Віктор МАЛИШЕВ1, Юрій ЛІПСЬКИЙ2, Ангеліна ГАБ1, Дмитро ШАХНІН1
1ПЗВО «Міжнародний Європейський Університет» просп. Акад. Глушкова, буд. 42Б, 03187 Київ, Україна еmail: viktor.malyshev.igic@gmail.com 22Національна академія статистики, обліку та аудиту вул. Підгірна, 1, 04107 Київ, Україна e-mail: lipskyy@mineralis.com.ua
DOI: https://doi.org/10.37827/ntsh.chem.2025.78.007
СВІТОВИЙ РИНОК ШТУЧНОГО ІНТЕЛЕКТУ В ХІМІЧНІЙ ПРОМИСЛОВОСТІ
Об’єктом дослідження є загальна характеристика світового ринку штучного інтелекту в хімічній промисловості. Підтверджено, що підприємства та установи хімічної промисловості все більше використовують штучний інтелект для бізнесу і наукових пошуків. Штучний інтелект ширше застосовують у хімічній промисловості завдяки використанню прогнозної аналітики для передбачення можливих небезпек і проактивного їх зменшення, а також створенню реалістичного досвіду віртуальної реальності, що імітує небезпечні ситуації. Впровадженням штучного інтелекту в цій сфері є дослідження та розвиток, виробництво, прогнозування та планування, управління ризиками. Аналіз наукових публікацій щодо ринку штучного інтелекту в хімічній промисловості свідчить про значну його інтеграцію в процеси хімічного виробництва. Прогнозується, що ринок буде зростати з середньорічним сукупним темпом зростання 32 % у період з 2025 до 2034 року. Для проведення маркетингового аналізу застосовано метод пошуку літературних даних і метод аналізу. Аналіз ринку штучного інтелекту в хімічній промисловості проведено за такими сегментами: географічні регіони, тип розгортання, призначення витрат, застосування, призначення, кінцеве використання, перспективні технології. Визначено чинники зростання ринку, тенденції та можливості ринку, потенційний попит і обсяг ринків різних країн світу, динаміку та конкуренцію на світовому ринку. Оцінено обсяги та динаміку світових ринків хімічної продукції (агрохімікатів, хімічної продукції, каталізаторів, спецхімікатів), обладнання (сенсорів, приладо-будування), інтернету речей.
Ключові слова: штучний інтелект, хімічна промисловість, світовий ринок, сегментний аналіз, тенденції.
Література:
-
1. Artificial Intelligence in Society, OECD Publishing, Paris, 2019. (https://doi.org/10.1787/eedfee77-en).
2. Top 20 Applications of Artificial Intelligence (AI) in 2025.
(https://www.geeksforgeeks.org/applications-of-ai/).
3. Malyshev V., Gab A., Shakhnin D., Lipsky Y. Research of the Global Higher Education Market and of the Use of
Artificial Intelligence in this Field. EUREKA: Social and Humanities. 2024. No. 5. P. 28–41.
(https://doi.org/10.21303/2504-5571.2024.003663).
4. Malyshev V., Lipskyi Y., Kovalenko V., Gab A., Shakhnin D., Orel O. Assessment of the Global Artificial
Intelligence Market in Healthcare. Technology Audit and Production Reserves. 2024. No. 6/4 (80). P. 62–70.
(https://doi.org/10.15587/2706-5448.2024.316451).
5. Ding H., Hua P., Huang Z. Survey on Recent Progress of AI for Chemistry: Methods, Applications, and
Opportunities. arXiv:2502.17456v1 [physics.chem-ph] (https://doi.org/10.48550/arXiv.2502.17456).
6. Baum Z. J., Yu X., Ayala P. Y., Zhao Y., Watkins S. P., Zhou Q. Artificial Intelligence in Chemistry: Current
Trends and Future Directions. J. Chem. Inf. Model. 2021. Vol. 61(7). P. 3197–3212.
(https://doi.org/10.1021/acs.jcim.1c00619).
7. Lin S., Krishnan V., Womack D. Optimizing the chemicals value chain with AI. IBM Institute for Business Value.
2020. 59035359USEN-00.(https://www.ibm.com/thought-leadership/institute-business-value/report/chemicals-value-chain-ai).
8. Laska M., Karwala I. Artificial intelligence in the chemical industry – risks and opportunities. Scientific
Papers of Silesian University of Technology. Organization and Management Series. 2023. No. 172. P. 403–416.
(http://doi.org/10.29119/1641-3466.2023.172.25).
