Kernels and Graphs on M25 + H (parent repository)
Pihlajamäki, Antti; Kärkkäinen, Tommi; Malola, Sami; Häkkinen, Hannu. (2023). Kernels and Graphs on M25 + H (parent repository). University of Jyväskylä. 10.17011/jyx/dataset/87525
Päivämäärä
2023Tekijänoikeudet
Pihlajamäki, Antti and University of Jyväskylä
The repository contains codes related to article "Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts". There are two main types of codes: codes to transform a catalytic system of protected gold nanoparticle and a single hydrogen atom into a graph-based representation, and codes to run kernel-based machine learning methods to predict interaction energies between the nanoparticle and the hydrogen atom.
This is the metadata for the parent repository of the codes. Updates and possible corrections are documented in the GitLab project, where the material saved and shared. The GitLab project can be found and downloaded from the following address: https://gitlab.jyu.fi/mlnovcat-aneepihl/kernels-and-graphs-on-m25-h
Julkaisija
University of JyväskyläSisältää aineistoja
- Pihlajamäki, Antti; Kärkkäinen, Tommi; Malola, Sami; Häkkinen, Hannu. Kernels and Graphs on M25 + H. V. 31.3.2023. University of Jyväskylä. https://doi.org/10.17011/jyx/dataset/87521
Asiasanat
Aineisto tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/ResearchDataset/183430608
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Tutkimusdata [284]
Rahoittaja(t)
Suomen Akatemia; Academy of FinlandRahoitusohjelmat(t)
Muut, SA; Others, AoFLisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
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GraphBNC source code (parent repository)
Pihlajamäki, Antti; Matus Cortés, Maria; Malola, Sami; Häkkinen, Hannu (University of Jyväskylä, 2024)GraphBNC is a framework that combines graph theory based methods, machine learning and other computational tools for placing protected gold nanoclusters on blood proteins. Machine learning part, artificial neural networks ... -
Kernels and Graphs on M25 + H
Kärkkäinen, Tommi; Häkkinen, Hannu; Malola, Sami; Pihlajamäki, Antti (University of Jyväskylä, 2023)Codes related to article "Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts". There are two main types of codes: codes to transform a catalytic system of ... -
GraphBNC source code
Pihlajamäki, Antti; Matus Cortés, Maria; Malola, Sami; Häkkinen, Hannu (University of Jyväskylä, 2024)GraphBNC is a framework that combines graph theory based methods, machine learning and other computational tools for placing protected gold nanoclusters on blood proteins. Machine learning part, artificial neural networks ... -
3D Printed Palladium Catalyst for Suzuki-Miyaura Cross-coupling Reactions
Haukka, Matti; Bulatov, Evgeny; Lahtinen, Elmeri; Kivijärvi, Lauri; Hey-Hawkins, Evamarie (Wiley-VCH Verlag, 2020)Selective laser sintering (SLS) 3d printing was utilized to manufacture solid catalyst for Suzuki-Miyaura cross-coupling reactions from polypropylene as a base material and palladium nanoparticles on silica (SilicaCat Pd0 ... -
Highly Robust but Surface-Active : An N-Heterocyclic Carbene-Stabilized Au25 Nanocluster
Shen, Hui; Deng, Guocheng; Kaappa, Sami; Tan, Tongde; Han, Ying-Zi; Malola, Sami; Lin, Shui-Chao; Teo, Boon K.; Häkkinen, Hannu; Zheng, Nanfeng (Wiley-VCH Verlag, 2019)Surface organic ligands play a critical role in stabilizing atomically precise metal nanoclusters in solutions. However, it is still challenging to prepare highly robust ligated metal nanoclusters that are surface-active ...
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