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AI in Catalyst Development Patent Landscape Report

AI in Catalyst Development Patent Landscape Report

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Table of Content

Key Findings
Executive Summary
1. Introduction
2. Patent Landscape Overview
2.1. Patent Family Analysis
2.2. Patent-Market Coverage
2.3. Geographical jurisdiction
2.4. Global AI in Catalyst Development Patent Activity by Application

3. Market and Competitor Analysis
3.1. Market at a Glance
3.2. Market Share of Main Producers
3.3. Some of The Main Processes in AI In Catalyst Development
3.4. Top Applicants
3.5. Top AI in Catalyst Development Applicants by Application
3.6. Market Coverage of Top Applicants
3.7. Top Owners
3.8. Highly-Cited Applicants
3.10. Collaboration
3.10.1. Top Ten Applicants’ collaborations
3.10.2. The strongest cooperation networks
3.11. Top applicant activity
3.12. Pioneer companies in the last 5 years
3.13. Top applicant clustering
3.14. Pending patents

4. Technology Analysis
4.1. Top Technologies
4.1.1. AI in Catalyst Development Patent Trends Across Industries (Application)
4.1.2. Top Technologies by Class
4.1.2. Top Technologies by Sub-Class
4.1.3. Top Technologies by Main-Group
4.1.4. Top Technologies by Sub-Group
4.1.5. Top Technologies and Main Trends
4.2. Five Recent dominant technologies
4.3. Key Patents
4.4. The main themes of patents
4.5. Technology clustering
4.6. Top inventors

5. Key Players’ Patent Profile
5.1. Chevron Phillips Chemical Company LP
 5.1.1. Chevron Phillips Chemical Company LP Overview
 5.1.2. Patent family analysis
 5.1.3. Top Technologies
 5.1.4. Collaboration
 5.1.5. Merge and Acquisitions
 5.1.6. Topic modeling
 5.1.7. Patents clustering

5.2. BASF Corporation
 5.2.1. BASF Corporation Overview
 5.2.2. Patent family analysis
 5.2.3. Top Technologies
 5.2.4. Collaboration
 5.2.5. Merge and Acquisitions
 5.2.6. Key patents
 5.2.7. Topic modeling
 5.2.8. Patents clustering

5.3. Imubit Israel Ltd.
 5.3.1. Imubit Israel Ltd. Overview
 5.3.2. Patent family analysis
 5.3.3. Top Technologies
 5.3.4. Merge and Acquisitions
 5.3.5. Key patents
 5.3.6. Topic modeling

5.4. HTE GmbH – The High Throughput Experimentation Company
 5.4.1. HTE GmbH – The High Throughput Experimentation Overview
 5.4.2. Patent family analysis
 5.4.3. Top Technologies
 5.4.4. Collaboration
 5.4.5. Merge and Acquisitions
 5.4.6. Key patents
 5.4.7. Topic modeling

5.5. CYMBALUK TED H
 5.5.1. CYMBALUK TED H Overview
 5.5.2. Patent family analysis
 5.5.3. Collaboration
 5.5.4. Key patents

5.6. California Institute of Technology (Caltech)
 5.6.1. Caltech Overview
 5.6.2. Patent family analysis
 5.6.3. Top Technologies
 5.6.4. Collaboration
 5.6.5. Topic modeling

5.7. Marathon Petroleum Company LP
 5.7.1. Marathon Petroleum Company LP Overview
 5.7.2. Patent family analysis
 5.7.3. Top Technologies
 5.7.4. Merge and Acquisitions
 5.7.5. Key patents
 5.7.6. Topic modeling

5.8. MCDANIEL MAX P
 5.8.1. MCDANIEL MAX P Overview
 5.8.2. Patent family analysis
 5.8.3. Collaboration
 5.8.4. Key patents

5.9. BENHAM ELIZABETH A
 5.9.1. BENHAM ELIZABETH A Overview
 5.9.2. Patent family analysis
 5.9.3. Collaboration
 5.9.4. Key patents

5.10. University of Berlin Technical
 5.10.1. University of Berlin Technical Overview
 5.10.2. Patent family analysis
 5.10.3. Collaboration

5.11. Honeywell International Inc
 5.11.1. Honeywell International Inc Overview
 5.11.2. Patent family analysis
 5.11.3. Top Technologies
 5.11.4. Merge and Acquisitions
 5.11.5. Key patents
 5.11.6. Topic modeling

