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化學生成AI的全球市場的評估:模式，各用途，各終端用戶，各地區，機會，預測(2016年～2030年)
市場調查報告書
商品編碼
1347130

化學生成AI的全球市場的評估:模式，各用途，各終端用戶，各地區，機會，預測(2016年～2030年)

Generative AI in Chemical Market Assessment, By Model, By Applications, By End-user, By Region, Opportunities and Forecast, 2016-2030F
出版日期: | 出版商: Market Xcel - Markets and Data | 英文 122 Pages | 商品交期: 3-5個工作天內

價格

據預測，全球化學品生成人工智慧市場規模將從 2022 年的 1.512 億美元增至 2030 年的 9.364 億美元，在 2023-2030 年預測期內複合年增長率為 25.6%。

新冠肺炎 (COVID-19) 的影響

COVID-19 大流行的高峰期造成了極大的破壞性，人們因 COVID-19 病毒而死亡。為了根除 COVID-19 病毒，科學家們製造了一個可怕的局面，迫使他們釋放藥物和疫苗。生成式人工智慧在藥物發現中發揮重要作用，因為使用手動方法不可能在有限的時間內開發出疫苗。透過使用化學分子及其特性的數據集，並在這些數據集上實施生成人工智慧模型，能夠推導出可以減少 COVID-19 病毒在全球傳播的相關化學分子。事實上，生成式人工智慧作為一種在短時間內發現新藥物和化學分子的強大人工智慧工具正在吸引科學家的注意。

俄羅斯-烏克蘭戰爭的影響

俄羅斯吞併烏克蘭具有前所未有的全球影響，也是全球經濟的擔憂。供應鏈中斷和新穎的技術創新是入侵的一些負面後果。戰爭降低了生成式人工智慧新創公司的利潤，促使整個化學產業對生成式人工智慧的投資減少。西方國家對俄羅斯實施的製裁迫使這些國家開發自己的化學品和藥品。2023 年，俄羅斯量子中心透過在 ChEMBL 資料集上實施生成式 AI 模型，成功產生了 2,331 個具有藥物特性的新化學結構。因此，戰爭影響並阻礙了這些新創公司和公司在生成人工智慧市場和化學市場的發展。

本報告提供全球化學生成AI市場相關調查分析，提供市場規模與預測，市場動態，主要企業的形勢及預測等資訊。

目錄

第1章 調查手法

第2章 計劃的範圍和定義

第3章 化學生成AI市場上COVID-19的影響

第4章 俄羅斯·烏克蘭戰爭的影響

第5章 摘要整理

第6章 客戶的迴響

  • 市場認知度與產品資訊
  • 品牌的認知度和忠誠度
  • 決定購買時考慮的要素
  • 購買頻率
  • 購買媒體

第7章 化學生成AI市場預測(2016年～2030年)

  • 市場規模與預測
    • 金額
  • 各模式
    • Deep學習
    • 自然語言處理
    • 識別的模式
    • 強化學習
    • 其他
  • 各用途
    • 複雜結構的預測
    • 新配合的最佳化
    • 化學流程的最佳化
    • 即時設備監測
    • 生產能力的最佳化
    • 價格設定的最佳化
    • 實驗室自動化
    • 其他
  • 各終端用戶
    • 化學處理產業
    • 研究開發
    • 其他
  • 各地區
    • 北美
    • 歐洲
    • 南美
    • 亞太地區
    • 中東·非洲
  • 市場佔有率:各企業(2022年)

第8章 化學生成AI市場預測:各地區(2016年～2030年)

  • 北美
    • 各模式
    • 各用途
    • 各終端用戶
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 法國
    • 義大利
    • 英國
    • 俄羅斯
    • 荷蘭
    • 西班牙
    • 土耳其
    • 波蘭
  • 南美
    • 巴西
    • 阿根廷
  • 亞太地區
    • 印度
    • 中國
    • 日本
    • 澳洲
    • 越南
    • 韓國
    • 印尼
    • 菲律賓
  • 中東·非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第9章 市場製圖(2022年)

  • 各模式
  • 各用途
  • 各終端用戶
  • 各地區

第10章 宏觀環境和產業結構

  • 供需分析
  • 進出口的分析
  • 支援/價值鏈分析
  • PESTEL分析
  • 波特的五力分析

第11章 市場動態

  • 成長促進因素
  • 阻礙成長的要素(課題，阻礙因素)

