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| Test configurations with fixed value for dihedral beta in alpha-gamma plane of 120 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules. | [
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| Test configurations with fixed value for dihedral beta in alpha-gamma plane of 120 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules. | [
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| 4 | 2,347 | 2,347 | 63,369 | 0 | 2,347 | 0 | 2,347 | 0 | 2,347 | -17,661.110219 | 0.003184 | 0 | 2023-12-01T18:04:34 | [
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| null | null | null | -17,661.166517 | null | 0.285354 | 3.04027 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | 2024-08-16T15:10:23 | 6268278432476961622381116502443644905521102960046310132085786120198177520811737528625330291989373043918114580171920397065867979978150876770256738133192446 | PO_6268278432476961622381116 | {"dihedrals": [215.0, 120.0, 47.0], "hash": "7138865581566804248406726771728491422947128951538232481617154107366601376812219084400170341186453327568902356413899488601766101291397352140057883484062052", "id": "MD_7138865581566804248406726"} | MD_7138865581566804248406726 | null | [
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| [
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| 2023-12-01T23:04:25 | 4673351914965223159729590927261296999498414517076250003531890095573113683327488355014215879249745323892130834395532119630451778464970183557606814680346339 | CO_4673351914965223159729590 | 3BPA_test_dih_beta120 | [
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"Cas van der Oord",
"Jiri Kucera",
"Alice E. A. Allen",
"Daniel J. Cole",
"Christoph Ortner",
"Gábor Csányi"
]
| Test configurations with fixed value for dihedral beta in alpha-gamma plane of 120 degreesfrom 3BPA dataset. Used to showcase the performance of linear atomic cluster expansion (ACE) force fields in a machine learning model to predict the potential energy surfaces of organic molecules. | [
"C",
"H",
"N",
"O"
]
| 4 | 2,347 | 2,347 | 63,369 | 0 | 2,347 | 0 | 2,347 | 0 | 2,347 | -17,661.110219 | 0.003184 | 0 | 2023-12-01T18:04:34 | [
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| CC-BY-4.0 | {'source-publication': 'https://doi.org/10.1021/acs.jctc.1c00647', 'source-data': 'https://doi.org/10.1021/acs.jctc.1c00647', 'other': None} | 10.60732/09d00e4e | 10185573093864123849716176184559073216212497469109140478569682756756964644641629331520778206484859340812213383948133111023213290861443756269526451083246522 | DS_8cg3pdvxt0pa_0 | 3BPA_test_dih_beta120__Kovács-Oord-Kucera-Allen-Cole-Ortner-Csányi__DS_8cg3pdvxt0pa_0 |
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