Generative AI In Material Science Market Size 2025-2029
The generative ai in material science market size is forecast to increase by USD 1.7 billion, at a CAGR of 27.9% between 2024 and 2029.
The demand from high-stakes industries for materials with unprecedented performance characteristics is a primary market driver for generative AI in material science. The use of generative AI in manufacturing and generative AI in automotive sectors allows for inverse design methodology, where desired properties are defined upfront to guide the creation of novel structures. This approach, which utilizes generative models and generative adversarial networks, radically shortens discovery timelines compared to traditional research. This shift enables rapid innovation in areas such as lightweight alloys and advanced electronics, directly addressing fundamental material limitations that previously inhibited progress. It is a key enabler for strategic advancement in industrial design.A significant trend is the integration of generative AI platforms with robotic automation to create autonomous, closed-loop discovery systems. This paradigm addresses the critical bottleneck of experimental validation by automating the design-build-test-learn cycle. This approach is highly relevant for generative AI in chemical and generative AI in energy applications. However, a foundational challenge remains the persistent issue of data scarcity and accessibility. The efficacy of generative models is predicated on the volume and quality of training data, and the fragmented, unstructured, and often proprietary nature of material science data creates a formidable barrier to developing robust and generalizable AI models.
What will be the Size of the Generative AI In Material Science Market during the forecast period?
Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
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The global generative AI in material science market is defined by the application of advanced computational techniques to accelerate materials innovation. Core to this field is the use of inverse design methodology, where generative models propose novel atomic structures based on predefined performance targets. This approach is central to developments in generative AI in manufacturing, enabling the creation of materials with tailored properties. The process relies heavily on generative adversarial networks and other sophisticated algorithms to explore vast chemical spaces, moving beyond traditional trial-and-error research paradigms.A significant shift is occurring toward more practical and holistic design frameworks. The focus is expanding from single-property optimization to multi-objective optimization, which balances competing requirements such as performance, cost, and durability. This is particularly relevant for generative AI in chemical applications, where commercial viability is key. Furthermore, the integration of manufacturability-aware design ensures that computationally discovered materials have a clear and viable path to production, bridging the gap between theoretical discovery and real-world implementation.The technological underpinnings of this market continue to evolve rapidly. The democratization of high-performance computing, often via cloud platforms, provides the necessary power for training complex AI systems. These platforms support GPU-accelerated simulations and large-scale materials informatics, which are essential for processing computational simulation results and experimental data. The ongoing development of AI-driven molecular prediction tools and more advanced generative AI algorithms is continuously enhancing the predictive accuracy and innovative potential of these systems, driving further adoption in generative AI in industrial design.
How is this Generative AI In Material Science Industry segmented?
The generative ai in material science industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.
- Type
- Materials discovery and design
- Predictive modeling and simulation
- Process optimization
- Deployment
- Cloud based
- On premises
- Hybrid
- Application
- Pharmaceuticals and chemicals
- Automotive and aerospace
- Electronics and semiconductors
- Energy storage and conversion
- Others
- Geography
- North America
- Europe
- Germany
- UK
- France
- Italy
- The Netherlands
- Spain
- APAC
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- South America
- Middle East and Africa
- Rest of World (ROW)
By Type Insights
The materials discovery and design segment is estimated to witness significant growth during the forecast period.
The materials discovery and design segment functions as the primary engine of innovation in the market. It inverts the traditional research paradigm through inverse design, where researchers define target properties and deploy generative models to propose novel atomic structures. This approach is transformative for generative AI in energy applications, accelerating the search for new battery and semiconductor materials. It leverages sophisticated deep learning architectures, including generative adversarial networks and diffusion models, to explore a virtually infinite chemical space beyond human conception.
This segment represents the vanguard of the market, accounting for over 40% of its total value. Its commercial implications are profound, enabling the design of next-generation materials for a wide range of industries. The market is characterized by intense activity from established technology corporations and a burgeoning ecosystem of specialized startups. These entities are developing proprietary platforms tailored for specific industrial applications, from designing novel molecules for drug delivery systems to creating advanced materials for more powerful and efficient computer chips, solidifying its role as the most dynamic area of the market.
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The Materials discovery and design segment was valued at USD 133.70 million in 2019 and showed a gradual increase during the forecast period.
Regional Analysis
North America is estimated to contribute 46.9% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.
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North America, particularly the United States, stands as the unequivocal leader in the global generative AI in material science market. The region's dominance is underpinned by a highly mature and dynamic ecosystem that seamlessly integrates academia, government research, and a vibrant commercial sector. This environment is characterized by unparalleled access to venture capital, a concentration of the world's foremost AI talent, and the presence of both established technology giants and a flourishing landscape of specialized startups. The market is driven by substantial R&D investments from key industries such as aerospace, defense, and biotechnology.
The competitive landscape in North America is dense, featuring specialized firms offering sophisticated platforms-as-a-service alongside the massive R&D divisions of multinational corporations. The US market alone constitutes over 80% of the regional activity, underscoring its dominance in driving innovation. Government-backed institutions and national laboratories are at the forefront of foundational research, often collaborating directly with private industry to translate discoveries into commercial applications. This powerful flywheel of innovation cements the region's position as the primary hub for both the development and application of this technology.
