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Primary Catalyst Driving Generative Artificial Intelligence (AI) In Material Science Market Evolution in 2025: Artificial Intelligence Technologies Propel Growth Of The Generative Artificial Intelligence In The Material Science Market Driver
Generative Artificial Intelligence AI In Material Science
The burgeoning investment in artificial intelligence technologies is anticipated to fuel the expansion of the generative artificial intelligence in material science market. The incline in artificial intelligence investments can be attributed to a variety of factors that include heightened demand for automation, improved data analytics, novel applications, along with support from both government and private sectors. Generative AI in material science expedites discoveries and innovation by fine-tuning material properties and processes, thus attracting substantial investment into artificial intelligence technologies. For instance, in May 2022, a study by IBM, a US-based technology corporation, indicated a notable increase in global AI adoption to 35%, a four-point rise from the prior year. The study further predicted that in 2022, 13% more businesses were likely to deploy AI than in 2021. In particular, while 35% of organizations confirmed AI adoption, 42% were contemplating adoption, and 66% were either presently implementing or planning to utilize AI to meet their sustainability objectives. Consequently, the escalating investments in artificial intelligence technologies is influencing the growth of the generative artificial intelligence in material science market.
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What Is the projected market size and growth rate for the generative artificial intelligence (ai) in material science market?
The market for generative artificial intelligence (AI) within the realm of material science has notably expanded in past years. This expansion is projected to increase from $1.26 billion in 2024 to $1.68 billion in 2025, which indicates a compound annual growth rate (CAGR) of 33.8%. Factors contributing to this growth in the historic period encompass the discovery of innovative materials, governmental support in research and development, elevated computing performance, greater data accessibility, and a heightened demand for materials with a lighter weight.
The market size of generative artificial intelligence (AI) in the field of material science is projected to experience a massive surge in the upcoming years. The estimated growth will see it reach a value of $5.35 billion by 2029, with a compound annual growth rate (CAGR) of 33.6%. The said expansion in the forecasted period is linked to factors like the need for environment-friendly materials, customization in material design, surge in private investments, evolution of autonomous systems, and widespread usage of biotech applications. The leading trends during these years are expected to be predictive analytics powered by AI, creation of self-repairing materials, utilization of generative AI for designing nanomaterials, spread out research networks, and the merger of AI and additive manufacturing techniques.
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What new trends are reshaping the generative artificial intelligence (ai) in material science market and its opportunities?
Leading corporations within the generative AI sector for material science are concentrating on the creation of groundbreaking solutions, such as heightened generative AI models for drug discovery. These are geared towards accelerating the pace of pharmaceutical research and studies in life sciences through the use of superior generative AI technologies. The high-speed generative AI models for drug discovery are advanced computer systems. They utilize machine learning methods to effectively design and anticipate prospective new drugs speedily. To exemplify, Nvidia Corporation, a computer hardware manufacturer based in the US, introduced BioNeMo Cloud Service in March 2023. This service offers pre-established and adjustable generative AI models for drug discovery, including options like AlphaFold2 and MoFlow. These expedite the design and refinement of molecules. Their crucial role includes substantially slashing the duration and expenses associated with research and development in drug discovery and life sciences. Consequently, this results in quicker detection and generation of new therapeutic candidates and substances.
What major market segments define the scope and growth of the generative artificial intelligence (ai) in material science market?
The generative artificial intelligence (AI) in material science market covered in this report is segmented -
1) By Type: Materials Discovery And Design; Predictive Modeling And Simulation; Process Optimization
2) By Deployment: Cloud-Based; On-Premises; Hybrid
3) By Application: Pharmaceuticals And Chemicals; Electronics And Semiconductors; Energy Storage And Conversion; Automotive And Aerospace; Construction And Infrastructure; Consumer Goods; Other Applications
Subsegments:
1) By Materials Discovery and Design: AI-Driven Materials Screening; AI-Based Computational Chemistry; Quantum Materials Design; Material Property Prediction
2) By Predictive Modeling and Simulation: AI-Based Simulation For Material Behavior; Predictive Analytics For Material Performance; Failure Prediction And Reliability Analysis; Thermal And Mechanical Property Simulation
3) By Process Optimization: AI For Manufacturing Process Optimization; Energy Efficiency In Material Processing; AI-Driven Quality Control In Material Production; Supply Chain Optimization For Materials
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Which region dominates the generative artificial intelligence (ai) in material science market?
North America was the largest region in the generative artificial intelligence in material science market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the generative artificial intelligence in material science market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
Which key market leaders are driving the generative artificial intelligence (ai) in material science industry growth?
Major companies operating in the generative artificial intelligence in material science market are Microsoft Corporation, Siemens AG, International Business Machines Corporation (IBM), NVIDIA Corporation, Hexagon AB, Illumina Inc., ANSYS Inc., DeepMind Technologies Limited, Altair Engineering Inc., OpenAI, Schrödinger Inc., XtalPi, Alchemy Insights Inc., Citrine Informatics Inc., QuesTek Innovations LLC, Materials Zone, Kebotix Inc., Nanotronics Imaging Inc., AION Labs, Exabyte.io
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What Is Covered In The Generative Artificial Intelligence (AI) In Material Science Global Market Report?
•Market Size Forecast: Examine the generative artificial intelligence (ai) in material science market size across key regions, countries, product categories, and applications.
•Segmentation Insights: Identify and classify subsegments within the generative artificial intelligence (ai) in material science market for a structured understanding.
•Key Players Overview: Analyze major players in the generative artificial intelligence (ai) in material science market, including their market value, share, and competitive positioning.
•Growth Trends Exploration: Assess individual growth patterns and future opportunities in the generative artificial intelligence (ai) in material science market.
•Segment Contributions: Evaluate how different segments drive overall growth in the generative artificial intelligence (ai) in material science market.
•Growth Factors: Highlight key drivers and opportunities influencing the expansion of the generative artificial intelligence (ai) in material science market.
•Industry Challenges: Identify potential risks and obstacles affecting the generative artificial intelligence (ai) in material science market.
•Competitive Landscape: Review strategic developments in the generative artificial intelligence (ai) in material science market, including expansions, agreements, and new product launches.
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