Use Cases of AI in R&D: Materials Discovery and Product Development (Materials Informatics)

Explore how artificial intelligence is revolutionizing materials discovery and chemical R&D, ushering in a new era of concurrent materials design. Enthought's head of Materials Informatics (MI) and polymer scientist Michael Heiber, PhD dives into the accelerating pace of innovation in materials science and engineering, driven by advanced AI technologies and MI. This presentation was originally given at the 2025 R&D Innovation Summit in Tokyo to highlight the power of AI for industry materials discovery and product development.

Learn about:
The shift from traditional R&D to an AI-driven learning cycle.
How concurrent engineering is being transformed by the ability to design materials and products simultaneously, leading to globally optimized solutions.
How machine learning and AI are enabling faster material development, reducing time-to-market, and creating significant business opportunities for materials suppliers and product designers.
Key AI technology trends including generative AI for materials, AI-driven optimization for complex properties, and specialized large language models (LLMs) for predictive chemistry.

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