Market Dynamics
Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.
The global generative AI in material science market is experiencing unprecedented growth as industries seek to accelerate innovation and reduce R&D costs. Companies are leveraging a cloud-based generative materials platform to conduct high-throughput virtual screening materials, drastically shortening development cycles. The application of generative AI for catalyst design and AI-driven alloy composition optimization is enabling the creation of novel materials with superior performance characteristics. Furthermore, the use of domain-specific foundation models chemistry is becoming a cornerstone for advanced research, complemented by machine learning force fields simulation to predict material behavior accurately. This technological synergy facilitates AI-driven sustainable material discovery, meeting growing demand for eco-friendly products. Techniques like deep learning for structure-property relationships are fundamental, while transformer models for molecular design are pushing the boundaries of what's possible in molecular engineering.The market's expansion is also fueled by specialized applications, such as using generative adversarial networks for OLEDs to develop next-generation displays for OEMs and employing variational autoencoders for polymer design. The push for electrification relies heavily on breakthroughs in battery technology, where AI for solid-state electrolyte discovery and AI-powered custom electrolyte design are critical. For structural materials, the inverse design of high-entropy alloys and the creation of generative models for metal-organic frameworks are opening new frontiers. The integration of quantum mechanical simulation materials with graph networks for materials exploration provides a deeper understanding of material properties. This advanced ecosystem is paving the way for fully autonomous laboratories for material discovery, which incorporate automated synthesis pathway prediction and multi-objective manufacturability aware design to create market-ready materials efficiently.
What are the key market drivers leading to the rise in the adoption of Generative AI In Material Science Industry?
- The market is primarily driven by the escalating demand from high-stakes industries for novel materials with unprecedented performance characteristics.
Intensifying industrial demand for next-generation materials with unprecedented performance characteristics is a primary market driver. Traditional R&D cycles are insufficient for sectors like aerospace and automotive electrification, which face fundamental material limitations. Generative AI offers a revolutionary inverse design methodology, allowing scientists to define desired properties like superior tensile strength or higher thermal conductivity and then generate new, viable chemical structures. This radically shortens the discovery and screening phase from years to months. With North America poised to capture nearly 47% of the market's incremental growth, the alignment with core industrial R&D objectives positions generative AI as an essential component of strategic innovation and a means to secure a competitive advantage in a rapidly evolving technological landscape.The global and increasingly urgent mandate for sustainability has emerged as a foundational driver for the market. Pressures from regulatory bodies, investors, and consumers compel industries to focus on decarbonization and the principles of a circular economy. Generative AI provides an indispensable toolkit for the de novo design of environmentally benign materials, such as novel catalysts for carbon capture or materials for the green energy transition. Another critical aspect is addressing resource scarcity by designing high-performance alternatives to rare-earth elements, a key focus for the EV and wind power industries. The ability to create purpose-built, sustainable materials positions generative AI as a critical enabling technology for companies seeking to meet ESG commitments, ensuring robust, long-term investment and support for market growth.
What are the market trends shaping the Generative AI In Material Science Industry?
- An upcoming trend is the increasing integration of generative AI platforms with robotic automation to create autonomous, closed-loop discovery systems.
A defining trend is the integration of generative AI with robotic automation to create autonomous, closed-loop discovery systems, often called self-driving laboratories. This approach addresses the critical bottleneck of experimental validation by creating a seamless, automated design-build-test-learn cycle. The generative AI engine hypothesizes new material structures, which are then passed to a robotic platform for synthesis and characterization. The resulting experimental data is fed back to the AI model, enabling an exponential acceleration in discovery. This trend is not merely about speed; it also allows for the exploration of more complex experimental spaces. As this trend in generative AI in manufacturing matures, with regions like North America showing a projected CAGR of 28.3%, the market is seeing a rise in end-to-end automated material innovation solutions.Another powerful trend is the development and deployment of large-scale, domain-specific foundation models tailored for chemistry and material science. These large neural networks are pre-trained on vast, diverse datasets, allowing them to develop a nuanced understanding of the principles governing material behavior. Once pre-trained, the model can be rapidly fine-tuned for a wide range of specific downstream applications, such as designing new lightweight alloys or discovering novel catalysts. This approach, vital for generative AI in chemical industries, dramatically lowers the barrier to entry for organizations lacking immense computational resources and accelerates the development cycle for new applications. These models demonstrate superior performance and generalizability compared to smaller predecessors, fostering a new platform-based ecosystem.
What challenges does the Generative AI In Material Science Industry face during its growth?
- A key challenge affecting industry growth is the persistent issue of data scarcity, quality, and accessibility, which constrains the development of robust models.
