Page 107 - Read Online
P. 107
Page 14 of 15 Wang et al. J. Mater. Inf. 2026, 6, 9
59. Kipf, T. N.; Welling, M. Semi-supervised classification with graph convolutional networks. arXiv 2016, arXiv:1609.02907. Available
online: https://doi.org/10.48550/arXiv.1609.02907 (accessed 23 January 2026).
60. Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; Bengio, Y. Graph attention networks. arXiv 2017, arXiv:1710.10903.
Available online: https://doi.org/10.48550/arXiv.1710.10903 (accessed 23 January 2026).
61. Gasteiger, J.; Groß, J.; Günnemann, S. Directional message passing for molecular graphs. arXiv 2020, arXiv:2003.03123. Available
online: https://doi.org/10.48550/arXiv.2003.03123 (accessed 23 January 2026).
62. Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.
Phys. Rev. Lett. 2018, 120, 145301. DOI PubMed
63. Paszke, A.; Gross, S.; Massa, F.; et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural
Information Processing Systems 32 (NeurIPS 2019), Vancouver, Canada, December 8-14, 2019; Curran Associates, Inc.: New York,
NY, USA. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html (accessed 2026-01-23).
64. Fey, M.; Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. arXiv 2019, arXiv:1903.02428. Available online: htt
ps://doi.org/10.48550/arXiv.1903.02428 (accessed 23 January 2026).
65. Topsakal, O.; Akinci, T. C. Creating large language model applications utilizing LangChain: a primer on developing LLM Apps fast.
ICAENS 2023, 1, 1050-6. DOI
66. Abadi, M.; Agarwal, A.; Barham, P.; et al. TensorFlow: large-scale machine learning on heterogeneous systems. 2015. https://www.tens
orflow.org/ (accessed 2026-01-23).
67. Choi, W.; Choudhary, N.; Han, G. H.; Park, J.; Akinwande, D.; Lee, Y. H. Recent development of two-dimensional transition metal
dichalcogenides and their applications. Mater. Today. 2017, 20, 116-30. DOI
68. Zhang, S. S. A review on electrolyte additives for lithium-ion batteries. J. Power. Sources. 2006, 162, 1379-94. DOI
69. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865-8. DOI
PubMed
70. Heyd, J.; Scuseria, G. E.; Ernzerhof, M. Hybrid functionals based on a screened Coulomb potential. J. Chem. Phys. 2003, 118, 8207-15.
DOI
71. Ermolaev, G. A.; Stebunov, Y. V.; Vyshnevyy, A. A.; et al. Broadband optical properties of monolayer and bulk MoS 2 . npj. 2D. Mater.
Appl. 2020, 4, 155. DOI
72. Winther, K. T.; Thygesen, K. S. Band structure engineering in van der Waals heterostructures via dielectric screening: the GΔW method.
2D. Mater. 2017, 4, 025059. DOI
73. Haregewoin, A. M.; Wotango, A. S.; Hwang, B. Electrolyte additives for lithium ion battery electrodes: progress and perspectives.
Energy. Environ. Sci. 2016, 9, 1955-88. DOI
74. Gu, Y.; Wu, X.; Gopalakrishna, T. Y.; Phan, H.; Wu, J. Graphene-like molecules with four zigzag edges. Angew. Chem. Int. Ed. Engl.
2018, 57, 6541-5. DOI PubMed
75. Fried, L. E.; Manaa, M. R.; Pagoria, P. F.; Simpson, R. L. Design and synthesis of energetic materials. Annu. Rev. Mater. Res. 2001, 31,
291-321. DOI
76. Göbel, M.; Klapötke, T. M. Development and testing of energetic materials: the concept of high densities based on the trinitroethyl
functionality. Adv. Funct. Mater. 2009, 19, 347-65. DOI
77. Hong, G.; Gan, X.; Leonhardt, C.; et al. A brief history of OLEDs-Emitter development and industry milestones. Adv. Mater. 2021, 33,
e2005630. DOI
78. Santos PL, Stachelek P, Takeda Y, Pander P. Recent advances in highly-efficient near infrared OLED emitters. Mater. Chem. Front.
2024, 8, 1731-66. DOI
79. Smith, E. L.; Abbott, A. P.; Ryder, K. S. Deep eutectic solvents (DESs) and their applications. Chem. Rev. 2014, 114, 11060-82. DOI
PubMed
80. Hansen, B. B.; Spittle, S.; Chen, B.; et al. Deep eutectic solvents: a review of fundamentals and applications. Chem. Rev. 2021, 121,
1232-85. DOI PubMed
81. Flamme, B.; Rodriguez, Garcia. G.; Weil, M.; et al. Guidelines to design organic electrolytes for lithium-ion batteries: environmental
impact, physicochemical and electrochemical properties. Green. Chem. 2017, 19, 1828-49. DOI
82. Hu, Y.; Yang, X.; Lv, Y.; et al. Identification of potential electrolyte additives via density functional theory analysis. ChemistrySelect
2023, 8, e202300098. DOI
83. Fan, X.; Wang, C. High-voltage liquid electrolytes for Li batteries: progress and perspectives. Chem. Soc. Rev. 2021, 50, 10486-566.
DOI PubMed
84. Gao, P.; Andersen, A.; Sepulveda, J.; et al. SOMAS: a platform for data-driven material discovery in redox flow battery development.
Sci. Data. 2022, 9, 740. DOI PubMed PMC