第12章 主要企業形勢

  • 市場領導者前五名公司的競爭矩陣
  • 市場領導者前五名公司市場收益分析(2022年)
  • 如果合併和收購/合資企業(相符合)
  • SWOT分析(市場5參與企業)
  • 如果專利分析(相符合)

第13章 價格分析

第14章 案例研究

第15章 主要企業預測

  • IBM
  • Microsoft Azure
  • Deepmatter
  • Insilico Medicine
  • Syntelly
  • Unit8
  • Sravathi.ai
  • Citrine Informatics
  • Ansatz AI
  • Nexocode

第16章 策略性推薦事項

第17章 本公司相關資料，免責聲明

Product Code: MX10382

Generative AI in the Chemical Market size was valued at USD 151.2 million in 2022, which is expected to reach USD 936.4 million in 2030 with a CAGR of 25.6% for the forecast period between 2023 and 2030. AI and ML advancements have impacted various sectors for performing automation and predicting hidden discoveries. The application of generative AI across chemical industries has also benefited enormous practices, making these operations more accessible and practical. Generative AI in the chemical domain has the potential to create momentum in the research and development process by significantly increasing the speed and accuracy compared to previous R&D operations. It can assist in automating data extraction, selecting relevant formulation, enhance quality testing accuracy, supply chain management, etc. With the implementation of Generative AI, chemical reaction monitoring and optimization has been advancing. Proper AI algorithms have boosted the various chemical operations such as computational molecular design, synthesis planning, compound property prediction.

Mitsui Chemicals has implemented IBM Watson using a Generative Pre-trained Transformer (GPT) that has already benefited by enhancing the revenue share of Mitsui Chemicals. IBM Watson has significantly transformed around 20 business modules, and over 100 new applications and bugs have been discovered. In 2023, Mitsui extended the application of IBM Watson in various R&D operations using humongous 5 million data points that comprise news, patents, scientific documents, etc. Likewise, chemical companies are putting effort into implementing generative AI in their conventional practices and making their operations more feasible with more accuracy.

Enhanced Predictive Forecasting and Formulation

The conventional trial process to determine the formulation of any compound is very tedious as it must undergo several run and testing steps. There are possible chances of error by manually carrying out such a determination process. The implementation of generative AI in these practices has significantly reduced forecasting errors and has the potential to predict various important methods. Generative AI models and advanced analytics can assist in predicting the composition of materials processing in any operations. Mass balance can also predict the real-time quantity of materials required and left simultaneously. The determination of complex formulation which requires different compounds along with specific composition has become easier as AI models can separately predict the suitable compound along with its composition in the formulation.

Advanced forecast methods using generative AI has optimized the production process such that the new product can be commenced into the market rapidly, ultimately reducing processing time and increasing company's revenue. ChemIntelligence is a precise AI tool that incorporated ML-Bayesian algorithms which assist in developing formulations in a minimum number of performed experiments. This AI formulation tool can extend its applications to adhesives, coatings, drugs, cleaning solutions, food & drinks, etc. The significance of such generative AI tools can be explored in different chemical sectors which will open global market opportunities and fascinate chemical companies to invest and make their processes more feasible.

Structured Data for Designing Molecules

The deployment of generative AI models requires enlarged high-quality datasets to train the algorithm. Building humongous, structured dataset based on chemical configuration, properties, and reaction is very challenging such that the training is difficult on relevant AI models. A proper database comprises of historical information on chemical molecules, their bonding pattern, feasible reactions, and significant characteristics. Designing novel molecular structures along with their properties can be achieved using generative AI algorithms and structured chemical dataset. The steps and time involved in predicting novel molecules are optimized. Generative AI has facilitated the prediction of various molecular properties without any manual intervention and with more effective and accuracy.

Insilico Medicine, an AI company has successfully developed generative adversarial networks (GANs) and reinforcement learning (RL) models to identify novel molecular structures by specifying the suitable parameters. Insilico is extensively using generative AI in different clinical stages and in 2023 it has successfully accomplished the first dose of INS028_055 making it the first anti-fibrotic small molecule inhibitor designed through generative AI algorithms. The automation of molecule discovery has encouraged many AI companies to build selective generative models which is significantly going to transform the potential of global market in generative AI.