A foundational challenge constraining market growth is the persistent issue of data scarcity, quality, and accessibility. The efficacy of any generative AI model depends on the volume, veracity, and diversity of its training data. Material science data is notoriously fragmented, unstructured, and often locked in proprietary silos, creating a formidable barrier to developing robust and generalizable AI models. The scarcity is most acute when exploring novel material classes where little historical data exists. Furthermore, aggregating heterogeneous experimental and computational data requires a monumental effort in data cleaning, normalization, and uncertainty quantification. While the market is growing, with the largest country market representing over 80% of its regional total, these data-centric challenges remain the primary bottleneck limiting predictive accuracy and reliability.A second major challenge is the significant gap between computational prediction and real-world viability, often called the synthesis chasm. A generative AI model can propose millions of novel material structures, but a material that exists only in a simulation is of no practical value if it cannot be synthesized and manufactured at scale. This involves two hurdles: ensuring the synthesizability of generated candidates and navigating the bottleneck of physical experimental validation. Many models lack an intrinsic understanding of synthetic chemistry and may generate configurations that are practically impossible to create. Additionally, the manual, slow, and resource-intensive process of experimental validation becomes a rate-limiting step, severely diminishing the iterative power of the AI-driven approach and constraining the overall pace of validated discovery.
Exclusive Customer Landscape
The generative ai in material science market forecasting report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the generative ai in material science market report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.
Customer Landscape
Key Companies & Market Insights
Companies are implementing various strategies, such as strategic alliances, generative ai in material science market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the industry.
Aionics Inc. - The company provides an advanced AI-powered platform facilitating custom design solutions for electrolytes and new molecules. This offering extends to integrated supply chain functionalities, leveraging proprietary artificial molecular intelligence and transformer-based models to accelerate materials innovation.
The industry research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:
- Aionics Inc.
- Arzeda Corp.
- BASF SE
- Citrine Informatics Inc.
- Dassault Systemes SE
- DeepMaterials LLC
- Google LLC
- International Business Machines Corp.
- Kebotix Inc.
- Mat3ra Inc.
- MaterialsZone Ltd.
- Microsoft Corp.
- NVIDIA Corp.
- Optibrium
- Orbital Materials Inc.
- Phaseshift Inc.
- Quantum Generative Materials LLC
- sandboxAQ
- Schrodinger Inc.
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key industry players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
Recent Development and News in Generative Ai In Material Science Market
In June 2024, Materials Nexus announced it had designed and identified MagNex, a new rare-earth-free permanent magnet, using its cloud-native AI platform.In March 2024, NVIDIA announced the expansion of its generative AI microservices and cloud APIs aimed at the scientific computing community, providing researchers with access to pre-trained, domain-specific foundation models.In February 2024, researchers from the Massachusetts Institute of Technology published a study detailing the use of a generative AI diffusion model to rapidly design and discover new metal-organic frameworks for carbon capture.In January 2024, Microsoft and the Pacific Northwest National Laboratory announced the use of an advanced AI platform to accelerate the discovery of a new solid-state battery material by screening over 32 million potential candidates.
Research Analyst Overview
The global generative AI in material science market is evolving through the application of sophisticated deep learning architectures. Techniques such as generative adversarial networks and variational autoencoders are central to a new inverse design methodology, enabling the direct generation of novel structures with desired properties. This approach facilitates AI-driven molecular prediction and atomistic design for complex materials, including next-generation solid-state battery material and porous metal-organic frameworks. The process moves beyond conventional high-throughput screening by integrating GPU-accelerated simulations and quantum mechanical simulations. This shift is powered by high-performance computing, which rapidly processes computational simulation results, accelerating the exploration of vast chemical spaces and refining the understanding of structure-property relationships for accelerated molecular generation.Operational frameworks are advancing toward automated material innovation, with an anticipated 35% increase in the integration of AI for science discovery. This transition is characterized by closed-loop discovery systems and self-driving laboratories that connect computational prediction with experimental validation. A materials informatics platform is central, utilizing data from crystallographic databases to address negative data bias. The development of multimodal foundation models and a generative AI diffusion model is crucial for multi-objective optimization, while physics-informed neural networks help navigate the synthesis chasm with accurate synthesis pathway prediction. Incorporating manufacturability-aware design, these systems use graph neural networks and machine learning force fields for atomic-level simulation, enabling unstructured data extraction from proprietary data silos.
Dive into Technavio’s robust research methodology, blending expert interviews, extensive data synthesis, and validated models for unparalleled Generative AI In Material Science Market insights. See full methodology.
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Market Scope
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Report Coverage
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Details
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Page number
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311
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Base year
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2024
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Historic period
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2019 - 2023 |
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Forecast period
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2025-2029
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Growth momentum & CAGR
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Accelerating at a CAGR of 27.9%
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Market growth 2024-2029
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USD 1.7 billion
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Market structure
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Fragmented
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YoY growth 2024-2029(%)
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24.5%
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Key countries
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US, Canada, Mexico, Germany, UK, France, Italy, The Netherlands, Spain, China, Japan, India, South Korea, Australia, Indonesia, Brazil, Argentina, Colombia, Saudi Arabia, UAE, South Africa, Israel, Turkey
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Competitive landscape
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Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks
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What are the Key Data Covered in this Generative AI In Material Science Market Research and Growth Report?
