Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Stuke, Annika"'
Autor:
Yeu, In Won, Stuke, Annika, pez-Zorrilla, Jon L., Stevenson, James M., Reichman, David R., Friesner, Richard A., Urban, Alexander, Artrith, Nongnuch
Artificial neural network (ANN) potentials enable highly accurate atomistic simulations of complex materials at unprecedented scales. Despite their promise, training ANN potentials to represent intricate potential energy surfaces (PES) with transfera
Externí odkaz:
http://arxiv.org/abs/2412.05773
Machine learning methods usually depend on internal parameters -- so called hyperparameters -- that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition o
Externí odkaz:
http://arxiv.org/abs/2004.00675
Autor:
Stuke, Annika, Kunkel, Christian, Golze, Dorothea, Todorović, Milica, Margraf, Johannes T., Reuter, Karsten, Rinke, Patrick, Oberhofer, Harald
Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. W
Externí odkaz:
http://arxiv.org/abs/2001.08954
Autor:
Stuke, Annika, Todorović, Milica, Rupp, Matthias, Kunkel, Christian, Ghosh, Kunal, Himanen, Lauri, Rinke, Patrick
Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning w
Externí odkaz:
http://arxiv.org/abs/1812.08576
Autor:
Stuke, Annika
MBTR of AA is published in https://zenodo.org/record/3967308 It contains the HOMO values and the MBTR K2 and K2K3 This repo contains the CM, XYZ and HOMO (same as above) of AA Maintained by Kunal Ghosh (firstname.lastname @ aalto.fi)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a9b4bd45c47b4c8b2656c202662dba5
Autor:
Stuke, Annika, Todorović, Milica, Rupp, Matthias, Kunkel, Christian, Ghosh, Kunal, Himanen, Lauri, Rinke, Patrick
Publikováno v:
Journal of Chemical Physics; 5/28/2019, Vol. 150 Issue 20, pN.PAG-N.PAG, 13p, 4 Diagrams, 2 Charts, 9 Graphs
Akademický článek
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Autor:
Stuke, Annika
AA dataset pre-split based on JCP paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::579399f31a2e7a67694c8889928cec3f
Akademický článek
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Autor:
Ghosh, Kunal, Stuke, Annika, Todorović, Milica, Jørgensen, Peter Bjørn, Schmidt, Mikkel N., Vehtari, Aki, Rinke, Patrick
openaire: EC/H2020/676580/EU//NoMaD 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: m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______661::776b226ee8bf87c807d01f91b9d4b29a
https://aaltodoc.aalto.fi/handle/123456789/36841
https://aaltodoc.aalto.fi/handle/123456789/36841