, Igor A. Abrikosov, Leonid Dubrovinsky, Natalia Dubrovinskaia
Publikováno v:
Chemistry – A European Journal. 28
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c39dbc2bd93851c8291b812b09ae8a69
https://doi.org/10.1002/chem.202203123
https://doi.org/10.1002/chem.202203123