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Materials science

🔬 The Limits of AI in Science - Why We Need Self-Driving Labs — Joseph Krause, Radical AI

🔬 The Limits of AI in Science - Why We Need Self-Driving Labs — Joseph Krause, Radical AI

Joseph Krause, CEO of Radical AI, details how his company uses Self-Driving Labs (SDLs) and AI scientists to overcome the experimental bottleneck in materials science. By automating the full loop of hypothesis generation, synthesis, characterization, and testing, Radical AI is accelerating the discovery of novel alloys for aerospace, defense, and semiconductor applications, achieving 10x the pace of traditional methods. Krause explains why materials science is uniquely challenging for AI, how human intuition trains the AI, and why experimental data, not models, forms the core competitive advantage in this rapidly evolving, geopolitically significant field.

Can we AI our way to a more sustainable world?

Can we AI our way to a more sustainable world?

Microsoft experts Doug Burger, Amy Luers, and Ishai Menache discuss the dual role of AI in sustainability. They analyze the environmental footprint of datacenters and explore how AI-driven optimization and materials discovery can be pivotal in decarbonizing global systems like energy, industry, and food production.

🔬There Is No AlphaFold for Materials — AI for Materials Discovery with Heather Kulik

🔬There Is No AlphaFold for Materials — AI for Materials Discovery with Heather Kulik

Professor Heather Kulik shares her hard-won perspective on applying AI to materials science, from discovering novel polymers with surprising quantum properties to the practical limitations of LLMs and the critical need for integrating deep domain expertise with data-driven methods.