9. How AI is transforming chemistry research.
(https://www.chemistryworld.com/research/how-ai-is-transforming-chemistry-research/4020650.article).
10. Insights 2024: Attitudes toward AI. Elsevier. 2024.
(https://www.elsevier.com/insights/attitudes-toward-ai).
11. Shen R., Su W. Review of the Applications of AI in the Process Analysis and Optimization of Chemical Products.
Pharmaceutical Fronts. 2023. Vol. 5(4). e219.
(https://doi.org/10.1055/s-0043-1777425).
12. Roles of generative AI in drug discovery: advantages, case studies, and examples. geeksforgeeks.org 2024.
(https://www.geeksforgeeks.org/roles-of-generative-ai-in-drug-is-discovery-advantages-case-studies-and-examples/).
13. AI in Chemicals Market Size to Reach USD 28 Billion by 2034. 2025.
(https://www.globenewswire.com/news-release/2025/02/20/3029322/0/en/AI-in-Chemicals-Market-Size-to-Reach-USD-28-Billion-by-2034.htm).
14. Dhapte A. AI in Chemicals Market Research Report By Application (Process Optimization, Predictive Maintenance,
Quality Control, Supply Chain Management), By Technology (Machine Learning, Natural Language Processing, Robotics
Process Automation, Deep Learning), By End Use Industry (Petrochemical, Pharmaceutical, Agricultural Chemicals,
Specialty Chemicals), By Deployment Type (On-Premises, Cloud-Based, Hybrid) and By Regional (North America,
Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2035. 2025. MRFR/ICT/20607-HCR.
(https://www.marketresearchfuture.com/reports/ai-in-chemicals-market-22207).
15. AI in Chemicals Market Size, Share & Trends Analysis Report by Type (Hardware, Software, and Services),
Application (Molecule Design, Retrosynthesis, Reaction Outcome Prediction, Reaction Conditions Prediction, and
Chemical Reaction Optimization), End-Use (Base Chemicals and Petrochemicals, Specialty Chemicals, and
Agrochemicals), and By Region (North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2025–2033.
2025.
(https://straitsresearch.com/report/artificial-intelligence-in-chemicals-market).
16. Artificial Intelligence (AI) in Chemicals Market Size, Share, Report 2025 to 2034. Market Research Reports,
Cervicorn Consulting, 2025, Report Code: 2361.
(https://www.cervicornconsulting.com/artificial-intelligence-ai-in-chemicals-market).
17. Artificial Intelligence (AI) Market Size, Share, and Trends 2025 to 2034. ICT, Precedence Research, 2025,
Report Code: 1635.
(https://www.precedenceresearch.com/artificial-intelligence-market).
18. Artificial Intelligence (AI) Software Market Size: 2024 to 2030. ABI Research, 2025, MD-AISOFT-103.
(https://www.abiresearch.com/news-resources/chart-data/report-artificial-intelligence-market-size-global).
19. AI Data Management Market Size, Share & Trends Analysis Report By Deployment, By Offering, By Data Type, By
Application, By Technology, By Vertical (BFSI, Retail & E-commerce), By Region, And Segment Forecasts, 2024 –
2030. Grand View Research, 2025, Report ID:
GVR-4-68040-355-0.
(https://www.grandviewresearch.com/industry-analysis/ai-data-management-market-report).
20. Data Science Platform Market Size, Share, and Trends 2024 to 2034. Precedence Research, 2024, Report Code:
2030.
(https://www.precedenceresearch.com/data-science-platforms-market).
21. Laboratory Informatics Market Size, Share, and Trends 2025 to 2034. Precedence Research, 2025, Report Code:
1536.
(https://www.precedenceresearch.com/laboratory-informatics-market).
22. Serrano D. R., Luciano F. C., Anaya B. J., Ongoren B., Kara A., Molina G., Ramirez B. I., Sánchez-Guirales S.
A., Simon J. A., Tomietto G., Rapti C., Ruiz H. K. Rawat S., Kumar D., Lalatsa A. Artificial Intelligence (AI)
Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024. Vol.
16(10). 1328.
(https://doi.org/10.3390/pharmaceutics16101328).
23. AI In Chemicals Market Size, Share & Trends Analysis Report By Type (Hardware, Software, Services), By
Application (Production Optimization, New Material Innovation), By End-use, By Region, And Segment Forecasts, 2024
– 2030. Grand View Research, 2025, Report ID: GVR-4-68040-444-9.
(https://www.grandviewresearch.com/industry-analysis/ai-chemicals-market-report).
