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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:54:19 | 12633337454287455759145870216568180440822254672159736556629721783537919756011824993230603197261437298603141117279797402336586265595939901146482341071391040 | CO_1263333745428745575914587 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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| 2023-12-01T22:54:19 | 12664069298612149853321858050396992555520560693753560400961037801921004043280095415482488305238574822892619743736255597208591711571571012089263531950238521 | CO_1266406929861214985332185 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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| 2023-12-01T22:54:19 | 12780507956864702652108821546913690293704367713311482084384413157975679820485331020757891694451374883074718013968279562633454370658948786610114344686957570 | CO_1278050795686470265210882 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| 2023-12-01T22:54:19 | 12865666428591559396001055972663354857023681064720068669164603052941302463061918616357516246319879408479201764693763766338209470954284360809017038498169767 | CO_1286566642859155939600105 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:54:19 | 13206738784194518614005988307867004578563616065695537168959376980386384400714934679061795508068243608378296905800504143821985916404908547794670803369125188 | CO_1320673878419451861400598 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| true | null | null | 23.960024 | null | 3.757778 | 7.175194 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:14:35 | 11520686055015955404342893579223183844015302957550866166052802616524317846452322423312541007992875873896902407858609118338382820668295673960145636320480031 | PO_1152068605501595540434289 | {"materials-project-id": "mp-23153", "temperature": 420, "hash": "3571143252367592943292734308767478741631058341465699072692346118474668104552841293857544432576409953248442026102484375624596587164967844387116473887586712", "id": "MD_3571143252367592943292734"} | MD_3571143252367592943292734 | null | [
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| [
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| 2023-12-01T22:54:19 | 13366986334992276888507664051459671191737791701394201790671484792429296815118390143032583830651979259235226388172148276029934410454704508862825103303569497 | CO_1336698633499227688850766 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
I17 | I | A | [
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| [
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| [
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| [
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| 3 | 6073513251232235751806256264526851938439680407542540723775597825891363301239581643506584253984630755501506570042006017965729730567532138951436629671774617 | 1 | VASP | DFT-PBE | null | [
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| true | null | null | 5.419083 | null | 0.488853 | 0.929362 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:59:34 | 4649091672889864397330582289671695857783738675448079052531487651445716309829713657176646865760166810157074886349284213129428871340104493242412936235657276 | PO_4649091672889864397330582 | {"materials-project-id": "mp-684663", "temperature": 105, "hash": "4078305070143412340585116292351889474107034974188880088291519147744351659104812726359003632554910134187359767244777063068988788921274400436003107985076184", "id": "MD_4078305070143412340585116"} | MD_4078305070143412340585116 | null | [
"I_iter_all_I_iter1_420_4_mp-684663_interstitials_BCC2_db111_105_1",
"I_iter_all_I_iter1_420_4_mp-684663_interstitials_BCC2_db111_105_6",
"I_iter_all_I_iter1_420_4_mp-684663_interstitials_BCC2_db111_105_10",
"I_iter_all_I_iter1_420_4_mp-684663_interstitials_BCC2_db111_105_12"
]
| [
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]
| 2023-12-01T22:54:19 | 13367219831878901337706860782731314594298254245443861672875414524305163690590542082059227037475876103132630654656232755294081824979641998695503011597720875 | CO_1336721983187890133770686 | 23-Single-Element-DNPs_RSCDD_2023-I | [
"Christopher M. Andolina",
"Wissam A. Saidi"
]
| Configurations of I from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
"I"
]
| 1 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
1
]
| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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