Au38Q MBTR-K3
Linja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi (2020). Au38Q MBTR-K3. V. 11.11.2020. Zenodo. https://dx.doi.org/10.5281/zenodo.4268064.
JYU-tekijät:
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Kaikki tekijät: Linja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi
Rahoittajat: Suomen Akatemia
Oikeudenhaltijat:
Saatavuus ja tunnisteet
Saatavuustieto: Ladattavissa verkosta
Aineiston julkaisuvuosi: 2020
Aineiston pysyvät tunnisteet: doi:10.5281/zenodo.4268064
Aineiston kuvailu
Kuvaus: The dataset contains nine variants of the same idea. In each, an observation refers to a MBTR description of the structural angles of the Au38Q hybrid nanoparticle of a single timestep in a DFT simulation and the potential energy of the said nanoparticle at the timestep. The input space is the MBTR description and the output space is the potential energy. Features refer to the output of the MBTR descriptor, here used as the input.
We used three different numbers of observations and three different numbers of descriptor accuracies. Regarding the the number of observations, we used RS-maximin to find out the most different observations available and used the first 4000 and first 8000 as the selections in 4k and 8k variants. Regarding the number of features, we used different descriptor accuracy values [2,10,100] that produced descriptors of lengths [80,400,4000]. This allowed the number of features to represent the data description resolution. Downsampling of the number of features from 4000 to lower numbers was not used.
Further details are presented in paper Do Randomized Algorithms Improve the Efficiency of
Minimal Learning Machine? by Linja et al.
We used three different numbers of observations and three different numbers of descriptor accuracies. Regarding the the number of observations, we used RS-maximin to find out the most different observations available and used the first 4000 and first 8000 as the selections in 4k and 8k variants. Regarding the number of features, we used different descriptor accuracy values [2,10,100] that produced descriptors of lengths [80,400,4000]. This allowed the number of features to represent the data description resolution. Downsampling of the number of features from 4000 to lower numbers was not used.
Further details are presented in paper Do Randomized Algorithms Improve the Efficiency of
Minimal Learning Machine? by Linja et al.
Aineiston kieli: englanti
Vapaat asiasanat: Regressio; Many Body Tensor Representation; MBTR; Hybridinanopartikkelit
YSO-asiasanat: koneoppiminen; regressioanalyysi
Tieteenalat : 113 Tietojenkäsittely- ja informaatiotieteet
Käsitteletkö erityisiä henkilötietoryhmiä tutkimuksessasi?: Ei
Aineistoon liittyvät Converiksessa olevat hankkeet
- Hybridinanopartikkelien rakenteiden ennustaminen tekoälyä hyväksi käyttäen
- Kärkkäinen, Tommi
- Suomen Akatemia