- CAGR of the Generative AI In Material Science industry during the forecast period
- Detailed information on factors that will drive the growth and forecasting between 2024 and 2029
- Precise estimation of the size of the market and its contribution of the industry in focus to the parent market
- Accurate predictions about upcoming growth and trends and changes in consumer behaviour
- Growth of the market across North America, Europe, APAC, South America, Middle East and Africa
- Thorough analysis of the market’s competitive landscape and detailed information about companies
- Comprehensive analysis of factors that will challenge the generative ai in material science market growth of industry companies
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1 Executive Summary
- 1 Executive Summary
- 1.1 Market overview
- Executive Summary - Chart on Market Overview
- Executive Summary - Data Table on Market Overview
- Executive Summary - Chart on Global Market Characteristics
- Executive Summary - Chart on Market by Geography
- Executive Summary - Chart on Market Segmentation by Type
- Executive Summary - Chart on Market Segmentation by Deployment
- Executive Summary - Chart on Market Segmentation by Application
- Executive Summary - Chart on Incremental Growth
- Executive Summary - Data Table on Incremental Growth
- Executive Summary - Chart on Company Market Positioning
2 Technavio Analysis
- 2 Technavio Analysis
- 2.1 Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
- Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
- 2.2 Criticality of inputs and Factors of differentiation
- Chart on Overview on criticality of inputs and factors of differentiation
- 2.3 Factors of disruption
- Chart on Overview on factors of disruption
- 2.4 Impact of drivers and challenges
- Chart on Impact of drivers and challenges in 2024 and 2029
3 Market Landscape
- 3 Market Landscape
- 3.1 Market ecosystem
- Chart on Parent Market
- Data Table on - Parent Market
- 3.2 Market characteristics
- Chart on Market characteristics analysis
- 3.3 Value chain analysis
- Chart on Value chain analysis
4 Market Sizing
- 4 Market Sizing
- 4.1 Market definition
- Data Table on Offerings of companies included in the market definition
- 4.2 Market segment analysis
- 4.3 Market size 2024
- 4.4 Market outlook: Forecast for 2024-2029
- Chart on Global - Market size and forecast 2024-2029 ($ billion)
- Data Table on Global - Market size and forecast 2024-2029 ($ billion)
- Chart on Global Market: Year-over-year growth 2024-2029 (%)
- Data Table on Global Market: Year-over-year growth 2024-2029 (%)
5 Historic Market Size
- 5 Historic Market Size
- 5.1 Global Generative AI In Material Science Market 2019 - 2023
- Historic Market Size - Data Table on Global Generative AI In Material Science Market 2019 - 2023 ($ billion)
- 5.2 Type segment analysis 2019 - 2023
- Historic Market Size - Type Segment 2019 - 2023 ($ billion)
- 5.3 Deployment segment analysis 2019 - 2023
- Historic Market Size - Deployment Segment 2019 - 2023 ($ billion)
- 5.4 Application segment analysis 2019 - 2023
- Historic Market Size - Application Segment 2019 - 2023 ($ billion)
- 5.5 Geography segment analysis 2019 - 2023
- Historic Market Size - Geography Segment 2019 - 2023 ($ billion)
- 5.6 Country segment analysis 2019 - 2023
- Historic Market Size - Country Segment 2019 - 2023 ($ billion)
6 Five Forces Analysis
- 6 Five Forces Analysis
- 6.1 Five forces summary
- Five forces analysis - Comparison between 2024 and 2029
- 6.2 Bargaining power of buyers
- Bargaining power of buyers - Impact of key factors 2024 and 2029
- 6.3 Bargaining power of suppliers
- Bargaining power of suppliers - Impact of key factors in 2024 and 2029
- 6.4 Threat of new entrants
- Threat of new entrants - Impact of key factors in 2024 and 2029
- 6.5 Threat of substitutes
- Threat of substitutes - Impact of key factors in 2024 and 2029
- 6.6 Threat of rivalry
- Threat of rivalry - Impact of key factors in 2024 and 2029
- 6.7 Market condition
- Chart on Market condition - Five forces 2024 and 2029
7 Market Segmentation by Type
- 7 Market Segmentation by Type
- 7.1 Market segments
- Chart on Type - Market share 2024-2029 (%)
- Data Table on Type - Market share 2024-2029 (%)
- 7.2 Comparison by Type
- Chart on Comparison by Type
- Data Table on Comparison by Type
- 7.3 Materials discovery and design - Market size and forecast 2024-2029
- Chart on Materials discovery and design - Market size and forecast 2024-2029 ($ billion)
- Data Table on Materials discovery and design - Market size and forecast 2024-2029 ($ billion)
- Chart on Materials discovery and design - Year-over-year growth 2024-2029 (%)
- Data Table on Materials discovery and design - Year-over-year growth 2024-2029 (%)
- 7.4 Predictive modeling and simulation - Market size and forecast 2024-2029
- Chart on Predictive modeling and simulation - Market size and forecast 2024-2029 ($ billion)
- Data Table on Predictive modeling and simulation - Market size and forecast 2024-2029 ($ billion)
- Chart on Predictive modeling and simulation - Year-over-year growth 2024-2029 (%)
- Data Table on Predictive modeling and simulation - Year-over-year growth 2024-2029 (%)
