Everything on Model Validation

ML potentials often fail catastrophically compared to physics-based models due to a lack of experimental ground truth and rigorous validation protocols.

Impact: Establishing community challenges based on experimental data is critical to ensure ML models are robust and trustworthy before replacing traditional physics-based simulations.

— from AI in Materials Science: Discovery, Data Gaps, and Active Learning · Latent Space: The AI Engineer Podcast· Mar 24, 2026