Generative AI in Chemical

Generative AI in Chemical

Generative AI in the chemical industry refers to the use of AI models that can generate new chemical compounds or predict their properties. These models are trained on large datasets of known chemical compounds and their properties, allowing them to make predictions about new compounds and suggest potential applications for them. The growing demand of optimize chemical processes and reduction of waste is fueling the market.

The Generative AI in Chemical Market was valued at 1.2 billion in 2022 and is expected to grow at a steady rate of around 28.3% in the forecasted period (2023-2030) owing to the advancements in technology. The chemical industry is constantly looking for new and innovative compounds to develop new products and improve existing ones. Generative AI can help accelerate this process by identifying potential candidates for further study and suggesting new applications. Moreover, generative AI models can be trained on large datasets of known chemical compounds and their properties, allowing them to make predictions about new compounds and suggest potential applications for them. Therefore, the need for predictive modeling is accelerating the growth of the market.

• Based on technology, the market is segmented into machine learning, reinforcement learning, deep learning, molecular docking, and others. Among these, the deep learning segment is expected to capture a significant market share over the forecast period due to its ability to handle complex and high-dimensional data, such as chemical compounds and their properties. Deep learning algorithms, such as neural networks, learn to represent and process complex data in a hierarchical manner, which allows them to make accurate predictions and identify patterns that may not be apparent to other types of AI models.
• Based on applications, the market is categorized into discovery of new materials, production optimization, pricing optimization, and others. Among these, the discovery of a new materials segment is expected to dominate the market over the forecast period due to the growing demand for new and innovative materials with unique properties for various applications. The ability of generative AI models to accelerate the discovery and optimization of materials is expected to drive significant growth in this segment over the forecast period. For instance, Researchers at the University of California, San Diego, used generative AI models to design new materials with improved performance and energy density compared to traditional materials in a study published in the journal Nature in 2021.
• For a better understanding of the market adoption of generative AI in chemical industry, the market is analyzed based on its worldwide presence in countries such as North America (U.S., Canada, and the Rest of North America), Europe (Germany, U.K., France, Spain, Italy, Rest of Europe), Asia-Pacific (China, Japan, India, Rest of Asia-Pacific), Rest of World. North America is anticipated to hold a large share of the market. North America is home to a large number of chemical companies, including some of the world's largest and most innovative ones. These companies are investing heavily in research and development, and are actively exploring the use of generative AI in their operations. Moreover, North America has a well-established ecosystem for research and development, with a large number of universities and research institutions that are at the forefront of innovation. This has created an environment that is conducive to the development and adoption of new technologies, including generative AI. For example, in 2021, researchers at the Massachusetts Institute of Technology (MIT) have started using generative AI models to design new catalysts for chemical reactions, which could improve efficiency and reduce costs.
• Some of the major players operating in the market include IBM Corporation; Google; Mitsui Chemicals; Accenture; Azelis Group NV; Tricon Energy Inc.; Biesterfeld AG; Omya AG; HELM AG; Sinochem Corporation