Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Johannes T. Margraf"'
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-8 (2023)
Abstract The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of
Externí odkaz:
https://doaj.org/article/9f084306f16c4a529078f1efb6724486
Autor:
Pierre Kube, Jinhu Dong, Nuria Sánchez Bastardo, Holger Ruland, Robert Schlögl, Johannes T. Margraf, Karsten Reuter, Annette Trunschke
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Propylene and propylene oxide are formed over boron nitride or SiO2 in the gas phase without yielding large amounts of CO2. Conversion at non-specific interfaces can thus be a successful strategy for the synthesis of oxidation-sensitive products.
Externí odkaz:
https://doaj.org/article/9ae5d4f3bb9b444da9db6e2c1469b8a1
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Existing methods for organic semiconductor computational screening are limited by the computational demand of the process, leading to the identification of non-optimal material candidates. Here, the authors report machine learning method to guide the
Externí odkaz:
https://doaj.org/article/17214865234d479bbb2f2a156defa811
Autor:
Johannes T. Margraf, Karsten Reuter
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
Semilocal density functionals, while computationally efficient, do not account for non-local correlation. Here, the authors propose a machine-learning approach to DFT that leads to non-local and transferable functionals applicable to non-covalent, io
Externí odkaz:
https://doaj.org/article/abc59ecbfc9544beb4d600f57d73e09e
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell mol
Externí odkaz:
https://doaj.org/article/61fa2a4df63746c2accee2753ea074c2
Autor:
Johannes T. Margraf, Karsten Reuter
Publikováno v:
ACS Omega, Vol 4, Iss 2, Pp 3370-3379 (2019)
Externí odkaz:
https://doaj.org/article/0238b44cfd204fd0908476286cac9565
Publikováno v:
Nanomaterials, Vol 12, Iss 17, p 2950 (2022)
The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characte
Externí odkaz:
https://doaj.org/article/8a66e1a0f40e4f40ad684201aaa1347f
Publikováno v:
Nature Catalysis. 6:112-121
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d9d9660708f07e8fff8f00585cfdddd
https://doi.org/10.1038/s41929-022-00896-y
https://doi.org/10.1038/s41929-022-00896-y
Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They therefore lack a description of long-range electrostatic interactions and non-local
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbbf568c802e6375f4bbf56249264759
https://doi.org/10.26434/chemrxiv-2023-n0dxz
https://doi.org/10.26434/chemrxiv-2023-n0dxz
Autor:
Johannes T. Margraf
Publikováno v:
Angewandte Chemie
Angewandte Chemie International Edition
Angewandte Chemie International Edition
Machine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data-driven endeavour. Unfortunately, large well-curated databases are sparse in chemistry. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54b1af96f6022d9b8ab73ed32246fd23
https://hdl.handle.net/21.11116/0000-000C-C5C0-C21.11116/0000-000C-C5BE-0
https://hdl.handle.net/21.11116/0000-000C-C5C0-C21.11116/0000-000C-C5BE-0