Impact of COVID-19

The COVID-19 pandemic peak era was very devastating as due to COVID virus people are succumbs to death. It has created horrific situation which enforced scientists to unveil drug or vaccine to eradicate the virus of COVID-19. Generative AI delivers a prominent role in drug discovery as with manual efforts the scientists would never be able to develop vaccine in limited time. Using chemical molecules and their properties dataset and implementing generative AI models on these datasets consequently led to relevant chemical molecules that could restrict the COVID-19 virus from spreading globally. Indeed, the generative AI has gained interest among the scientists to use it an incredible AI tool for discovering novel drug, chemical molecules in a lesser time.

Impact of Russia-Ukraine War

The annexation of Russia on Ukraine has developed unprecedented impacts globally which turned out to be global economic concern. The disruption in supply chains and novel innovations were some of the negative outcomes of the invasion. The investment in generative AI across chemical sectors got reduced as revenue for new startups in generative AI lowered down due to war. The sanctions imposed by Western countries on Russia enforced these countries to develop their own chemical products and drugs. In 2023 Russian Quantum Center has successfully generated 2331 novel chemical structures with medicinal characteristics by implementing generative AI models on ChEMBL dataset. Thus, the war had impacted and halted the development of these startups and companies in both AI generative and chemical market.

Key Players Landscape and Outlook

With AI and ML advancements, big companies and tech startups frequently invest in their research to build generative AI models for specific applications. IBM, one of the giant tech companies, developed the RXN model in 2018 for chemistry-solving problems. Its AI-enabled algorithm effectively predicts possible outcomes of chemical reactions by optimizing synthesis processes. RXN models can be integrated into an autonomous laboratory for executing developed chemical synthesis procedures. Its advanced scientific infrastructure is specialized in training multiple complex AI models for various chemical processes simultaneously and with greater accuracy. The developed platform has an incredibly massive opportunity for the global market to expand in generative AI.

Table of Contents

1. Research Methodology

2. Project Scope & Definitions

3. Impact of COVID-19 on the Generative AI in Chemical Market

4. Impact of Russia-Ukraine War

5. Executive Summary

6. Voice of Customer

  • 6.1. Market Awareness and Product Information
  • 6.2. Brand Awareness and Loyalty
  • 6.3. Factors Considered in Purchase Decision
    • 6.3.1. Brand Name
    • 6.3.2. Quality
    • 6.3.3. Quantity
    • 6.3.4. Price
    • 6.3.5. Product Specification
    • 6.3.6. Application Specification
    • 6.3.7. Availability of Product
  • 6.4. Frequency of Purchase
  • 6.5. Medium of Purchase

7. Generative AI in Chemical Market Outlook, 2016-2030F

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. By Model
    • 7.2.1. Deep Learning
      • 7.2.1.1. Variational Autoencoders
      • 7.2.1.2. Generative Adversarial Networks
      • 7.2.1.3. Others
    • 7.2.2. Natural Language Processing
    • 7.2.3. Discriminative Model
    • 7.2.4. Reinforcement Learning
    • 7.2.5. Others
  • 7.3. By Application
    • 7.3.1. Complex Structure Predictions
    • 7.3.2. Novel Formulation Optimization
    • 7.3.3. Chemical Process Optimization
    • 7.3.4. Real-time Equipment Monitoring
    • 7.3.5. Production Capacity Optimization
    • 7.3.6. Pricing Optimization
    • 7.3.7. Laboratory Automation
    • 7.3.8. Others
  • 7.4. By End-user
    • 7.4.1. Chemical Processing Industry
      • 7.4.1.1. Food
      • 7.4.1.2. Pharma
      • 7.4.1.3. Others
    • 7.4.2. Research & Development
    • 7.4.3. Others
  • 7.5. By Region
    • 7.5.1. North America
    • 7.5.2. Europe
    • 7.5.3. South America
    • 7.5.4. Asia-Pacific
    • 7.5.5. Middle East and Africa
  • 7.6. By Company Market Share (%), 2022