5.12. PetroChina Company Limited
 5.12.1. PetroChina Company Limited Overview
 5.12.2. Patent family analysis
 5.12.3. Top Technologies
 5.12.4. Collaboration
 5.12.5. Merge and Acquisitions
 5.12.6. Key patents
 5.12.7. Topic modeling

5.13. Hyundai Motor Company
 5.13.1. Hyundai Motor Company Overview
 5.13.2. Patent family analysis
 5.13.3. Collaboration
 5.13.4. Merge and Acquisitions

5.14. SABIC SK Nexlene Company Pte. Ltd.
 5.14.1. SABIC SK Nexlene Company Overview
 5.14.2. Patent family analysis
 5.14.3. Top Technologies
 5.14.4. Merge and Acquisitions
 5.14.5. Topic modeling

5.15. ExxonMobil Research & Engineering Co
 5.15.1. ExxonMobil Research & Engineering Co Overview
 5.15.2. Patent family analysis
 5.15.3. Top Technologies
 5.15.4. Merge and Acquisitions
 5.15.5. Topic modeling

5.16. Sinopec Beijing Chemical Research Institute Co Ltd
 5.16.1. Sinopec Beijing Chemical Research Institute Co Ltd Overview
 5.16.2. Patent family analysis
 5.16.3. Collaboration

Disclaimer

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AI in catalyst sample

Description

The AI in Catalyst Development Report provides a comprehensive patent landscape analysis, encompassing 133 patents filed between 2010 and 2024 across key jurisdictions. This dataset, sourced from international filings and supported by market adoption trends and AI-driven innovation data, forms the foundation for evaluating the industry’s shift toward intelligent catalyst discovery and optimization. By combining quantitative and qualitative insights, the report identifies core technological advancements and market dynamics that are shaping the evolution of catalyst development through artificial intelligence.

The report is divided into key sections offering unique insights into various aspects of the field. These sections include the Landscape Overview, Market Analysis, Technology Analysis, and Key Players. Each section is designed to assist stakeholders in making informed decisions on research, investment, and strategic planning within the AI-enabled catalyst domain.

Landscape Overview
This section provides a detailed view of patent activity trends in AI for catalyst development, highlighting consistent growth since 2015 and accelerating adoption in recent years. It reveals China, the United States, and Europe as leading jurisdictions for innovation, reflecting global investment in computational chemistry, high-throughput experimentation, and digital laboratory platforms.

Market Analysis Section
The market analysis focuses on the application of AI in catalyst R&D across industries such as petrochemicals, renewable energy, automotive, and specialty chemicals. By aligning patent activity with commercialization trends, this section uncovers the regions and sectors where AI-driven catalyst innovation is gaining the most traction and identifies opportunities for accelerated deployment.

Technology Analysis Section
In this section, the report explores key technological domains such as AI-assisted molecular design, predictive modeling, high-throughput screening, and process optimization. Leading innovations like deep learning for reaction pathway prediction and reinforcement learning for catalyst performance optimization are identified as transformative forces driving faster discovery cycles and more efficient development pipelines.

Top Player Section
The top player section examines the strategic contributions of organizations such as BASF SE, ExxonMobil, Johnson Matthey, Sinopec, and the Chinese Academy of Sciences. It highlights their patent holdings, technological focus, and collaborative initiatives to accelerate AI-driven catalyst innovation. For example, BASF’s integration of AI platforms in catalyst screening and Sinopec’s machine learning-driven materials design programs are showcased as industry benchmarks.

Overall
This report provides a holistic perspective on the state and trajectory of AI in catalyst development. By integrating data on patents, technologies, and market trends, it serves as an essential resource for stakeholders aiming to leverage AI-powered advancements in materials science and industrial catalysis.

What You Will Get

120+ Pages PDF Report: Detailed insights on patent and market trends.

40+ Pages PDF Slides: A concise overview for presentations.

Excel Files: Comprehensive data for in-depth analysis.

People Who Might Be Interested in the Report

Corporate Executives: Strategy, operations, and technology leaders in chemicals, energy, and manufacturing.

R&D Professionals: Innovators applying AI in catalyst and materials science.

Investment Analysts: Evaluators of emerging opportunities in AI-driven industrial innovation.

Policy Makers: Advocates for sustainable and technologically advanced manufacturing practices.

Academics and Consultants: Researchers and advisors exploring AI applications in chemistry and industrial processes.

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