Revolutionizing Green Chemistry through Artificial Intelligence and its Applications

The integration of Artificial Intelligence (AI) into green chemistry has emerged as a transformative approach for developing sustainable and environmentally benign chemical processes. AI techniques such as machine learning, data analytics, and predictive modeling enable researchers to design eco-friendly synthesis pathways, optimize reaction conditions, reduce waste generation, and minimize energy consumption. By analyzing large chemical datasets, AI can identify greener solvents, catalysts, and reaction mechanisms that enhance efficiency while lowering environmental impact. Furthermore, AI-driven tools accelerate the discovery of sustainable materials and improve lifecycle assessment, supporting the principles of green chemistry. The application of AI also facilitates real-time monitoring and intelligent process control in industrial chemistry, leading to safer and more resource-efficient production systems. Despite challenges such as data availability, model transparency, and computational limitations, the synergy between AI and green chemistry holds significant potential for advancing sustainable innovation.

Artificial Intelligence, Green Chemistry

1. Venkatesan K, Sundarababu J, Anandan SS. The recent developments of green and sustainable chemistry in multidimen-Sional way: current trends and challenges. Green Chem Lett Rev. 2024;17:231–48. [Google Scholar] [Crossref]

2. De Marco BA, Rechelo BS, Tótoli EG, Kogawa AC, Salgado HRN. Evolution of green chemistry and its multidimensional Impacts: a review. Saudi Pharm J. 2019;27:1–8. [Google Scholar] [Crossref]

3. Ahmad T, Zhang D, Huang C, Zhang H, Dai N, Song Y, et al. Artificial intelligence in sustainable energy industry: status quo, Challenges and opportunities. J Clean Prod. 2021;289:125834. [Google Scholar] [Crossref]

4. Jiang X, Luo S, Liao K, Jiang S, Ma J, Jiang J, et al. Artificial intelligence and automation to power the future of chemistry. Cell Rep Phys Sci. 2024;5:102049. [Google Scholar] [Crossref]

5. Onifade M, Zvarivadza T, Adebisi JA, Said KO, Dayo-Olupona O, Lawal AI, et al. Advancing towards sustainability: the emergence of green mining technologies and practices. Green Smart Min Eng. 2024;1:157–74. [Google Scholar] [Crossref]

6. Kumar A, Thakur AK, Gaurav GK, Klemeš JJ, Sandhwar VK, Pant KK, Kumar R. A critical review on sustainable hazardous-Waste management strategies: a step towards a circular economy. Environ Sci Pollut Res Int. 2023;30:105030–55. [Google Scholar] [Crossref]

7. Locatelli M, Kabir A, Perrucci M, Ulusoy S, Ulusoy HI. Ali I. Green profile tools: current status and future perspectives. Adv Sample Prep. 2023;6:100068. [Google Scholar] [Crossref]

8. Jurisch M, Gomes JCL, Parreiras BM, Pimenta JVC, Sepulveda TM, de Macedo AN, et al. Greenness metrics for analytical Methodologies linked to mass spectrometry. Green Anal Chem. 2024;10:100140. [Google Scholar] [Crossref]

9. Schwaller P, Gaudin T, Lanyi D, Bekas C, Laino T. Found in translation: predicting outcomes of complex organic chemistry Reactions using neural sequence-to-sequence models. Chem Sci. 2018;9:6091–8. https://doi.org/10.1039/C8SC02339E. [Google Scholar] [Crossref]

10. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559:547–55. https://doi.org/10.1038/s41586-018-0337-2. [Google Scholar] [Crossref]

11. Moosavi SM, Jablonka KM, Smit B. The role of machine learning in the Understanding and design of materials. J Am Chem Soc. 2020;142:20273–87. https://doi.org/10.1021/jacs.0c09105. [Google Scholar] [Crossref]

12. Farhadi B, You J, Zheng D, Liu L, Wu S, Li J, et al. Machine learning for fast development of advanced energy materials. Next Yadav et al. Discover Chemical Engineering (2026) 6:1 Page 16 of 19 [Google Scholar] [Crossref]

13. Bornscheuer UT, Huisman GW, Kazlauskas RJ, Lutz S, Moore JC, Robins K. Engineering the third wave of biocatalysis. Nature. 2012;485:185–94. https://doi.org/10.1038/nature11117. [Google Scholar] [Crossref]

14. Wu S, Snajdrova R, Moore JC, Baldenius K, Bornscheuer UT. Biocatalysis: enzymatic synthesis for industrial applications. Angew Chem Int Ed. 2021;60:88–119. https://doi.org/10.1002/anie.202006648. [Google Scholar] [Crossref]

15. Elton DC, Boukouvalas Z, Fuge MD, Chung PW. Deep learning for molecular design — a review of the state of the Art. Mol Syst Des Eng. 2019;4:828–49. https://doi.org/10.1039/C9ME00039A. [Google Scholar] [Crossref]

16. Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter Engineering. Science. 2018;361:360–5. https://doi.org/10.1126/science.aat2663. [Google Scholar] [Crossref]

17. Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in Early drug discovery. Pharmaceuticals (Basel). 2023;16:1259. [Google Scholar] [Crossref]

18. Hassija V, Chamola V, Mahapatra A, Singal A, Goel D, Huang K, et al. Interpreting black-box models: a review on explainable Artificial intelligence. Cognit Comput. 2024;16:45–74. [Google Scholar] [Crossref]

19. Kapustina O, Burmakina P, Gubina N, Serov N, Vinogradov V. User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals. Artif Intell Chem. 2024;2:100072. [Google Scholar] [Crossref]

20. Feng F, Li J, Zhang F, Sun J. The impact of artificial intelligence on green innovation efficiency: moderating role of dynamic Capability. Int Rev Econ Financ. 2024;96:103649. [Google Scholar] [Crossref]

21. Olawade DB, Ige AO, Olaremu AG, Ijiwade JO, Adeola AO. The synergy of artificial intelligence and nanotechnology Towards advancing innovation and sustainability — a mini-review. Nano Trends. 2024;8:100052. [Google Scholar] [Crossref]

22. Ncube A, Mtetwa S, Bukhari M, Fiorentino G, Passaro R. Circular economy and green chemistry: the need for radical innovative approaches in the design for new products. Energies. 2023;16:1752. https://doi.org/10.3390/en16041752. [Google Scholar] [Crossref]

23. Aly AA, Górecki T. Green approaches to sample Preparation based on extraction techniques. Molecules. 2020;25:1719. https://doi.org/10.3390/molecules25071719. [Google Scholar] [Crossref]

24. Oliveira JRP, Tusset AM, Andrade DI, Balthazar JM, Pagani RN, Lenzi GG. Action plans study: principles of green chemistry, Sustainable development, and smart cities. Sustainability. 2024;16:8041. https://doi.org/10.3390/su16188041. [Google Scholar] [Crossref]

25. Hermann E, Hermann G, Tremblay JC. Ethical artificial intelligence in chemical research and development: a dual advantage for sustainability. Sci Eng Ethics. 2021;27:45. https://doi.org/10.1007/s11948-021-00325-6. [Google Scholar] [Crossref]

26. Kumar R, Verma A, Shome A, Sinha R, Sinha S, Jha PK, et al. Impacts of plastic pollution on ecosystem services, sustainable Development Goals, and need to focus on circular economy and policy interventions. Sustainability. 2021;13:9963. https://Doi.org/10.3390/su13179963. [Google Scholar] [Crossref]

27. Ray PC, Yu H, Fu PP. Toxicity and environmental risks of nanomaterials: challenges and future needs. J Environ Sci Health C Environ Carcinogen Ecotoxicology Rev. 2009;27:1–35. https://doi.org/10.1080/10590500802708267. [Google Scholar] [Crossref]

28. Billiard KM, Dershem AR, Gionfriddo E. Implementing green analytical methodologies using solid-phase microextraction: a Review. Molecules. 2020;25:5297. https://doi.org/10.3390/molecules25225297. [Google Scholar] [Crossref]

29. El Deeb S, Abdelsamad K, Parr MK. Greener and whiter analytical chemistry using cyrene as a more sustainable and eco-Friendlier mobile phase constituent in chromatography. Pharmaceuticals. 2023;16:1488. https://doi.org/10.3390/ph161014 [Google Scholar] [Crossref]

30. Martínez J, Cortés JF, Miranda R. Green chemistry metrics — a review. Processes. 2022;10:1274. https://doi.org/10.3390/pr10071274. [Google Scholar] [Crossref]

31. Malik S, Muhammad K, Waheed Y. Nanotechnology: a revolution in modern industry. Molecules. 2023;28:661. https://doi.oRg/10.3390/molecules28020661. [Google Scholar] [Crossref]

32. Yigitcanlar T, Mehmood R, Corchado JM. Green artificial intelligence: towards an efficient, sustainable and equitable Technology for smart cities and futures. Sustainability. 2021;13:8952. https://doi.org/10.3390/su13168952. [Google Scholar] [Crossref]

• Green Synthesis of Cobalt Oxide/Gold (Coo/Au) Bimetallic Nanoparticles Using Sinapinic Acid: A Comprehensive Study
• Advances in Solar Cell Technologies: A Comprehensive Review of Material Synthesis, Structural Properties, Efficiency and Diverse Applications
• Thermal Decomposition of Co-Fe-Cr-Citrate Complex Via Structural and Spectral Study
• Surface Activity and Thermodynamic Assessment of Surfactants Derived from Oreochromis Niloticus Oil (Nile Tilapia Fish)
• Green Synthesis of Robust Metal-Organic Frameworks: A Sustainable Approach for Advanced Applications