Artificial Intelligence (AI) In Materials Discovery Market Global Report 2026

The artificial intelligence (AI) in materials discovery market size has grown exponentially in recent years. It will grow from $0.74 billion in 2025 to $0.97 billion in 2026 at a compound annual growth rate (CAGR) of 30.0%. The growth in the historic period can be attributed to growing adoption of computational modeling, increasing availability of digital materials datasets, rising investment in artificial intelligence-based research, expanding application of machine learning in laboratories, and increasing industry-academia collaborations.

The artificial intelligence (AI) in materials discovery market size is expected to see exponential growth in the next few years. It will grow to $2.74 billion in 2030 at a compound annual growth rate (CAGR) of 29.7%. The growth during the forecast period can be attributed to the increasing need for rapid discovery of advanced materials, growing demand for high-performance materials for energy storage, rising adoption of generative artificial intelligence models, expanding deployment of cloud-based simulation platforms, and increasing pressure to reduce research and development cycles. Major trends during the forecast period include advancements in multimodal artificial intelligence models, innovations in high-throughput computational screening, developments in autonomous laboratory systems, ongoing research and development of materials-focused foundation models, and progress in quantum-enhanced materials simulations.

The increasing adoption of AI-driven computational modeling and simulations is anticipated to drive the expansion of the artificial intelligence (AI) in materials discovery market going forward. AI-driven computational modeling and simulations utilize machine learning and computational algorithms to forecast material properties, develop new compounds, and optimize structures, thereby reducing dependence on conventional trial-and-error experimentation. The adoption of AI-driven modeling is rising due to increasing pressure on research institutions and industries to accelerate innovation while lowering development costs. AI in materials discovery facilitates this trend by supporting high-throughput virtual screening, precise property prediction, and rapid discovery of novel materials. For instance, in September 2023, according to Ames National Laboratory, a US-based government research lab, AI-based modeling achieved a 100× speed-up compared to first-principles calculations and identified 16 new P-rich compounds. Therefore, the increasing adoption of AI-driven computational modeling and simulations is driving the growth of the artificial intelligence (AI) in materials discovery market.

Key companies operating in the artificial intelligence (AI) in materials discovery market are concentrating on advancing large-scale crystal structure prediction, including deep-learning-driven exploration of new crystalline compounds, to broaden the accessible chemical space, accelerate material identification, and improve computational screening processes. Large-scale crystal structure prediction refers to the application of graph-based neural networks and algorithmic exploration systems that generate, assess, and rank millions of hypothetical crystal structures based on established stability and performance criteria. For example, in November 2023, Google DeepMind, a UK-based artificial intelligence company, introduced GNoME, an AI-powered materials discovery system that predicted 2.2 million new crystal structures and identified approximately 380,000 as potentially stable. The system applies graph neural networks to model atomic interactions, incorporates active learning to continuously improve predictions, and uses high-accuracy density functional theory (DFT) checks to confirm structural stability. This launch marks a significant advancement in computational materials discovery by expanding the collection of known stable crystals, accelerating early-stage screening, and allowing researchers to identify candidates with promising functional properties across a wide range of material classes.

In October 2024, Comstock Inc., a US-based provider of renewable energy technologies and advanced materials solutions, acquired Quantum Generative Materials LLC (GenMat) for an undisclosed amount. With this acquisition, Comstock aims to accelerate its AI-led materials innovation by integrating GenMat’s physics-based generative modeling platform, automated synthesis workflows, and specialized materials research capabilities to expand its portfolio of high-performance, energy-efficient, and sustainability-focused materials, thereby strengthening long-term competitiveness in next-generation materials development. Quantum Generative Materials LLC is a US-based provider of AI-driven materials discovery solutions that combine computational modeling, advanced algorithms, and autonomous experimentation to design, predict, and optimize novel materials for applications across the energy, sustainability, and advanced manufacturing sectors.

Major companies operating in the artificial intelligence (AI) in materials discovery market are Google LLC, Microsoft Corporation, BASF SE, International Business Machine Corp, Dassault Systèmes, Nautilus Materials Inc., Schrödinger Inc., Enthought Inc., Citrine Informatics Inc., Iktos SA, Quantum Motion, Aionics Inc., Exabyte.io, Materials Zone Ltd., Polymerize AG, Atinary Technologies GmbH, Phaseshift Technologies, Polaron Analytics, Kebotix Inc.

North America was the largest region in the artificial intelligence (AI) in materials discovery market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the artificial intelligence (AI) in materials discovery market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

The countries covered in the artificial intelligence (AI) in materials discovery market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

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