- 1.
Peng, C.; Fang, X.; Zeng, R. Research and Development of Hydrocracking Catalysts and Technology. In Catalysis, Spivey, J., Dooley, K.M., Han, Y.F., Eds.; The Royal Society of Chemistry: London, UK, 2016; Volume 28, pp. 86–118.
- 2.
Fukuyama, H.; Terai, S.; Uchida, M.; et al. Active carbon catalyst for heavy oil upgrading. Catal. Today 2004, 98, 207–215.
- 3.
Peng, C.; Zhou, Z.; Cheng, Z.; et al. Upgrading of light cycle oil to high-octane gasoline through selective hydrocracking over non-noble metal bifunctional catalysts. Energy Fuels 2019, 33, 1090–1097.
- 4.
Sun, J.A.; Selvam, E.; Bregvadze, A.; et al. Hydrocracking of polyolefins over ceria-promoted Ni/BEA catalysts. Green Chem. 2025, 27, 3905–3915.
- 5.
Vance, B.C.; Yuliu, Z.; Najmi, S.; et al. Unlocking naphtha from polyolefins using Ni-based hydrocracking catalysts. Chem. Eng. J. 2024, 487, 150468.
- 6.
Yan, J.; Li, G.; Lei, Z.; et al. Upcycling polyolefins to methane-free liquid fuel by a Ru1-ZrO2 catalyst. Nat. Commun. 2025, 16, 2800.
- 7.
Zhan, J.; Li, L.; Dai, R.; et al. Engineering porous beta zeolite-encapsulated nickel catalyst for waste polyolefins upcycling. Appl. Catal. B Environ. 2025, 373, 125359.
- 8.
Kristensen, T.; Hulteberg, C.; Blomberg, S.; et al. Parametric analysis and optimization of vanillin hydrodeoxygenation over a sulfided Ni-Mo/δ-Al2O3 catalyst under continuous-flow conditions. Top. Catal. 2023, 66, 1341–1352.
- 9.
Li, X.; Wang, Q.; Wu, Y.; et al. Optimization of key parameters using RSM for improving the production of the green biodiesel from FAME by hydrotreatment over Pt/SAPO-11. Biomass Bioenergy 2022, 158, 106379.
- 10.
Pang, Z.; Huang, P.; Lian, C.; et al. Data-driven prediction of product yields and control framework of hydrocracking unit. Chem. Eng. Sci. 2024, 283, 119386.
- 11.
Ma, Q.; Nie, H.; Yang, P.; et al. Insights into structure-activity relationships between Y zeolites and their n-C10 hydrocracking performances via machine learning approaches. Chin. J. Catal. 2025, 71, 187–196.
- 12.
Wang, W.; Li, M.; Zhang, Y.; et al. Structure-performance relationship between zeolites properties and hydrocracking performance of tetralin over NiMo/Al2O3-Y catalysts: A machine-learning-assisted study. Fuel 2025, 390, 134652.
- 13.
Oberhausen, C.M.; Auchenbach, K.E.; Vlachos, D.G. Investigating the role of acid sites in the hydrocracking of polyethylene-EVOH multilayer film waste over Pt/BEA Catalyst. Chem. Eng. J. 2025, 508, 160869.
- 14.
Wang, J.; Yan, J.; Cui, Q.; et al. Effect of SiO2 support particle sizes on the performance of FeZn catalysts in VR slurry-phase hydrocracking. Catal. Today 2025, 449, 115183.
- 15.
Zhang, K.; Hu, Z.; Ren, L.; et al. Research on the process of naphtha hydrocracking to chemical materials. Carbon Resour. Convers. 2025, 8, 100315.
- 16.
Mroz, A.M.; Basford, A.R.; Hastedt, F.; et al. Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry. Chem. Soc. Rev. 2025, 54, 5433–5469.
- 17.
Tkatchenko, A. Machine learning for chemical discovery. Nat. Commun. 2020, 11, 4125.
- 18.
Yang, L.; Guo, Q.; Zhang, L. AI-assisted chemistry research: A comprehensive analysis of evolutionary paths and hotspots through knowledge graphs. Chem. Commun. 2024, 60, 6977–6987.
- 19.
Fu, Z.; Huang, P.; Wang, X.; et al. Artificial intelligence-assisted ultrafast high-throughput screening of high-entropy hydrogen evolution reaction catalysts. Adv. Energy Mater. 2025, 2500744.
- 20.
Wei, C.; Shi, Y.; Mu, W.; et al. Large language models assisted materials development: Case of predictive analytics for oxygen evolution reaction catalysts of (Oxy)hydroxides. ACS Sustain. Chem. Eng. 2025, 13, 5368–5380.
- 21.
Zheng, Z.; Florit, F.; Jin, B.; et al. Integrating machine learning and large language models to advance exploration of electrochemical reactions. Angew. Chem. Int. Ed. 2025, 64, e202418074.
- 22.
Stella, F.; Della Santina, C.; Hughes, J. How can LLMs transform the robotic design process? Nat. Mach. Intell. 2023, 5, 561–564.
- 23.
Vemprala, S.H.; Bonatti, R.; Bucker, A.; et al. ChatGPT for robotics: Design principles and model abilities. IEEE Access 2024, 12, 55682–55696.
- 24.
Jablonka, K.M.; Schwaller, P.; Ortega-Guerrero, A.; et al. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 2024, 6, 122–123.
- 25.
Su, Y.; Wang, X.; Ye, Y.; et al. Automation and machine learning augmented by large language models in a catalysis study. Chem. Sci. 2024, 15, 12200–12233.
- 26.
Zheng, Z.; Rong, Z.; Rampal, N.; et al. A GPT-4 reticular chemist for guiding MOF discovery. Angew. Chem. Int. Ed. 2023, 62, e202311983.
- 27.
Chen, B.; Zhang, Z.; Langrené, N.; et al. Unleashing the potential of prompt engineering for large language models. Patterns 2025, 6, 101260.
- 28.
Luo, F.; Zhang, J.; Wang, Q.; et al. Leveraging prompt engineering in large language models for accelerating chemical research. ACS Cent. Sci. 2025, 11, 511–519.
- 29.
Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature 1986, 323, 533–536.
- 30.
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.
- 31.
Lecun, Y.; Bottou, L.; Bengio, Y.; et al. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278–2324.
- 32.
Selvaraju, R.R.; Cogswell, M.; Das, A.; et al. Grad-CAM: Visual Explanations from Deep Networks Via Gradient-Based Localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017.