Generative Artificial Intelligence (AI) in Material Science Market Report 2025

This Generative Artificial Intelligence (AI) in Material Science market report delivers an in-depth analysis of the market’s key characteristics, including size, growth potential, and segmentation. It provides a detailed breakdown of the market across major regions and leading countries, highlighting historical data and future growth projections. The report also examines the competitive landscape, market share insights, emerging trends, and strategic developments shaping the market.

The generative artificial intelligence (AI) in material science market size has grown exponentially in recent years. It will grow from $1.26 billion in 2024 to $1.68 billion in 2025 at a compound annual growth rate (CAGR) of 33.8%. The growth in the historic period can be attributed to the discovery of new materials, government funding for research and development, rising performance of computing, increasing data availability, and demand for lightweight materials.

The generative artificial intelligence (AI) in material science market size is expected to see exponential growth in the next few years. It will grow to $5.35 billion in 2029 at a compound annual growth rate (CAGR) of 33.6%. The growth in the forecast period can be attributed to the demand for sustainable materials, personalized material design, increased private investment, growth in autonomous systems, and rising adoption of biotechnology applications. Major trends in the forecast period include AI-driven predictive analytics, the development of self-healing materials, the adoption of generative AI in nanomaterial design, decentralized research networks, and the integration of AI with additive manufacturing.

The anticipated growth of generative artificial intelligence (AI) in the material science market is expected to be fueled by increasing investment in AI technologies. This rise in investment is driven by several factors, including heightened demand for automation, improved data analytics, innovative applications, and support from both government and private sectors. Generative AI in material science accelerates discovery and innovation by optimizing material properties and processes, thus attracting significant investment. For example, a May 2022 report by International Business Machines Corporation (IBM) revealed a notable increase in global AI adoption, reaching 35%, up four percentage points from the previous year. The report also projected a 13% increase in the number of firms using AI in 2022 compared to 2021. Specifically, 35% of organizations reported adopting AI, 42% were considering adoption, and 66% were either implementing or planning to use AI to meet their sustainability goals. This growing investment in AI technologies is driving the expansion of generative AI in material science.

Leading companies in the generative AI in material science market are focusing on developing innovative solutions, such as advanced generative AI models for drug discovery, to enhance the speed and efficiency of drug discovery and life sciences research. For instance, in March 2023, Nvidia Corporation, a US-based computer hardware company, introduced the BioNeMo Cloud Service, which includes pre-trained and customizable generative AI models for drug discovery, such as AlphaFold2 and MoFlow. These models accelerate molecular design and optimization, significantly reducing the time and cost associated with research and development, and facilitating the faster identification and creation of new therapeutic candidates and materials.

In January 2024, SandboxAQ, a US-based enterprise SaaS company, acquired Good Chemistry for $75 million. This acquisition aims to enhance SandboxAQ's AI simulation capabilities in drug discovery and materials design by integrating Good Chemistry’s quantum and computational chemistry platforms. It will expand SandboxAQ’s technology portfolio and accelerate the development of new materials and pharmaceuticals through Good Chemistry’s expertise and industry partnerships. Good Chemistry, a Canadian computer application company, utilizes cloud computing technology to predict chemical properties.

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.

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. The countries covered in the generative artificial intelligence in material science market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Generative artificial intelligence in material science leverages sophisticated algorithms to create new materials by predicting their properties and behaviors through extensive datasets and simulations. This technology speeds up the discovery of new materials, enhances existing ones, and facilitates the development of innovative materials for a range of industrial uses.

The primary types of generative AI in material science include materials discovery and design, predictive modeling and simulation, and process optimization. Materials discovery and design use computational techniques and algorithms to identify and enhance new materials for specific applications. These AI systems can be implemented through cloud-based, on-premises, or hybrid models and are applicable in fields such as pharmaceuticals, chemicals, electronics, semiconductors, energy storage and conversion, automotive, aerospace, construction, infrastructure, and consumer goods.

The generative artificial intelligence in material science market research report is one of a series of new reports that provides generative artificial intelligence in material science market statistics, including generative artificial intelligence in material science industry global market size, regional shares, competitors with a generative artificial intelligence in material science market share, detailed generative artificial intelligence in material science market segments, market trends and opportunities, and any further data you may need to thrive in the generative artificial intelligence in material science industry. This generative artificial intelligence in material science market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The generative artificial intelligence in material science market includes revenues earned by entities by providing services such as material property analysis consulting, integration services for AI tools in workflows, and technical support and training. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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