dailyai.report

Data scarcity in materials science prevents AI from succeeding through raw data alone. Nature argues that coupling machine learning with deep chemical expertise is the only way to bridge the gap between virtual predictions and physical synthesis. This shift forces researchers to prioritize domain-specific knowledge over generic model scaling to achieve viable laboratory results.