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Can AI revolutionize materials discovery?

Catalyst with Shayle Kann

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Challenges in AI-Driven Materials Discovery

This chapter explores the limitations of data available for training AI models in materials science, contrasting it with the expansive datasets in generative AI. It discusses the crucial need for quality experimental data and the potential of synthetic data to enhance model accuracy, as well as the ongoing relevance of traditional laboratory methods. The conversation also highlights the rise of AI startups in materials discovery, particularly focusing on metal-organic frameworks and their applications in addressing climate challenges.

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