AI-Powered Materials Discovery & Computational Chemistry Market
完整報告名稱與涵蓋範圍
AI-Powered Materials Discovery & Computational Chemistry Market Size, Share & Trends Analysis Report, By Platform Type (AI Materials Discovery and Generative Design Platforms, Machine-Learned Interatomic Potentials and Simulation Software, Autonomous Laboratories and Self-Driving Experimentation, Materials Data, Informatics and Workflow Platforms), By Application (Batteries, Energy Storage and Catalysts, Semiconductors, Electronic and Photonic Materials, Polymers, Coatings and Specialty Chemicals, Pharmaceutical Formulation and Biomaterials), By End User (Chemical and Materials Companies, Battery, Semiconductor and Electronics Manufacturers, Pharmaceutical Companies, Academic, Government and National Laboratories), By Region, and Segment Forecasts, 2026-2035
報告摘要
The AI-powered materials discovery and computational chemistry market covers generative design and discovery platforms, machine-learned interatomic potentials and simulation software, autonomous laboratories and self-driving experimentation systems and materials data and informatics platforms applied to batteries, catalysts, semiconductors, polymers, specialty chemicals and pharmaceutical materials. Conventional density functional theory codes without machine learning, laboratory automation hardware sold separately and AI drug discovery addressed in a separate InsightAce study are excluded.
The field has been transformed by foundation models trained on millions of computed structures that predict stability and properties at speeds orders of magnitude faster than first-principles methods, by generative models that propose novel crystals and molecules and by robotic laboratories that synthesise and test candidates in closed loops. Chemical, battery and semiconductor companies are deploying these tools to shorten development cycles for electrolytes, catalysts, alloys and electronic materials, technology groups and cloud providers offer models and simulation at scale, and well-funded start-ups combine AI with autonomous synthesis. Validation of predicted materials, data quality and ownership, integration with laboratory workflows and the transition from discovery to manufacturable products shape commercial adoption.
North America and Europe host most platform developers, with Japan, South Korea and China as major industrial users and investors. Competition involves technology groups with scientific AI programs, computational chemistry software vendors, materials informatics companies and autonomous laboratory start-ups.
The InsightAce study delivers revenue forecasts in US$ across all platform type, application and end user and regional segments for 2026-2035, alongside segment-level trend analysis, competitive landscape assessment and detailed company profiling.
授權報價
| Single User | $4,500 USD |
| Enterprise / Global Site Licence | $9,500 USD |
目錄 Table of Contents
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI-Powered Materials Discovery & Computational Chemistry Market Snapshot
Chapter 4. Global AI-Powered Materials Discovery & Computational Chemistry Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Market Drivers
4.3. Market Challenges
4.4. Emerging Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis - Porter's Five Forces Analysis
4.7. Competitive Landscape and Market Positioning
4.8. Market Opportunity Analysis
Chapter 5. Market Segmentation 1: By Platform Type Estimates & Trend Analysis
5.1. Platform Type & Market Share, 2026 & 2035
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analysis, 2026 to 2035, by Platform Type:
5.2.1. AI Materials Discovery and Generative Design Platforms
5.2.2. Machine-Learned Interatomic Potentials and Simulation Software
5.2.3. Autonomous Laboratories and Self-Driving Experimentation
5.2.4. Materials Data, Informatics and Workflow Platforms
