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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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C50
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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C50
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MD_1024398629573454165794357
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[
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[
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"DS_tag5zubl21w8_0",
"DS_07zjrtacjg3z_0",
"DS_8zft35i5sbgd_0"
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CO_7780579906411347127576823
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CA-9_BB_validation
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[
"Daniel Hedman",
"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
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[
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1
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[
3
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2023
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1
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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DS_8zft35i5sbgd_0
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CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C71
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C
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A
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{"input": {"encut": {"value": 520, "units": "eV"}}, "hash": "102439862957345416579435757980411251184715859364031590652706556386856859773466490977891980138017845683216054395454090990042136855050809363628328039055230", "id": "MD_1024398629573454165794357"}
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MD_1024398629573454165794357
| 2024-08-16T15:37:51 |
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PO_8750818473443852843806649
| null | null | null |
[
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[
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"DS_tag5zubl21w8_0",
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[
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"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
] | 1 | 4,003 | 4,003 | 233,034 | 0 | 4,003 | 0 | 4,003 | 0 | 4,003 | -501.735936 | 10,202.734006 | 0 | 2023-12-01T18:52:50 |
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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CA-9_BB_validation
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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2023
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C59
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MD_1024398629573454165794357
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PO_4237913186628438314574006
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[
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[
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CO_7863504736365352956389898
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CA-9_BB_validation
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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VASP
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DFT-PBE
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MD_1024398629573454165794357
| 2024-08-16T14:54:52 |
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PO_2340261454103579048720175
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[
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"DS_tag5zubl21w8_0",
"DS_9cdtww9pcjqd_0",
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CO_8100370147266377294823567
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CA-9_BB_validation
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[
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"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
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[
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[
3
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2023
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[
1
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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DS_8zft35i5sbgd_0
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CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C59
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C
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DFT-PBE
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MD_1024398629573454165794357
| 2024-08-16T15:04:37 |
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PO_1333045868888587197444140
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[
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"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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CO_8132311688353224469439139
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CA-9_BB_validation
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[
"Daniel Hedman",
"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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MD_1024398629573454165794357
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PO_1133050777765069080610223
| null | null | null |
[
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[
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CO_8236533255795725477512509
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CA-9_BB_validation
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[
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"Fredrik Sandin",
"J. Andreas Larsson",
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
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[
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[
3
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2023
|
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1
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
|
10.60732/95c19122
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DS_8zft35i5sbgd_0
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CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C71
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C
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A
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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2023
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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C54
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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MD_1024398629573454165794357
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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C50
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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CO_8657779427751972236533329
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[
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"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C72
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MD_1024398629573454165794357
| 2024-08-16T14:28:34 |
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[
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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2023
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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C60
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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2023
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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MD_1024398629573454165794357
| 2024-08-16T15:10:33 |
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PO_9342519719262686241007502
| null | null | null |
[
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[
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"DS_07zjrtacjg3z_0",
"DS_8zft35i5sbgd_0"
] | 2023-12-01T23:58:30 |
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CO_8862987378265402434754470
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CA-9_BB_validation
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[
"Daniel Hedman",
"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
] | 1 | 4,003 | 4,003 | 233,034 | 0 | 4,003 | 0 | 4,003 | 0 | 4,003 | -501.735936 | 10,202.734006 | 0 | 2023-12-01T18:52:50 |
[
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[
3
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2023
|
[
1
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
|
10.60732/95c19122
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DS_8zft35i5sbgd_0
|
CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
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C59
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C
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A
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DFT-PBE
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MD_1024398629573454165794357
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PO_9582388832081043273277619
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[
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CA-9_BB_validation
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[
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"Gustav Johansson",
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"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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CO_8903476681955330183475457
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CA-9_BB_validation
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[
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"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
] | 1 | 4,003 | 4,003 | 233,034 | 0 | 4,003 | 0 | 4,003 | 0 | 4,003 | -501.735936 | 10,202.734006 | 0 | 2023-12-01T18:52:50 |
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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[
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"Fredrik Sandin",
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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C60
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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PO_1266933287442126041212762
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[
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[
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CO_9196669884972552051022708
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CA-9_BB_validation
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[
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"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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2023
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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DS_8zft35i5sbgd_0
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MD_1024398629573454165794357
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PO_7030803675375805253501179
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[
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[
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CO_9198354701206386454684409
|
CA-9_BB_validation
|
[
"Daniel Hedman",
"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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CA-9_BB_validation
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[
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"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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[
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"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
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MD_1024398629573454165794357
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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2023
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DFT-PBE
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MD_1024398629573454165794357
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[
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CA-9_BB_validation
|
[
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"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
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MD_1024398629573454165794357
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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2023
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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C63
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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2023
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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10.60732/95c19122
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C71
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
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[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
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C71
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MD_1024398629573454165794357
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
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[
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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] | null | null | null | -508.501116 | null | 3.103047 | 7.369639 |
{"input": {"encut": {"value": 520, "units": "eV"}}, "hash": "102439862957345416579435757980411251184715859364031590652706556386856859773466490977891980138017845683216054395454090990042136855050809363628328039055230", "id": "MD_1024398629573454165794357"}
|
MD_1024398629573454165794357
| 2024-08-16T14:47:40 |
5385640225829355900456818295628380379505428729276833546411269601167270025587482120443071068857332871691475407297749710225169982260721557318186176474643149
|
PO_5385640225829355900456818
| null | null | null |
[
"BR_validation__3326",
"BB_validation__1480",
"CA-9_training__6541"
] |
[
"DS_9cdtww9pcjqd_0",
"DS_fgakfcwhn9zc_0",
"DS_8zft35i5sbgd_0"
] | 2023-12-01T23:56:36 |
991498958172116692094073635899373289486727679158914307295435843560743091444759839402291913647091305318105160298292330801468221022979618005185669008778410
|
CO_9914989581721166920940736
|
CA-9_BB_validation
|
[
"Daniel Hedman",
"Tom Rothe",
"Gustav Johansson",
"Fredrik Sandin",
"J. Andreas Larsson",
"Yoshiyuki Miyamoto"
] |
Binning-binning configurations from CA-9 dataset used during validation step for NNP_BB potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
|
[
"C"
] | 1 | 4,003 | 4,003 | 233,034 | 0 | 4,003 | 0 | 4,003 | 0 | 4,003 | -501.735936 | 10,202.734006 | 0 | 2023-12-01T18:52:50 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
1
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
|
10.60732/95c19122
|
9987403613029629768726893456954031024119887046898145763926846230248531061270033011778359623539974970922272163569034827090035859539661759104921473348571593
|
DS_8zft35i5sbgd_0
|
CA-9_BB_validation__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_8zft35i5sbgd_0
|
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