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Novel and efficient Graph neural network (GNN) for accurate chemical property pre...

Docket 17/843,341, U.S. Patent Application (June 17, 2022)
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Transmittal Letter

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UFYXBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
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Foreign Reference

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UGHXBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
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Electronic Filing System Acknowledgment Receipt

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT52LTMXBLUEX6 (U.S. Pat. App. Jun. 24, 2024)
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Information Disclosure Statement (IDS) Form (SB08)

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UDUXBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
Meredig, B.; Agrawal, A.; Kirklin, S.; Saal, J. E.; Doak, J. W.; Thompson, A.; Zhang, K.; Choudhary, A.; Wolverton, C., Combinatorial screening for new materials in unconstrained composition space with machine learning.
Montavon, G.; Rupp, M.; Gobre, V.; Vazquez-Mayagoitia, A; Hansen, K.; Tkatchenko, A.; Muller, K. R.; von Lilienfeld, O. A., Machine learning of molecular electronic properties in chemical compound space.
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E., Scikit-learn: Machine Learning in Python.
A; Berquist, E.; Brandhorst, K.; Bravaya, K. B.; Brown, S. T.; Casanova, D.; Chang, C. M.; Chen, Y. Q.; Chien, S. H.; Smith, M. B.; Michl, J., Recent Advances in Singlet Fission.
; Sears, J. S.; Yang, B.; Aziz, S. G.; Coropceanu, V.; Bredas, J. L., Theoretical Study of the Local and Charge-Transfer Excitations in Model Complexes of Pentacene-C-60 Using Tuned Range-Separated Hybrid Functionals.
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Foreign Reference

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UE4XBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
[0007] One example of a green emissive moleculeis tris(2-phenylpyridine) iridium, denoted Ir(ppy)s, which hasthe structure: [0008] In this, and later figures herein, we depict the dative bond from nitrogen to metal (here,Ir) as a straight line.
For example, substituents such as alkyl and aryl groups, branched or unbranched, and preferably containing at least 3 carbons, may be used in small molecules to enhancetheir ability to undergo solution processing.
In particular, by containing only one of the dibenzo-substituted pyridine ligands having FORMULAIL, the complexes provided herein will likely have lower sublimation temperatures (correlated with reduced molecular weight and/or weaker intermolecular interactions).
Ry, Re, R3 and Ry may represent mono,di,tri, or tetra substitutions; and each of Ri, Ro, R3 and R4 are independently selected from the group consisting of hydrogen, alkyl having four or fewer carbon atoms, and aryl.
Preferably, in order to make the compoundssublimable and/ or to reduce sublimation temperature, alkyls in the Ry, Ro, R3 and/or Ry positions of Formula I have four or fewer carbon atoms(e.g., methyl, ethyl, propyl, butyl, and isobutyl).
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Foreign Reference

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UE3XBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
... 11 May, 2006 Claims; Par. Nos. [0001], [0102] to [0107], [0189]; (Family: none} Telephone No. x (Konica Minolta Holdings, 1-11 [0076], examples [0078], WO 2007/108362 Al Inc.), 27 September, Claims; Par. examples (Family: none}j ...
), 13 April, 2006 (13.04.06), Claims; Par. Nos. [0001], [0273]; examples (Family: none) JP 2006-096697 A [0124], [0272], Form PCT/ISA/210 (continuation of second sheet) (April 2007) E| eae CO9K11/06 (2006, 01) i, HO11.51/50 (2006.
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Foreign Reference

Document Novel and efficient Graph neural network (GNN) for accurate chemical property prediction, 17/843,341, No. LXT51UGRXBLUEX7 (U.S. Pat. App. Jun. 24, 2024)
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