8. Generative AI in Chemical Market Outlook, By Region, 2016-2030F

  • 8.1. North America*
    • 8.1.1. By Model
      • 8.1.1.1. Deep Learning
      • 8.1.1.1.1. Variational Autoencoders
      • 8.1.1.1.2. Generative Adversarial Networks
      • 8.1.1.1.3. Others
      • 8.1.1.2. Natural Language Processing
      • 8.1.1.3. Discriminative Model
      • 8.1.1.4. Reinforcement Learning
      • 8.1.1.5. Others
    • 8.1.2. By Application
      • 8.1.2.1. Complex Structure Predictions
      • 8.1.2.2. Novel Formulation Optimization
      • 8.1.2.3. Chemical Process Optimization
      • 8.1.2.4. Real-time Equipment Monitoring
      • 8.1.2.5. Production Capacity Optimization
      • 8.1.2.6. Pricing Optimization
      • 8.1.2.7. Laboratory Automation
      • 8.1.2.8. Others
    • 8.1.3. By End-user
      • 8.1.3.1. Chemical Processing Industry
      • 8.1.3.1.1. Food
      • 8.1.3.1.2. Pharma
      • 8.1.3.1.3. Others
      • 8.1.3.2. Research & Development
      • 8.1.3.3. Others
    • 8.1.4. United States*
      • 8.1.4.1. By Model
      • 8.1.4.1.1. Deep Learning
      • 8.1.4.1.1.1. Variational Autoencoders
      • 8.1.4.1.1.2. Generative Adversarial Networks
      • 8.1.4.1.1.3. Others
      • 8.1.4.1.2. Natural Language Processing
      • 8.1.4.1.3. Discriminative Model
      • 8.1.4.1.4. Reinforcement Learning
      • 8.1.4.1.5. Others
      • 8.1.4.2. By Application
      • 8.1.4.2.1. Complex Structure Predictions
      • 8.1.4.2.2. Novel Formulation Optimization
      • 8.1.4.2.3. Chemical Process Optimization
      • 8.1.4.2.4. Real-time Equipment Monitoring
      • 8.1.4.2.5. Production Capacity Optimization
      • 8.1.4.2.6. Pricing Optimization
      • 8.1.4.2.7. Laboratory Automation
      • 8.1.4.2.8. Others
      • 8.1.4.3. By End-user
      • 8.1.4.3.1. Chemical Processing Industry
      • 8.1.4.3.1.1. Food
      • 8.1.4.3.1.2. Pharma
      • 8.1.4.3.1.3. Others
      • 8.1.4.4. Research & Development
      • 8.1.4.5. Others
    • 8.1.5. Canada
    • 8.1.6. Mexico

All segments will be provided for all regions and countries covered:

  • 8.2. Europe
    • 8.2.1. Germany
    • 8.2.2. France
    • 8.2.3. Italy
    • 8.2.4. United Kingdom
    • 8.2.5. Russia
    • 8.2.6. Netherlands
    • 8.2.7. Spain
    • 8.2.8. Turkey
    • 8.2.9. Poland
  • 8.3. South America
    • 8.3.1. Brazil
    • 8.3.2. Argentina
  • 8.4. Asia-Pacific
    • 8.4.1. India
    • 8.4.2. China
    • 8.4.3. Japan
    • 8.4.4. Australia
    • 8.4.5. Vietnam
    • 8.4.6. South Korea
    • 8.4.7. Indonesia
    • 8.4.8. Philippines
  • 8.5. Middle East & Africa
    • 8.5.1. Saudi Arabia
    • 8.5.2. UAE
    • 8.5.3. South Africa

9. Market Mapping, 2022

  • 9.1. By Model
  • 9.2. By Application
  • 9.3. By End-user
  • 9.4. By Region

10. Macro Environment and Industry Structure

  • 10.1. Supply Demand Analysis
  • 10.2. Import Export Analysis
  • 10.3. Supply/Value Chain Analysis
  • 10.4. PESTEL Analysis
    • 10.4.1. Political Factors
    • 10.4.2. Economic System
    • 10.4.3. Social Implications
    • 10.4.4. Technological Advancements
    • 10.4.5. Environmental Impacts
    • 10.4.6. Legal Compliances and Regulatory Policies (Statutory Bodies Included)
  • 10.5. Porter's Five Forces Analysis
    • 10.5.1. Supplier Power
    • 10.5.2. Buyer Power
    • 10.5.3. Substitution Threat
    • 10.5.4. Threat from New Entrant
    • 10.5.5. Competitive Rivalry

11. Market Dynamics

  • 11.1. Growth Drivers
  • 11.2. Growth Inhibitors (Challenges, Restraints)

12. Key Players Landscape

  • 12.1. Competition Matrix of Top Five Market Leaders
  • 12.2. Market Revenue Analysis of Top Five Market Leaders (in %, 2022)
  • 12.3. Mergers and Acquisitions/Joint Ventures (If Applicable)
  • 12.4. SWOT Analysis (For Five Market Players)
  • 12.5. Patent Analysis (If Applicable)

13. Pricing Analysis

14. Case Studies

15. Key Players Outlook

  • 15.1. IBM
    • 15.1.1. Company Details
    • 15.1.2. Key Management Personnel
    • 15.1.3. Products & Services
    • 15.1.4. Financials (As reported)
    • 15.1.5. Key Market Focus & Geographical Presence
    • 15.1.6. Recent Developments
  • 15.2. Microsoft Azure
  • 15.3. Deepmatter
  • 15.4. Insilico Medicine
  • 15.5. Syntelly
  • 15.6. Unit8
  • 15.7. Sravathi.ai
  • 15.8. Citrine Informatics
  • 15.9. Ansatz AI
  • 15.10. Nexocode

Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.