24. Artificial Intelligence (AI) in Chemicals Market By Type (Software, Hardware, and Services), By Technology, By
Application, By End User - Global Industry Outlook, Key Companies (Manuchar N.V, IMCD N.V., Univar Solutions Inc.,
and Others), Trends and Forecast 2024-2033. Dimension Market Research, 2024, Report Code: RC-897.
(https://dimensionmarketresearch.com/report/artificial-intelligence-in-chemicals-market/).
25. Granda J. M., Donina L., Dragone V., Long D.-L., Cronin L. Controlling an organic synthesis robot with machine
learning to search for new reactivity. Nature. 2018. Vol. 559. P. 377–381.
(https://doi.org/10.1038/s41586-018-0307-8).
26. Dabas R. Twenty ways AI is advancing chemistry. Chemistry World, 2024.
(https://www.chemistryworld.com/news/twenty-ways-ai-is-advancing-chemistry/4020269.article).
27. Dhudum R., Ganeshpurkar A., Pawar A. Revolutionizing Drug Discovery: A Comprehensive Review of AI
Applications. Drugs Drug Candidates. 2024. Vol. 3. P. 148–171.
(https://doi.org/10.3390/ddc3010009).
28. Ferreira F. J. N., Carneiro A. S. AI-Driven Drug Discovery: A Comprehensive Review. ACS Omega. 2025. Vol. 10.
P. 23889–23903.(https://doi.org/10.1021/acsomega.5c00549).
29. Tetko I. V., Engkvist O. From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J.
Cheminform. 2020. Vol 12. Article number: 74.
(https://doi.org/10.1186/s13321-020-00475-y).
30. Ottah D., Ngwu C. An Overview Of AI And Big Data Analytics In Revolutionizing Chemistry. Maiden AbiaChem
Conference. 2024.(https://doi.org/10.13140/RG.2.2.19001.58723).
31. Jiang X., Luo S., Liao K., Jiang S., Ma J., Jiang J., Shuai Z. Artificial intelligence and automation to power
the future of chemistry. Cell Reports Physical Science. 2024. Vol. 7. P. 102049.
(https://doi.org/10.1016/j.xcrp.2024.102049).
32. Bai J., Cao L., Mosbach S., Akroyd J., Lapkin A.A., Kraft M. From Platform to Knowledge Graph: Evolution of
Laboratory Automation. JACS Au. 2022. Vol. 2. P. 292–309.
(https://doi.org/10.1021/jacsau.1c00438).
33. Palanisami G. IoT in Chemistry Practical's. LimkedIn. 2024.
(https://www.linkedin.com/pulse/iot-chemistry-practicals-ganeshkumar-palanisamy-qb4nc).
34. Kreitlein S. Integrating AI: How to Incorporate Artificial Intelligence Into the Laboratory of the Future.
2024.
(https://www.labcompare.com/10-Featured-Articles/613922-Integrating-AI-How-to-Incorporate-Artificial-Intelligence-into-the-Laboratory-of-the-Future/).
35. Cardoso R. R. AI in analytical chemistry: Advancements, challenges, and future directions. Talanta. 2024. Vol.
274. Article 125949.
(https://doi.org/10.1016/j.talanta.2024.125949).
36. The role of Artificial Intelligence (AI) in Analytical Chemistry. EVISA. 2025.
(https://speciation.net/News/The-role-of-Artificial-Intelligence-AI-in-Analytical-Chemistry-;~/2025/02/12/10907.html).
37. Guo K., Shen Y., Gonzalez-Montiel G. A., Huang Y., Zhou Y., Surve M., Guo Z., Das P., Chawla N. V., Wiest O.,
Zhang X. Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond.
cs.AI arXiv:2502.09897.
(https://doi.org/10.48550/arXiv.2502.09897).
38. Workman Jr. J., Mark H. Artificial Intelligence in Analytical Spectroscopy, Part II: Examples in Spectroscopy.
Spectroscopy. 2023. Vol. 38. P. 10–15.
(https://doi.org/10.56530/spectroscopy.js8781e3).
39. Choi N., Kim H. Technological Convergence of Blockchain and Artificial Intelligence: A Review and Challenges.
Electronics. 2025. Vol. 14. Article 84.
(https://doi.org/10.3390/electronics14010084).
40. Taherdoost H. Blockchain Technology and Artificial Intelligence Together: A Critical Review on Applications.
Appl. Sci. 2022. Vol. 12. Article 12948.
(https://doi.org/10.3390/app122412948).