Chapter 6. Market Segmentation 2: By Application Estimates & Trend Analysis
6.1. Application & Market Share, 2026 & 2035
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analysis, 2026 to 2035, by Application:
6.2.1. Batteries, Energy Storage and Catalysts
6.2.2. Semiconductors, Electronic and Photonic Materials
6.2.3. Polymers, Coatings and Specialty Chemicals
6.2.4. Pharmaceutical Formulation and Biomaterials
Chapter 7. Market Segmentation 3: By End User Estimates & Trend Analysis
7.1. End User & Market Share, 2026 & 2035
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analysis, 2026 to 2035, by End User:
7.2.1. Chemical and Materials Companies
7.2.2. Battery, Semiconductor and Electronics Manufacturers
7.2.3. Pharmaceutical Companies
7.2.4. Academic, Government and National Laboratories
Chapter 8. AI-Powered Materials Discovery & Computational Chemistry Market: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. United States
8.1.2. Canada
8.2. Europe
8.2.1. Germany
8.2.2. United Kingdom
8.2.3. France
8.2.4. Italy
8.2.5. Spain
8.2.6. Rest of Europe
8.3. Asia Pacific
8.3.1. China
8.3.2. Japan
8.3.3. India
8.3.4. South Korea
8.3.5. Australia
8.3.6. Southeast Asia
8.3.7. Rest of Asia Pacific
8.4. Latin America
8.4.1. Brazil
8.4.2. Mexico
8.4.3. Rest of Latin America
8.5. Middle East & Africa
8.5.1. GCC Countries
8.5.2. South Africa
8.5.3. Rest of Middle East & Africa
Chapter 9. Competitive Landscape
9.1. Company Market Positioning Analysis
9.2. Strategic Developments (Partnerships, Expansions, Product Launches, M&A)
Chapter 10. Company Profiles
10.1. Microsoft Corporation (Azure Quantum Elements)
10.2. Google DeepMind
10.3. Schrodinger, Inc.
10.4. Citrine Informatics, Inc.
10.5. Kebotix, Inc.
10.6. Chemify Ltd.
10.7. Orbital Materials
10.8. CuspAI
10.9. Periodic Labs
10.10. Radical AI
10.11. Matlantis (Preferred Computational Chemistry)
10.12. Materials Design, Inc.
10.13. Dassault Systemes (BIOVIA)
10.14. Aionics, Inc.
10.15. Lila Sciences
圖表清單 List of Tables & Figures
Table 1. Global AI-Powered Materials Discovery & Computational Chemistry Market Revenue Forecast by Platform Type, 2026-2035 (US$ Mn)
Figure 1. Global AI-Powered Materials Discovery & Computational Chemistry Market Segmentation Overview
Table 2. Global AI-Powered Materials Discovery & Computational Chemistry Market Revenue Forecast by Application, 2026-2035 (US$ Mn)
Figure 2. Global AI-Powered Materials Discovery & Computational Chemistry Market Share by Platform Type, 2026 & 2035
Table 3. Global AI-Powered Materials Discovery & Computational Chemistry Market Revenue Forecast by End User, 2026-2035 (US$ Mn)
Figure 3. AI Materials Discovery Workflow from Generative Design to Autonomous Synthesis and Validation
Table 4. North America AI-Powered Materials Discovery & Computational Chemistry Market Revenue by Country, 2026-2035 (US$ Mn)
Figure 4. Global AI-Powered Materials Discovery & Computational Chemistry Market Share by Application, 2026 & 2035
Table 5. Europe AI-Powered Materials Discovery & Computational Chemistry Market Revenue by Country, 2026-2035 (US$ Mn)
Figure 5. Development Cycle Reduction Reported Across Battery, Catalyst and Electronic Materials Programs
Table 6. Asia Pacific AI-Powered Materials Discovery & Computational Chemistry Market Revenue by Country, 2026-2035 (US$ Mn)
Figure 6. Asia Pacific AI-Powered Materials Discovery & Computational Chemistry Growth Opportunity Analysis
Table 7. Latin America AI-Powered Materials Discovery & Computational Chemistry Market Revenue by Country, 2026-2035 (US$ Mn)
Figure 7. Platform Landscape by Business Model: Software, Partnered Discovery and Internal Pipelines
Table 8. Middle East & Africa AI-Powered Materials Discovery & Computational Chemistry Market Revenue by Country, 2026-2035 (US$ Mn)
Figure 8. Competitive Positioning of Key AI-Powered Materials Discovery & Computational Chemistry Market Participants
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