- 7.5 Process optimization - Market size and forecast 2024-2029
- Chart on Process optimization - Market size and forecast 2024-2029 ($ billion)
- Data Table on Process optimization - Market size and forecast 2024-2029 ($ billion)
- Chart on Process optimization - Year-over-year growth 2024-2029 (%)
- Data Table on Process optimization - Year-over-year growth 2024-2029 (%)
- 7.6 Market opportunity by Type
- Market opportunity by Type ($ billion)
- Data Table on Market opportunity by Type ($ billion)
8 Market Segmentation by Deployment
- 8 Market Segmentation by Deployment
- 8.1 Market segments
- Chart on Deployment - Market share 2024-2029 (%)
- Data Table on Deployment - Market share 2024-2029 (%)
- 8.2 Comparison by Deployment
- Chart on Comparison by Deployment
- Data Table on Comparison by Deployment
- 8.3 Cloud based - Market size and forecast 2024-2029
- Chart on Cloud based - Market size and forecast 2024-2029 ($ billion)
- Data Table on Cloud based - Market size and forecast 2024-2029 ($ billion)
- Chart on Cloud based - Year-over-year growth 2024-2029 (%)
- Data Table on Cloud based - Year-over-year growth 2024-2029 (%)
- 8.4 On premises - Market size and forecast 2024-2029
- Chart on On premises - Market size and forecast 2024-2029 ($ billion)
- Data Table on On premises - Market size and forecast 2024-2029 ($ billion)
- Chart on On premises - Year-over-year growth 2024-2029 (%)
- Data Table on On premises - Year-over-year growth 2024-2029 (%)
- 8.5 Hybrid - Market size and forecast 2024-2029
- Chart on Hybrid - Market size and forecast 2024-2029 ($ billion)
- Data Table on Hybrid - Market size and forecast 2024-2029 ($ billion)
- Chart on Hybrid - Year-over-year growth 2024-2029 (%)
- Data Table on Hybrid - Year-over-year growth 2024-2029 (%)
- 8.6 Market opportunity by Deployment
- Market opportunity by Deployment ($ billion)
- Data Table on Market opportunity by Deployment ($ billion)
9 Market Segmentation by Application
- 9 Market Segmentation by Application
- 9.1 Market segments
- Chart on Application - Market share 2024-2029 (%)
- Data Table on Application - Market share 2024-2029 (%)
- 9.2 Comparison by Application
- Chart on Comparison by Application
- Data Table on Comparison by Application
- 9.3 Pharmaceuticals and chemicals - Market size and forecast 2024-2029
- Chart on Pharmaceuticals and chemicals - Market size and forecast 2024-2029 ($ billion)
- Data Table on Pharmaceuticals and chemicals - Market size and forecast 2024-2029 ($ billion)
- Chart on Pharmaceuticals and chemicals - Year-over-year growth 2024-2029 (%)
- Data Table on Pharmaceuticals and chemicals - Year-over-year growth 2024-2029 (%)
- 9.4 Automotive and aerospace - Market size and forecast 2024-2029
- Chart on Automotive and aerospace - Market size and forecast 2024-2029 ($ billion)
- Data Table on Automotive and aerospace - Market size and forecast 2024-2029 ($ billion)
- Chart on Automotive and aerospace - Year-over-year growth 2024-2029 (%)
- Data Table on Automotive and aerospace - Year-over-year growth 2024-2029 (%)
- 9.5 Electronics and semiconductors - Market size and forecast 2024-2029
- Chart on Electronics and semiconductors - Market size and forecast 2024-2029 ($ billion)
- Data Table on Electronics and semiconductors - Market size and forecast 2024-2029 ($ billion)
- Chart on Electronics and semiconductors - Year-over-year growth 2024-2029 (%)
- Data Table on Electronics and semiconductors - Year-over-year growth 2024-2029 (%)
- 9.6 Energy storage and conversion - Market size and forecast 2024-2029
- Chart on Energy storage and conversion - Market size and forecast 2024-2029 ($ billion)
- Data Table on Energy storage and conversion - Market size and forecast 2024-2029 ($ billion)
- Chart on Energy storage and conversion - Year-over-year growth 2024-2029 (%)
- Data Table on Energy storage and conversion - Year-over-year growth 2024-2029 (%)
- 9.7 Others - Market size and forecast 2024-2029
- Chart on Others - Market size and forecast 2024-2029 ($ billion)
- Data Table on Others - Market size and forecast 2024-2029 ($ billion)
- Chart on Others - Year-over-year growth 2024-2029 (%)
- Data Table on Others - Year-over-year growth 2024-2029 (%)
- 9.8 Market opportunity by Application
- Market opportunity by Application ($ billion)
- Data Table on Market opportunity by Application ($ billion)
10 Customer Landscape
- 10 Customer Landscape
- 10.1 Customer landscape overview
- Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
11 Geographic Landscape
- 11 Geographic Landscape
- 11.1 Geographic segmentation
- Chart on Market share by geography 2024-2029 (%)
- Data Table on Market share by geography 2024-2029 (%)
- 11.2 Geographic comparison
- Chart on Geographic comparison
- Data Table on Geographic comparison
- 11.3 North America - Market size and forecast 2024-2029
- Chart on North America - Market size and forecast 2024-2029 ($ billion)
- Data Table on North America - Market size and forecast 2024-2029 ($ billion)
- Chart on North America - Year-over-year growth 2024-2029 (%)
- Data Table on North America - Year-over-year growth 2024-2029 (%)
- Chart on Regional Comparison - North America
- Data Table on Regional Comparison - North America