41. Artificial Intelligence and Blockchain: Insights and Actions for the Chemical Industry. CEFIC. 2019.
(https://cefic.org/resources/artificial-intelligence-and-blockchain-insights-and-actions-for-the-chemical-industry/).
42. Mitra S. M., D'Costa J. A., Sami M. M., Nibir M. M. H., Rahman M. A. Secure Blockchain and AI-Based Decision
Making for Chemical Supply Chain Management. 2024 International Conference on Advances in Computing,
Communication, Electrical, and Smart Systems (iCACCESS), Dhaka, Bangladesh. 2024. P. 1–6.
(https://doi.org/10.1109/iCACCESS61735.2024.10499490).
43. Peterson L., Gosea I. V., Benner P., Sundmacher K. Digital twins in process engineering: An overview on
computational and numerical methods. SSRN. 2024.
(https://doi.org/10.2139/ssrn.4747265).
44. Mane S., Dhote R. R., Sinha A., Thirumalaiswamy R. Digital twin in the chemical industry: A review. Digital
Twin and Applications. 2024. Vol. 1. P. 118–130.
(https://doi.org/10.1049/dgt2.12019).
45. Lampropoulos G. Combining Artificial Intelligence with Augmented Reality and Virtual Reality in Education:
Current Trends and Future Perspectives. Multimodal Technol. Interact. 2025. Vol. 9. Article 11.
(https://doi.org/10.3390/mti9020011).
46. von Ende E., Ryan S., Crain M. A., Makary M.S. Artificial Intelligence, Augmented Reality, and Virtual Reality
Advances and Applications in Interventional Radiology. Diagnostics. 2023. Vol. 13. Article 892.
(https://doi.org/10.3390/diagnostics13050892).
47. Ramos M. C., Collison C. J., White A. D. A review of large language models and autonomous agents in chemistry.
Chem. Sci. 2025. Vol. 16. P. 2514–2572.
(https://doi.org/10.1039/D4SC03921A).
48. Thorne C., Akhondi S. NLP for Chemistry – Introduction and Recent Advances. In: Proceedings of the 2024 Joint
International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024):
Tutorial Summaries. Torino, Italia. ELRA and ICCL. 2024. P 45–49.
(https://aclanthology.org/2024.lrec-tutorials.8/).
49. Yingngam B. Chapter 5. AI in Predictive Toxicology. In: AI-Powered Advances in Pharmacology. IGI Global, 2025.
P. 56.
(https://doi.org/10.4018/979-8-3693-3212-2.ch005).
50. Masarone S., Beckwith K. V., Wilkinson M. R., Tuli S., Lane A., Windsor S., Lane J., Hosseini-Gerami L.
Advancing predictive toxicology: overcoming hurdles and shaping the future. Digital Discovery. 2025. Vol. 4. P.
303–315.
(https://doi.org/10.1039/D4DD00257A).
51. Valavanidis A. Artificial Intelligence and Green Chemistry. High-impact synergies between green chemistry
fields and artificial intelligence. 2024. Vol. 1. P. 1–38
(https://www.researchgate.net/publication/381377427_Artificial_Intelligence_and_Green_Chemistry_High-impact_synergies_between_green_chemistry_fields_and_artificial_intelligence).
52. Ghasemlou M., Nguyen H. C., Talekar S., Pfeffer F. M., Barrow C. J. Artificial Intelligence (AI) for More
Sustainable Chemistry and a Greener Future. ACS Sustainable Chemistry & Engineering. 2025. Vol. 13. P. 3830–3833.
(https://doi.org/10.1021/acssuschemeng.5c00853).
53. Joshi R. P., Kumar N. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Molecules. 2021.
Vol. 26. Article 6761.
(https://doi.org/10.3390/molecules26226761).
54. Weiser B. Artificial intelligence for molecular design. A Bachelor thesis submitted to Carleton Univeristy,
Ottawa, Ontario, Canada, 2025.
www.researchgate.net/publication/350710022_ARTIFICIAL_INTELLIGENCE_FOR_MOLECULAR_DESIGN.
55. Park J., Kang D. Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis
Across Multiple Industries. Appl. Sci. 2024. Vol. 14. Article 11934.
(https://doi.org/10.3390/app142411934).
56. Singer G., Cohen Y. A framework for smart control using machine-learning modeling for processes with
closed-loop control in Industry 4.0. Engineering Applications of Artificial Intelligence. 2021. Vol. 102. Article
104236.
(https://doi.org/10.1016/j.engappai.2021.104236).
