Materials innovation has a scale-up problem, not discovery
Summary
The article argues that materials innovation is held back by scale-up challenges rather than discovery, and that AI-enabled workflows plus robust data infrastructure can unlock production-ready materials. It highlights Atomscale's approach—a hierarchy of physics-informed models that turns raw process data into real-time insights, enabling guided experimentation and cross-run knowledge accumulation. The piece also discusses physical and informational hurdles, the importance of shared data, and the potential to compress scale-up timelines.