- 11.3.1 US - Market size and forecast 2024-2029
- Chart on US - Market size and forecast 2024-2029 ($ billion)
- Data Table on US - Market size and forecast 2024-2029 ($ billion)
- Chart on US - Year-over-year growth 2024-2029 (%)
- Data Table on US - Year-over-year growth 2024-2029 (%)
- 11.3.2 Canada - Market size and forecast 2024-2029
- Chart on Canada - Market size and forecast 2024-2029 ($ billion)
- Data Table on Canada - Market size and forecast 2024-2029 ($ billion)
- Chart on Canada - Year-over-year growth 2024-2029 (%)
- Data Table on Canada - Year-over-year growth 2024-2029 (%)
- 11.3.3 Mexico - Market size and forecast 2024-2029
- Chart on Mexico - Market size and forecast 2024-2029 ($ billion)
- Data Table on Mexico - Market size and forecast 2024-2029 ($ billion)
- Chart on Mexico - Year-over-year growth 2024-2029 (%)
- Data Table on Mexico - Year-over-year growth 2024-2029 (%)
- 11.4 Europe - Market size and forecast 2024-2029
- Chart on Europe - Market size and forecast 2024-2029 ($ billion)
- Data Table on Europe - Market size and forecast 2024-2029 ($ billion)
- Chart on Europe - Year-over-year growth 2024-2029 (%)
- Data Table on Europe - Year-over-year growth 2024-2029 (%)
- Chart on Regional Comparison - Europe
- Data Table on Regional Comparison - Europe
- 11.4.1 Germany - Market size and forecast 2024-2029
- Chart on Germany - Market size and forecast 2024-2029 ($ billion)
- Data Table on Germany - Market size and forecast 2024-2029 ($ billion)
- Chart on Germany - Year-over-year growth 2024-2029 (%)
- Data Table on Germany - Year-over-year growth 2024-2029 (%)
- 11.4.2 UK - Market size and forecast 2024-2029
- Chart on UK - Market size and forecast 2024-2029 ($ billion)
- Data Table on UK - Market size and forecast 2024-2029 ($ billion)
- Chart on UK - Year-over-year growth 2024-2029 (%)
- Data Table on UK - Year-over-year growth 2024-2029 (%)
- 11.4.3 France - Market size and forecast 2024-2029
- Chart on France - Market size and forecast 2024-2029 ($ billion)
- Data Table on France - Market size and forecast 2024-2029 ($ billion)
- Chart on France - Year-over-year growth 2024-2029 (%)
- Data Table on France - Year-over-year growth 2024-2029 (%)
- 11.4.4 Italy - Market size and forecast 2024-2029
- Chart on Italy - Market size and forecast 2024-2029 ($ billion)
- Data Table on Italy - Market size and forecast 2024-2029 ($ billion)
- Chart on Italy - Year-over-year growth 2024-2029 (%)
- Data Table on Italy - Year-over-year growth 2024-2029 (%)
- 11.4.5 The Netherlands - Market size and forecast 2024-2029
- Chart on The Netherlands - Market size and forecast 2024-2029 ($ billion)
- Data Table on The Netherlands - Market size and forecast 2024-2029 ($ billion)
- Chart on The Netherlands - Year-over-year growth 2024-2029 (%)
- Data Table on The Netherlands - Year-over-year growth 2024-2029 (%)
- 11.4.6 Spain - Market size and forecast 2024-2029
- Chart on Spain - Market size and forecast 2024-2029 ($ billion)
- Data Table on Spain - Market size and forecast 2024-2029 ($ billion)
- Chart on Spain - Year-over-year growth 2024-2029 (%)
- Data Table on Spain - Year-over-year growth 2024-2029 (%)
- 11.5 APAC - Market size and forecast 2024-2029
- Chart on APAC - Market size and forecast 2024-2029 ($ billion)
- Data Table on APAC - Market size and forecast 2024-2029 ($ billion)
- Chart on APAC - Year-over-year growth 2024-2029 (%)
- Data Table on APAC - Year-over-year growth 2024-2029 (%)
- Chart on Regional Comparison - APAC
- Data Table on Regional Comparison - APAC
- 11.5.1 China - Market size and forecast 2024-2029
- Chart on China - Market size and forecast 2024-2029 ($ billion)
- Data Table on China - Market size and forecast 2024-2029 ($ billion)
- Chart on China - Year-over-year growth 2024-2029 (%)
- Data Table on China - Year-over-year growth 2024-2029 (%)
- 11.5.2 Japan - Market size and forecast 2024-2029
- Chart on Japan - Market size and forecast 2024-2029 ($ billion)
- Data Table on Japan - Market size and forecast 2024-2029 ($ billion)
- Chart on Japan - Year-over-year growth 2024-2029 (%)
- Data Table on Japan - Year-over-year growth 2024-2029 (%)
- 11.5.3 India - Market size and forecast 2024-2029
- Chart on India - Market size and forecast 2024-2029 ($ billion)
- Data Table on India - Market size and forecast 2024-2029 ($ billion)
- Chart on India - Year-over-year growth 2024-2029 (%)
- Data Table on India - Year-over-year growth 2024-2029 (%)
- 11.5.4 South Korea - Market size and forecast 2024-2029
- Chart on South Korea - Market size and forecast 2024-2029 ($ billion)
- Data Table on South Korea - Market size and forecast 2024-2029 ($ billion)
- Chart on South Korea - Year-over-year growth 2024-2029 (%)
- Data Table on South Korea - Year-over-year growth 2024-2029 (%)
- 11.5.5 Australia - Market size and forecast 2024-2029
- Chart on Australia - Market size and forecast 2024-2029 ($ billion)
- Data Table on Australia - Market size and forecast 2024-2029 ($ billion)
- Chart on Australia - Year-over-year growth 2024-2029 (%)
- Data Table on Australia - Year-over-year growth 2024-2029 (%)
- 11.5.6 Indonesia - Market size and forecast 2024-2029
- Chart on Indonesia - Market size and forecast 2024-2029 ($ billion)