57. Ma J., Sheridan R. P., Liaw A., Dahl G. E., Svetnik V. Deep Neural Nets as a Method for Quantitative
Structure−Activity Relationships. J. Chem. Inf. Model. 2015. Vol. 55. P. 263–274.
(https://doi.org/10.1021/ci500747n).
58. Matsuzaka Y., Uesawa Y. Computational Models That Use a Quantitative Structure–Activity Relationship Approach
Based on Deep Learning. Processes. 2023. Vol. 11. Article 1296.
(https://doi.org/10.3390/pr11041296).
59. Ma Z., Cui P., Wang X., Li L., Xu H., Fisher A., Cheng D. The integration of artificial intelligence and
high-throughput experiments: an innovative driving force in catalyst design. Chinese Journal of Chemical
Engineering. 2025. Vol. 84. P. 117–132
(https://doi.org/10.1016/j.cjche.2025.04.012).
60. Callaghan S. Toward machine learning-enhanced high-throughput experimentation for chemistry. Patterns (New
York, N.Y.). 2021. Vol. 2. Article 100221.
(https://doi.org/10.1016/j.patter.2021.100221).
61. Jain D., Dwadasi B. S., Kumar D., Mishra S., Ravikumar B., Gupta R., Srinivasan S. G., Jain V., Mynam M.,
Maiti S., Rai B. Materials Design in Digital Era: Challenges and Opportunities. Transactions of the Indian
Institute of Metals. 2019. Vol. 72. P. 1–10.
(https://doi.org/10.1007/s12666-019-01702-3).
62. Papadimitriou I., Gialampoukidis I., Vrochidis S., Kompatsiaris I. AI methods in materials design, discovery
and manufacturing: A review. Computational Materials Science. 2024. Vol. 235. Article 112793.
(https://doi.org/10.1016/j.commatsci.2024.112793).
63. Shen R., Su W. A Review of the Applications of Artificial Intelligence in the Process Analysis and
Optimization of Chemical Products. Pharmaceutical Fronts. 2023. Vol. 05. P. e219–e226.
(https://doi.org/10.1055/s-0043-1777425).
64. He C., Zhang C., Bian T., Jiao K., Su W., Wu K.-J., Su A. A Review on Artificial Intelligence Enabled Design,
Synthesis, and Process Optimization of Chemical Products for Industry 4.0. Processes. 2023. Vol. 11. Article 330.
(https://doi.org/10.3390/pr11020330).
65. Thakkar A., Watson T. J., Johansson S., Jorner K. Artificial intelligence and automation in computer aided
synthesis planning. Reaction Chemistry & Engineering. 2021. Vol. 6. P. 27–51.
(https://doi.org/10.1039/D0RE00340A).
66. Shen Y., Borowski J. E., Hardy M.A., et al. Automation and computer-assisted planning for chemical synthesis.
Nat. Rev. Methods Primers. 2021. Vol. 1. Article 23.
(https://doi.org/10.1038/s43586-021-00022-5).
67. Stefaniu A., Rasul A., Hussain G. Cheminformatics and its Applications. IntechOpen. 2020. Vol. 1.
(https://doi.org/10.5772/intechopen.83236).
68. de Jesus P. Cheminformatics: The Digital Revolution in Chemistry. ChemCopilot. 2025.
(https://www.chemcopilot.com/blog/cheminformatics).
69. Saifi I., Bhat B. A., Hamdani S. S., Bhat U. Y., Lobato-Tapia C. A., Mir M. A., Dar T. U. H., Ganie S. A.
Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science.
Journal of Biomolecular Structure and Dynamics. 2024. Vol. 42. P. 6523–6541.
(https://doi.org/10.1080/07391102.2023.2234039).
70. Huan L. Original review of quantum chemistry and 3D modeling of artificial intelligence. Journal of Quantum
Physics and Materials Chemistry. 2023. Vol. 11.
(https://doi.org/10.58473/JQPMC0013).
71. Swayne M. Study Introduces an AI Agent That Automates Quantum Chemistry Tasks from Natural Language Prompts.
QuantumInsider. 2025.
(https://thequantuminsider.com/2025/05/07/study-introduces-an-ai-agent-that-automates-quantum-chemistry-tasks-from-natural-language-prompts/).
How to Cite
МАЛИШЕВ В., ЛІПСЬКИЙ Ю., ГАБ А., ШАХНІН Д. СВІТОВИЙ РИНОК ШТУЧНОГО ІНТЕЛЕКТУ В ХІМІЧНІЙ ПРОМИСЛОВОСТІ. Праці НТШ. Хім. Наук. 2025. Т. 78. С. 7-32.