16. Strategic Recommendations

17. About Us & Disclaimer

List of Tables

  • Table 1. Pricing Analysis of Products from Key Players
  • Table 2. Competition Matrix of Top 5 Market Leaders
  • Table 3. Mergers & Acquisitions/ Joint Ventures (If Applicable)
  • Table 4. About Us - Regions and Countries Where We Have Executed Client Projects

List of Figures

  • Figure 1. Global Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 2. Global Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 3. Global Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 4. Global Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 5. Global Generative AI In Chemical Market Share, By Region, In USD Million, 2016-2030F
  • Figure 6. North America Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 7. North America Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 8. North America Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 9. North America Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 10. North America Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 11. United States Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 12. United States Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 13. United States Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 14. United States Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 15. Canada Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 16. Canada Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 17. Canada Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 18. Canada Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 19. Mexico Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 20. Mexico Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 21. Mexico Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 22. Mexico Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 23. Europe Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 24. Europe Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 25. Europe Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 26. Europe Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 27. Europe Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 28. Germany Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 29. Germany Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 30. Germany Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 31. Germany Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 32. France Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 33. France Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 34. France Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 35. France Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 36. Italy Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 37. Italy Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 38. Italy Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 39. Italy Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 40. United Kingdom Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 41. United Kingdom Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 42. United Kingdom Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 43. United Kingdom Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 44. Russia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 45. Russia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 46. Russia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 47. Russia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 48. Netherlands Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 49. Netherlands Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 50. Netherlands Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 51. Netherlands Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 52. Spain Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 53. Spain Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 54. Spain Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 55. Spain Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 56. Turkey Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 57. Turkey Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 58. Turkey Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 59. Turkey Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 60. Poland Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 61. Poland Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 62. Poland Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 63. Poland Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 64. South America Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 65. South America Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 66. South America Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 67. South America Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 68. South America Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 69. Brazil Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 70. Brazil Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 71. Brazil Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 72. Brazil Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 73. Argentina Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 74. Argentina Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 75. Argentina Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 76. Argentina Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 77. Asia-Pacific Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 78. Asia-Pacific Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 79. Asia-Pacific Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 80. Asia-Pacific Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 81. Asia- Pacific Cream Market Share, By End-use Industry, In USD Million, 2016-2030F
  • Figure 82. Asia-Pacific Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 83. India Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 84. India Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 85. India Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 86. India Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 87. China Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 88. China Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 89. China Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 90. China Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 91. Japan Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 92. Japan Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 93. Japan Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 94. Japan Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 95. Australia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 96. Australia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 97. Australia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 98. Australia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 99. Vietnam Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 100. Vietnam Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 101. Vietnam Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 102. Vietnam Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 103. South Korea Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 104. South Korea Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 105. South Korea Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 106. South Korea Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 107. Indonesia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 108. Indonesia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 109. Indonesia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 110. Indonesia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 111. Philippines Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 112. Philippines Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 113. Philippines Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 114. Philippines Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 115. Middle East & Africa Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 116. Middle East & Africa Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 117. Middle East & Africa Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 118. Middle East & Africa Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 119. Middle East & Africa Generative AI In Chemical Market Share, By Country, In USD Million, 2016-2030F
  • Figure 120. Saudi Arabia Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 121. Saudi Arabia Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 122. Saudi Arabia Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 123. Saudi Arabia Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 124. UAE Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 125. UAE Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 126. UAE Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 127. UAE Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 128. South Africa Generative AI In Chemical Market, By Value, In USD Million, 2016-2030F
  • Figure 129. South Africa Generative AI In Chemical Market Share, By Model, In USD Million, 2016-2030F
  • Figure 130. South Africa Generative AI In Chemical Market Share, By Application, In USD Million, 2016-2030F
  • Figure 131. South Africa Generative AI In Chemical Market Share, By End User, In USD Million, 2016-2030F
  • Figure 132. By Model Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 133. By Application Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 134. By End User Map-Market Size (USD Million) & Growth Rate (%), 2022
  • Figure 135. By Region Map-Market Size (USD Million) & Growth Rate (%), 2022