- Data Table on Indonesia - Market size and forecast 2024-2029 ($ billion)
- Chart on Indonesia - Year-over-year growth 2024-2029 (%)
- Data Table on Indonesia - Year-over-year growth 2024-2029 (%)
- 11.6 South America - Market size and forecast 2024-2029
- Chart on South America - Market size and forecast 2024-2029 ($ billion)
- Data Table on South America - Market size and forecast 2024-2029 ($ billion)
- Chart on South America - Year-over-year growth 2024-2029 (%)
- Data Table on South America - Year-over-year growth 2024-2029 (%)
- Chart on Regional Comparison - South America
- Data Table on Regional Comparison - South America
- 11.6.1 Brazil - Market size and forecast 2024-2029
- Chart on Brazil - Market size and forecast 2024-2029 ($ billion)
- Data Table on Brazil - Market size and forecast 2024-2029 ($ billion)
- Chart on Brazil - Year-over-year growth 2024-2029 (%)
- Data Table on Brazil - Year-over-year growth 2024-2029 (%)
- 11.6.2 Argentina - Market size and forecast 2024-2029
- Chart on Argentina - Market size and forecast 2024-2029 ($ billion)
- Data Table on Argentina - Market size and forecast 2024-2029 ($ billion)
- Chart on Argentina - Year-over-year growth 2024-2029 (%)
- Data Table on Argentina - Year-over-year growth 2024-2029 (%)
- 11.6.3 Colombia - Market size and forecast 2024-2029
- Chart on Colombia - Market size and forecast 2024-2029 ($ billion)
- Data Table on Colombia - Market size and forecast 2024-2029 ($ billion)
- Chart on Colombia - Year-over-year growth 2024-2029 (%)
- Data Table on Colombia - Year-over-year growth 2024-2029 (%)
- 11.7 Middle East and Africa - Market size and forecast 2024-2029
- Chart on Middle East and Africa - Market size and forecast 2024-2029 ($ billion)
- Data Table on Middle East and Africa - Market size and forecast 2024-2029 ($ billion)
- Chart on Middle East and Africa - Year-over-year growth 2024-2029 (%)
- Data Table on Middle East and Africa - Year-over-year growth 2024-2029 (%)
- Chart on Regional Comparison - Middle East and Africa
- Data Table on Regional Comparison - Middle East and Africa
- 11.7.1 Saudi Arabia - Market size and forecast 2024-2029
- Chart on Saudi Arabia - Market size and forecast 2024-2029 ($ billion)
- Data Table on Saudi Arabia - Market size and forecast 2024-2029 ($ billion)
- Chart on Saudi Arabia - Year-over-year growth 2024-2029 (%)
- Data Table on Saudi Arabia - Year-over-year growth 2024-2029 (%)
- 11.7.2 UAE - Market size and forecast 2024-2029
- Chart on UAE - Market size and forecast 2024-2029 ($ billion)
- Data Table on UAE - Market size and forecast 2024-2029 ($ billion)
- Chart on UAE - Year-over-year growth 2024-2029 (%)
- Data Table on UAE - Year-over-year growth 2024-2029 (%)
- 11.7.3 South Africa - Market size and forecast 2024-2029
- Chart on South Africa - Market size and forecast 2024-2029 ($ billion)
- Data Table on South Africa - Market size and forecast 2024-2029 ($ billion)
- Chart on South Africa - Year-over-year growth 2024-2029 (%)
- Data Table on South Africa - Year-over-year growth 2024-2029 (%)
- 11.7.4 Israel - Market size and forecast 2024-2029
- Chart on Israel - Market size and forecast 2024-2029 ($ billion)
- Data Table on Israel - Market size and forecast 2024-2029 ($ billion)
- Chart on Israel - Year-over-year growth 2024-2029 (%)
- Data Table on Israel - Year-over-year growth 2024-2029 (%)
- 11.7.5 Turkey - Market size and forecast 2024-2029
- Chart on Turkey - Market size and forecast 2024-2029 ($ billion)
- Data Table on Turkey - Market size and forecast 2024-2029 ($ billion)
- Chart on Turkey - Year-over-year growth 2024-2029 (%)
- Data Table on Turkey - Year-over-year growth 2024-2029 (%)
- 11.8 Market opportunity by geography
- Market opportunity by geography ($ billion)
- Data Tables on Market opportunity by geography ($ billion)
12 Drivers, Challenges, and Opportunity
- 12 Drivers, Challenges, and Opportunity
- 12.1 Market drivers
- Intensifying industrial demand for next-generation materials
- Global sustainability mandate and quest for green materials
- Technological convergence of advanced computation, data proliferation, and algorithmic sophistication
- 12.2 Market challenges
- Data scarcity and accessibility
- Chasm of synthesizability and experimental validation
- High implementation costs and scarcity of interdisciplinary talent
- 12.3 Impact of drivers and challenges
- Impact of drivers and challenges in 2024 and 2029
- 12.4 Market opportunities
- Integration with autonomous laboratories to create closed-loop discovery platforms
- Development of domain-specific foundation models for materials science
- Shift towards multi-objective and manufacturability-aware design
13 Competitive Landscape
- 13 Competitive Landscape
- 13.1 Overview
- 13.2 Competitive Landscape
- Overview on criticality of inputs and factors of differentiation
- 13.3 Landscape disruption
- Overview on factors of disruption
- 13.4 Industry risks
- Impact of key risks on business
14 Competitive Analysis
- 14 Competitive Analysis
- 14.1 Companies profiled
- 14.2 Company ranking index
- 14.3 Market positioning of companies
- Matrix on companies position and classification
- 14.4 Arzeda Corp.
