Corrigenda: Caterino M, Vásquez-Vélez L (2017) A revision of Prespelea Park (Staphylinidae, Pselaphinae). ZooKeys 685: 105–130. https://doi.org/10.3897/zookeys.685.13811

Caterino, Michael S.; Vásquez-Vélez, Laura M.

doi:10.3897/zookeys.688.19867

Cited by 2 publications

(3 citation statements)

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“…The NiPt solid solution dataset [43] with atomic structures containing 256 atoms, 864 atoms, and 2048 atoms is open-source and available at the following website https://doi.ccs.ornl.gov/ui/doi/430. The NiPt solid solution data for atomic structures with 108 atoms [67] is also open-source and available at the following website https://doi.ccs.ornl.gov/ui/doi/430.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…In figure 5 we show the training time as a function of the size of the crystal structure. To further qualify the scaling performance of the training, we included one additional set of crystal structures with 108 atoms [67] for the training, where the crystal structures have been obtained with the same methodology as already described in section 3.1.1. The scaling results show that the training time of the model scales linearly with the size of the crystal.…”

Section: Algorithmic Scalabilitymentioning

confidence: 99%

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Transferring predictions of formation energy across lattices of increasing size*

Lupo Pasini,

Karabin,

Eisenbach

2024

Mach. Learn.: Sci. Technol.

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In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum (NiPt) solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using the second nearest-neighbor modified embedded-atom method (2NN MEAM) empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic (FCC) crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2,048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

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Section: Data Availability Statementmentioning

confidence: 99%

Section: Algorithmic Scalabilitymentioning

confidence: 99%

Transferring predictions of formation energy across lattices of increasing size*

Lupo Pasini,

Karabin,

Eisenbach

2024

Mach. Learn.: Sci. Technol.

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“…(2021). The electron density data from the LPW instrument are publicly available at NASA Planetary Plasma Interactions of Planetary Data System (Andersson, 2017), super thermal electron data from SWEA Instrument are publicly available at Mitchell (2017), magnetic field data from the MGA instrument are publicly available at Connerney (2017), and neutral atmospheric density from the NGIMS instrument are publicly available at (Elrod et al., 2014). MAVEN's trajectory plots in Figure 2 are from https://lasp.colorado.edu/maven/sdc/public/pages/plots/ql_orb/index.html.…”

Section: Data Availability Statementmentioning

confidence: 99%

On the Gravity Wave‐Seeded Ionospheric Irregularities in the Martian Ionosphere

Tian,

Jiang,

Sánchez‐Cano

et al. 2024

JGR Planets

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For the past few decades, it has been demonstrated that gravity waves (GWs) and neutral winds can drive ionospheric irregularities on Earth. Still, as far as we know, the formation of ionospheric irregularities on Mars due to GWs has not been well studied. In this study, we use data from the NASA's Mars Atmosphere and Volatile Evolution (MAVEN) mission to show evidence of an irregularity event in the Martian ionosphere, potentially seeded by the GWs break (GWB). Statistical findings indicate that the observed ratio of GWB‐related irregularity events varies from ∼0.25 to ∼0.47 each year, and the average ratio in 2015–2020 is ∼0.37. We perform a numerical simulation to provide further insight into the processes behind irregularity formation, which employs neutral wind shear as a source of perturbation in the context of the GWB. The simulations yield results fundamentally aligned with the observed characteristics of ionospheric irregularities in the 2018 event by considering the wind shear as the disturbance source. This study provides supplementary insights into the perturbation sources involved in shaping irregularities within the Martian ionosphere and presents valuable information about the coupling between the Martian ionosphere and the lower atmosphere.

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Corrigenda: Caterino M, Vásquez-Vélez L (2017) A revision of Prespelea Park (Staphylinidae, Pselaphinae). ZooKeys 685: 105–130. https://doi.org/10.3897/zookeys.685.13811

Abstract: In our recent revision of the genus Prespelea Park, we neglected to explicitly designate a repository for the holotype of Prespelea enigma Caterino & Vásquez-Vélez sp. n. This omission invalided its description, and that species name is not yet available. We herein rectify this problem.

Cited by 2 publications

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Transferring predictions of formation energy across lattices of increasing size*

Transferring predictions of formation energy across lattices of increasing size*

On the Gravity Wave‐Seeded Ionospheric Irregularities in the Martian Ionosphere

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