Zobrazeno 1 - 10
of 71
pro vyhledávání: '"MILICA TODOROVIĆ"'
Publikováno v:
ACS Omega, Vol 9, Iss 32, Pp 34684-34691 (2024)
Externí odkaz:
https://doaj.org/article/8681c3a6740042fa837acd8e9af85faf
Autor:
Jingrui Li, Fang Pan, Guo‐Xu Zhang, Zenghui Liu, Hua Dong, Dawei Wang, Zhuangde Jiang, Wei Ren, Zuo‐Guang Ye, Milica Todorović, Patrick Rinke
Publikováno v:
Small Structures, Vol 5, Iss 11, Pp n/a-n/a (2024)
Structural disorder is common in metal‐halide perovskites and important for understanding the functional properties of these materials. First‐principles methods can address structure variation on the atomistic scale, but they are often limited by
Externí odkaz:
https://doaj.org/article/b53711afebd54fa0b8d64df19cc31ba2
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract Low-volatile organic compounds (LVOCs) drive key atmospheric processes, such as new particle formation (NPF) and growth. Machine learning tools can accelerate studies of these phenomena, but extensive and versatile LVOC datasets relevant for
Externí odkaz:
https://doaj.org/article/d6ef769c765b4c4896d80d09fca99824
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022)
Abstract We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parame
Externí odkaz:
https://doaj.org/article/35c8268bcc314ef1ab63e41ba83cb8ee
Publikováno v:
Beilstein Journal of Nanotechnology, Vol 11, Iss 1, Pp 1577-1589 (2020)
Identifying the atomic structure of organic–inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find
Externí odkaz:
https://doaj.org/article/7de4fe929b6a423d918e7621ea859413
Publikováno v:
New Journal of Physics, Vol 25, Iss 11, p 113046 (2023)
We have studied the possibility of utilizing artificial intelligence (AI) models to optimize high-temperature superconducting (HTS) multilayer structures for applications working in a specific field and temperature range. For this, we propose a new v
Externí odkaz:
https://doaj.org/article/714ebad3725942f4b41177551309c2e7
Autor:
Alexander T. Egger, Lukas Hörmann, Andreas Jeindl, Michael Scherbela, Veronika Obersteiner, Milica Todorović, Patrick Rinke, Oliver T. Hofmann
Publikováno v:
Advanced Science, Vol 7, Iss 15, Pp n/a-n/a (2020)
Abstract Density functional theory calculations are combined with machine learning to investigate the coverage‐dependent charge transfer at the tetracyanoethylene/Cu(111) hybrid organic/inorganic interface. The study finds two different monolayer p
Externí odkaz:
https://doaj.org/article/dd8eef7680cd4191b8d2262ccdf97cd1
Autor:
Kunal Ghosh, Annika Stuke, Milica Todorović, Peter Bjørn Jørgensen, Mikkel N. Schmidt, Aki Vehtari, Patrick Rinke
Publikováno v:
Advanced Science, Vol 6, Iss 9, Pp n/a-n/a (2019)
Abstract Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP),
Externí odkaz:
https://doaj.org/article/8366dcb23a5e45a782579f438cd5cc40
Publikováno v:
Frontiers in Chemistry, Vol 7 (2019)
Anatase TiO2 provides photoactivity with high chemical stability at a reasonable cost. Different methods have been used to enhance its photocatalytic activity by creating band gap states through the introduction of oxygen vacancies, hydrogen impuriti
Externí odkaz:
https://doaj.org/article/213243313fb243f69ad47d8578451213
As cloud and aerosol interactions remain large uncertainties in current climate models (IPCC) they are of special interest for atmospheric science. It is estimated that more than 70% of all cloud condensation nuclei origin from so-called New Particle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1360cc631b23f608356c5fccc79ca7db
https://doi.org/10.5194/egusphere-egu23-1135
https://doi.org/10.5194/egusphere-egu23-1135