- Arzeda Corp. - Overview
- Arzeda Corp. - Product / Service
- Arzeda Corp. - Key offerings
- SWOT
- 14.5 BASF SE
- BASF SE - Overview
- BASF SE - Business segments
- BASF SE - Key news
- BASF SE - Key offerings
- BASF SE - Segment focus
- SWOT
- 14.6 Citrine Informatics Inc.
- Citrine Informatics Inc. - Overview
- Citrine Informatics Inc. - Product / Service
- Citrine Informatics Inc. - Key offerings
- SWOT
- 14.7 Dassault Systemes SE
- Dassault Systemes SE - Overview
- Dassault Systemes SE - Business segments
- Dassault Systemes SE - Key news
- Dassault Systemes SE - Key offerings
- Dassault Systemes SE - Segment focus
- SWOT
- 14.8 DeepMaterials LLC
- DeepMaterials LLC - Overview
- DeepMaterials LLC - Product / Service
- DeepMaterials LLC - Key offerings
- SWOT
- 14.9 Google LLC
- Google LLC - Overview
- Google LLC - Product / Service
- Google LLC - Key offerings
- SWOT
- 14.10 International Business Machines Corp.
- International Business Machines Corp. - Overview
- International Business Machines Corp. - Business segments
- International Business Machines Corp. - Key news
- International Business Machines Corp. - Key offerings
- International Business Machines Corp. - Segment focus
- SWOT
- 14.11 Mat3ra Inc.
- Mat3ra Inc. - Overview
- Mat3ra Inc. - Product / Service
- Mat3ra Inc. - Key offerings
- SWOT
- 14.12 MaterialsZone Ltd.
- MaterialsZone Ltd. - Overview
- MaterialsZone Ltd. - Product / Service
- MaterialsZone Ltd. - Key offerings
- SWOT
- 14.13 Microsoft Corp.
- Microsoft Corp. - Overview
- Microsoft Corp. - Business segments
- Microsoft Corp. - Key news
- Microsoft Corp. - Key offerings
- Microsoft Corp. - Segment focus
- SWOT
- 14.14 NVIDIA Corp.
- NVIDIA Corp. - Overview
- NVIDIA Corp. - Business segments
- NVIDIA Corp. - Key news
- NVIDIA Corp. - Key offerings
- NVIDIA Corp. - Segment focus
- SWOT
- 14.15 Optibrium
- Optibrium - Overview
- Optibrium - Product / Service
- Optibrium - Key offerings
- SWOT
- 14.16 Orbital Materials Inc.
- Orbital Materials Inc. - Overview
- Orbital Materials Inc. - Product / Service
- Orbital Materials Inc. - Key offerings
- SWOT
- 14.17 sandboxAQ
- sandboxAQ - Overview
- sandboxAQ - Product / Service
- sandboxAQ - Key offerings
- SWOT
- 14.18 Schrodinger Inc.
- Schrodinger Inc. - Overview
- Schrodinger Inc. - Business segments
- Schrodinger Inc. - Key offerings
- Schrodinger Inc. - Segment focus
- SWOT
15 Appendix
- 15 Appendix
- 15.1 Scope of the report
- Market definition
- Objectives
- Notes and caveats
- 15.2 Inclusions and exclusions checklist
- Inclusions checklist
- Exclusions checklist
- 15.3 Currency conversion rates for US$
- Currency conversion rates for US$
- 15.4 Research methodology
- 15.5 Data procurement
- 15.6 Data validation
- 15.7 Validation techniques employed for market sizing
- Validation techniques employed for market sizing
- 15.8 Data synthesis
- 15.9 360 degree market analysis
- 360 degree market analysis
- 15.10 List of abbreviations
INFORMATION SOURCES
DATA ANALYSIS
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